From abd31d16a5619b4c7e3351a855ebfc0f6d7a46bc Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 21 Oct 2023 18:50:28 +0200 Subject: [PATCH 01/43] initial commit --- pyfixest/FixestMulti.py | 37 +- .../__pycache__/FixestMulti.cpython-310.pyc | Bin 20870 -> 20965 bytes .../__pycache__/FormulaParser.cpython-310.pyc | Bin 11325 -> 11325 bytes pyfixest/__pycache__/__init__.cpython-310.pyc | Bin 147 -> 147 bytes pyfixest/__pycache__/demean.cpython-310.pyc | Bin 6214 -> 6214 bytes pyfixest/__pycache__/did.cpython-310.pyc | Bin 0 -> 3743 bytes .../__pycache__/estimation.cpython-310.pyc | Bin 12050 -> 12050 bytes .../__pycache__/exceptions.cpython-310.pyc | Bin 1957 -> 2081 bytes pyfixest/__pycache__/feiv.cpython-310.pyc | Bin 2457 -> 2457 bytes pyfixest/__pycache__/feols.cpython-310.pyc | Bin 31653 -> 31811 bytes pyfixest/__pycache__/fepois.cpython-310.pyc | Bin 9870 -> 9870 bytes .../model_matrix_fixest.cpython-310.pyc | Bin 10172 -> 10239 bytes .../__pycache__/summarize.cpython-310.pyc | Bin 2502 -> 2502 bytes pyfixest/__pycache__/utils.cpython-310.pyc | Bin 6515 -> 6515 bytes .../__pycache__/visualize.cpython-310.pyc | Bin 5888 -> 5888 bytes pyfixest/data/df_het.csv | 46501 ++++++++++++++++ pyfixest/did.py | 169 + pyfixest/exceptions.py | 3 + pyfixest/feols.py | 49 +- pyfixest/model_matrix_fixest.py | 16 +- 20 files changed, 46742 insertions(+), 33 deletions(-) create mode 100644 pyfixest/__pycache__/did.cpython-310.pyc create mode 100644 pyfixest/data/df_het.csv create mode 100644 pyfixest/did.py diff --git a/pyfixest/FixestMulti.py b/pyfixest/FixestMulti.py index cf9188ff..a69c553d 100644 --- a/pyfixest/FixestMulti.py +++ b/pyfixest/FixestMulti.py @@ -166,10 +166,15 @@ def _estimate_all_models( na_index, na_index_str, _icovars, + X_is_empty ) = model_matrix_fixest(fml=fml, data=_data) weights = np.ones((Y.shape[0], 1)) + self._X_is_empty = False + if X_is_empty: + self._X_is_empty = True + coefnames = X.columns.tolist() if _method == "feols": @@ -229,10 +234,14 @@ def _estimate_all_models( X=Xd, weights=weights, coefnames=coefnames, - collin_tol=collin_tol, + collin_tol=collin_tol ) - FIT.get_fit() + # special case: sometimes it is useful to fit models as "Y ~ 0 | f1 + f2" to demean Y and to use the predict() method + if FIT._X_is_empty: + FIT._u_hat = Y.to_numpy() - Yd + else: + FIT.get_fit() elif _method == "fepois": # check for separation and drop separated variables @@ -284,19 +293,21 @@ def _estimate_all_models( FIT._fixef = None # FEOLS.split_log = x - # inference - vcov_type = _get_vcov_type(vcov, fval) - FIT.vcov(vcov=vcov_type) - FIT.get_inference() + if not FIT._X_is_empty: - # other regression stats - if _method == "feols": - FIT.get_performance() + # inference + vcov_type = _get_vcov_type(vcov, fval) + FIT.vcov(vcov=vcov_type) + FIT.get_inference() - if _icovars is not None: - FIT._icovars = _icovars - else: - FIT._icovars = None + # other regression stats + if _method == "feols": + FIT.get_performance() + + if _icovars is not None: + FIT._icovars = _icovars + else: + FIT._icovars = None # store fitted model self.all_fitted_models[fml] = FIT diff --git a/pyfixest/__pycache__/FixestMulti.cpython-310.pyc b/pyfixest/__pycache__/FixestMulti.cpython-310.pyc index a49b0348554bfa131b3611e2f0dc3f67344eac12..d74417265d9b432f540ceffc95dbbf965fa764de 100644 GIT binary patch delta 2083 zcmai#ZERCj7{~8(dw2W7x)rv*tlidiZFkpg4mNQhkr_6e49Jkc2ML2RxLaHY-FAB? zZ|7!lLn6v>;YgSY+B!jAd;zX#)Tl5KV)TO$K9~kkLx`VA!03#@^W5%DG_lS7^*sOc zoag+{)6;bCDBK=}oNl|_#Dcb(0-Vb7^xVGes5*^}5sf4A4d0a}?8a1za- ziXm@ept+UT0$M3CD@WOE z0rCE8HrTI);(}~qi9szy%!y;9R13*AdDb3|1V||fkzj|glpBOr0ZVBiEhw9{FbONy znN1h*C*3KI87w0KBQ8t9qns8Xp&3z#l+K9I&M&HGqI$odln|4gJtS&U+^QBBaz?Wc zWoZ(TVsn&x#D&|CfQKVe)EnPOq@*v!0T*A#PM_1o4}-Ew&NWQ49(3@AVIDh6+?hUV z<-{X9`haDIi3h7`9^#1=_RGAn9K|A1KGANET0GUv5@I6)$zmpF4_VPrW^3YOR{XMq zv4oinu)-}w61bQjqXit#Mg3|ijt2pA;({8K=M2IC5GRX6#1ad$r@t&I5nl>&xdJ>J 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2,5.80163710979655,2010,1993,0.12632146154317,FALSE,-17,-6,-0.581392605533284,0,0,5.34656596580644 +322,34,Group 2,5.80163710979655,2010,1994,-0.106920550397923,FALSE,-16,-6,-1.36427358081451,0,0,4.33044297858412 +322,34,Group 2,5.80163710979655,2010,1995,-0.270420204800212,FALSE,-15,-6,-1.53547362304744,0,0,3.9957432819489 +322,34,Group 2,5.80163710979655,2010,1996,0.00320779333995708,FALSE,-14,-6,-1.39657799477756,0,0,4.40826690835895 +322,34,Group 2,5.80163710979655,2010,1997,0.224162099936224,FALSE,-13,-6,1.27468670180596,0,0,7.30048591153873 +322,34,Group 2,5.80163710979655,2010,1998,0.0760938602866395,FALSE,-12,-6,0.0993705978257376,0,0,5.97710156790893 +322,34,Group 2,5.80163710979655,2010,1999,0.136987237048632,FALSE,-11,-6,0.486422202349875,0,0,6.42504654919506 +322,34,Group 2,5.80163710979655,2010,2000,0.18154398886121,FALSE,-10,-6,0.370776716940018,0,0,6.35395781559778 +322,34,Group 2,5.80163710979655,2010,2001,0.356223316177845,FALSE,-9,-6,1.2579576136363,0,0,7.41581803961069 +322,34,Group 2,5.80163710979655,2010,2002,-0.531970898974565,FALSE,-8,-6,0.631709073490607,0,0,5.9013752843126 +322,34,Group 2,5.80163710979655,2010,2003,-0.130892305477,FALSE,-7,-6,1.81876285842979,0,0,7.48950766274934 +322,34,Group 2,5.80163710979655,2010,2004,-0.0835454927715576,FALSE,-6,-6,-0.0912834723833992,0,0,5.6268081446416 +322,34,Group 2,5.80163710979655,2010,2005,-0.168886421474279,FALSE,-5,-5,-2.75867900335313,0,0,2.87407168496914 +322,34,Group 2,5.80163710979655,2010,2006,-0.198553870392569,FALSE,-4,-4,-2.04923717480679,0,0,3.5538460645972 +322,34,Group 2,5.80163710979655,2010,2007,0.0736411662970163,FALSE,-3,-3,0.0718827658161269,0,0,5.9471610419097 +322,34,Group 2,5.80163710979655,2010,2008,0.0419510640347825,FALSE,-2,-2,-1.36414817025752,0,0,4.47944000357382 +322,34,Group 2,5.80163710979655,2010,2009,0.117226819959701,FALSE,-1,-1,0.111818737247967,0,0,6.03068266700422 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2,5.80163710979655,2010,2018,-0.111974912848881,TRUE,8,6,-0.197753379820356,1,1.2,7.69190881712732 +322,34,Group 2,5.80163710979655,2010,2019,-0.00894864853974133,TRUE,9,6,0.521771546968891,1,1.35,8.6644600082257 +322,34,Group 2,5.80163710979655,2010,2020,-0.374950507229263,TRUE,10,6,0.642784302330568,1,1.5,8.56947090489786 +323,24,Group 2,3.92577538341629,2010,1990,0.0661588861645665,FALSE,-20,-6,-0.635538565999064,0,0,3.35639570358179 +323,24,Group 2,3.92577538341629,2010,1991,-0.0309803200634303,FALSE,-19,-6,0.0959818710471904,0,0,3.99077693440005 +323,24,Group 2,3.92577538341629,2010,1992,-0.119607424539513,FALSE,-18,-6,0.13119592938785,0,0,3.93736388826462 +323,24,Group 2,3.92577538341629,2010,1993,0.12632146154317,FALSE,-17,-6,0.66471127028114,0,0,4.7168081152406 +323,24,Group 2,3.92577538341629,2010,1994,-0.106920550397923,FALSE,-16,-6,1.47120771249988,0,0,5.29006254551825 +323,24,Group 2,3.92577538341629,2010,1995,-0.270420204800212,FALSE,-15,-6,1.99786954692088,0,0,5.65322472553696 +323,24,Group 2,3.92577538341629,2010,1996,0.00320779333995708,FALSE,-14,-6,-0.12157530120204,0,0,3.8074078755542 +323,24,Group 2,3.92577538341629,2010,1997,0.224162099936224,FALSE,-13,-6,1.17755307359917,0,0,5.32749055695168 +323,24,Group 2,3.92577538341629,2010,1998,0.0760938602866395,FALSE,-12,-6,-1.19806163478062,0,0,2.80380760892231 +323,24,Group 2,3.92577538341629,2010,1999,0.136987237048632,FALSE,-11,-6,-0.606520451614205,0,0,3.45624216885071 +323,24,Group 2,3.92577538341629,2010,2000,0.18154398886121,FALSE,-10,-6,-0.885631043496008,0,0,3.22168832878149 +323,24,Group 2,3.92577538341629,2010,2001,0.356223316177845,FALSE,-9,-6,0.540714280004545,0,0,4.82271297959868 +323,24,Group 2,3.92577538341629,2010,2002,-0.531970898974565,FALSE,-8,-6,-1.52441541919271,0,0,1.86938906524901 +323,24,Group 2,3.92577538341629,2010,2003,-0.130892305477,FALSE,-7,-6,-1.35200251495544,0,0,2.44288056298384 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2,3.92577538341629,2010,2012,-0.383607038892157,TRUE,2,2,-0.999431311612207,1,0.3,3.84273703291192 +323,24,Group 2,3.92577538341629,2010,2013,0.243287094162147,TRUE,3,3,0.398620686657605,1,0.45,6.01768316423604 +323,24,Group 2,3.92577538341629,2010,2014,0.118752505489616,TRUE,4,4,1.72668985568662,1,0.6,7.37121774459253 +323,24,Group 2,3.92577538341629,2010,2015,0.0593091311848276,TRUE,5,5,-0.236612126917841,1,0.75,5.49847238768327 +323,24,Group 2,3.92577538341629,2010,2016,0.0638345850097706,TRUE,6,6,1.74263373300415,1,0.9,7.63224370143021 +323,24,Group 2,3.92577538341629,2010,2017,0.329420388507327,TRUE,7,6,0.220834456346281,1,1.05,6.52603022826989 +323,24,Group 2,3.92577538341629,2010,2018,-0.111974912848881,TRUE,8,6,0.140866337948501,1,1.2,6.15466680851591 +323,24,Group 2,3.92577538341629,2010,2019,-0.00894864853974133,TRUE,9,6,1.39253702395426,1,1.35,7.65936375883081 +323,24,Group 2,3.92577538341629,2010,2020,-0.374950507229263,TRUE,10,6,1.89130588416266,1,1.5,7.94213076034968 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1,6.29347865279113,2000,1998,0.0760938602866395,FALSE,-2,-2,-0.144725614600288,0,0,6.22484689847748 +324,32,Group 1,6.29347865279113,2000,1999,0.136987237048632,FALSE,-1,-1,1.04381569621657,0,0,7.47428158605633 +324,32,Group 1,6.29347865279113,2000,2000,0.18154398886121,TRUE,0,0,-0.406592100241661,2,0,8.06843054141068 +324,32,Group 1,6.29347865279113,2000,2001,0.356223316177845,TRUE,1,1,0.101526269869978,2,0.05,8.80122823883895 +324,32,Group 1,6.29347865279113,2000,2002,-0.531970898974565,TRUE,2,2,-0.180171625875447,2,0.1,7.68133612794112 +324,32,Group 1,6.29347865279113,2000,2003,-0.130892305477,TRUE,3,3,-1.84413366051612,2,0.15,6.468452686798 +324,32,Group 1,6.29347865279113,2000,2004,-0.0835454927715576,TRUE,4,4,-0.233599694964693,2,0.2,8.17633346505488 +324,32,Group 1,6.29347865279113,2000,2005,-0.168886421474279,TRUE,5,5,-1.59309554146978,2,0.25,6.78149668984707 +324,32,Group 1,6.29347865279113,2000,2006,-0.198553870392569,TRUE,6,6,-0.0901273923883836,2,0.3,8.30479739001018 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1,6.29347865279113,2000,2015,0.0593091311848276,TRUE,15,6,0.223149494607555,2,0.75,9.32593727858351 +324,32,Group 1,6.29347865279113,2000,2016,0.0638345850097706,TRUE,16,6,2.03363731801113,2,0.8,11.190950555812 +324,32,Group 1,6.29347865279113,2000,2017,0.329420388507327,TRUE,17,6,1.51538003934535,2,0.85,10.9882790806438 +324,32,Group 1,6.29347865279113,2000,2018,-0.111974912848881,TRUE,18,6,-0.706152807370496,2,0.9,8.37535093257175 +324,32,Group 1,6.29347865279113,2000,2019,-0.00894864853974133,TRUE,19,6,-1.07158805464043,2,0.95,8.16294194961096 +324,32,Group 1,6.29347865279113,2000,2020,-0.374950507229263,TRUE,20,6,-0.132367507053389,2,1,8.78616063850848 +325,6,Group 2,1.91622093286539,2010,1990,0.0661588861645665,FALSE,-20,-6,-1.17679967519757,0,0,0.805580143832382 +325,6,Group 2,1.91622093286539,2010,1991,-0.0309803200634303,FALSE,-19,-6,-1.07446666350803,0,0,0.810773949293921 +325,6,Group 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2,2.24794917252688,2010,2001,0.356223316177845,FALSE,-9,-6,0.0354742562094124,0,0,2.63964674491414 +938,14,Group 2,2.24794917252688,2010,2002,-0.531970898974565,FALSE,-8,-6,-1.58710670407452,0,0,0.128871569477791 +938,14,Group 2,2.24794917252688,2010,2003,-0.130892305477,FALSE,-7,-6,-1.64038870334203,0,0,0.476668163707855 +938,14,Group 2,2.24794917252688,2010,2004,-0.0835454927715576,FALSE,-6,-6,1.35930323287083,0,0,3.52370691262616 +938,14,Group 2,2.24794917252688,2010,2005,-0.168886421474279,FALSE,-5,-5,-0.689957399116346,0,0,1.38910535193626 +938,14,Group 2,2.24794917252688,2010,2006,-0.198553870392569,FALSE,-4,-4,-0.96556221990039,0,0,1.08383308223392 +938,14,Group 2,2.24794917252688,2010,2007,0.0736411662970163,FALSE,-3,-3,-0.572063979920347,0,0,1.74952635890355 +938,14,Group 2,2.24794917252688,2010,2008,0.0419510640347825,FALSE,-2,-2,-1.0860107864172,0,0,1.20388945014446 +938,14,Group 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2,5.39239839533329,2010,2019,-0.00894864853974133,TRUE,9,6,-1.99331156509739,1,1.35,5.74013818169616 +1319,30,Group 2,5.39239839533329,2010,2020,-0.374950507229263,TRUE,10,6,0.749173115758729,1,1.5,8.26662100386275 +1320,32,Group 2,7.19867455116693,2010,1990,0.0661588861645665,FALSE,-20,-6,1.14818055974197,0,0,8.41301399707347 +1320,32,Group 2,7.19867455116693,2010,1991,-0.0309803200634303,FALSE,-19,-6,-0.178355093277177,0,0,6.98933913782633 +1320,32,Group 2,7.19867455116693,2010,1992,-0.119607424539513,FALSE,-18,-6,0.684094247546803,0,0,7.76316137417422 +1320,32,Group 2,7.19867455116693,2010,1993,0.12632146154317,FALSE,-17,-6,0.515446789417313,0,0,7.84044280212742 +1320,32,Group 2,7.19867455116693,2010,1994,-0.106920550397923,FALSE,-16,-6,0.181052303103038,0,0,7.27280630387205 +1320,32,Group 2,7.19867455116693,2010,1995,-0.270420204800212,FALSE,-15,-6,-0.291993299605861,0,0,6.63626104676086 +1320,32,Group 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1,6.49826518861432,2000,2020,-0.374950507229263,TRUE,20,6,0.962326039344407,2,1,10.0856407207295 +1459,12,Group 2,2.61152353337949,2010,1990,0.0661588861645665,FALSE,-20,-6,1.23081986798658,0,0,3.90850228753064 +1459,12,Group 2,2.61152353337949,2010,1991,-0.0309803200634303,FALSE,-19,-6,0.350628527893865,0,0,2.93117174120992 +1459,12,Group 2,2.61152353337949,2010,1992,-0.119607424539513,FALSE,-18,-6,-0.0257791195884383,0,0,2.46613698925154 +1459,12,Group 2,2.61152353337949,2010,1993,0.12632146154317,FALSE,-17,-6,-1.38994276654604,0,0,1.34790222837662 +1459,12,Group 2,2.61152353337949,2010,1994,-0.106920550397923,FALSE,-16,-6,-0.275752729851369,0,0,2.2288502531302 +1459,12,Group 2,2.61152353337949,2010,1995,-0.270420204800212,FALSE,-15,-6,-0.820606235987124,0,0,1.52049709259215 +1459,12,Group 2,2.61152353337949,2010,1996,0.00320779333995708,FALSE,-14,-6,0.000804686323610893,0,0,2.61553601304306 +1459,12,Group 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2,2.61152353337949,2010,2005,-0.168886421474279,FALSE,-5,-5,0.687901236939171,0,0,3.13053834884438 +1459,12,Group 2,2.61152353337949,2010,2006,-0.198553870392569,FALSE,-4,-4,-0.609612789466311,0,0,1.80335687352061 +1459,12,Group 2,2.61152353337949,2010,2007,0.0736411662970163,FALSE,-3,-3,-0.71817657599642,0,0,1.96698812368009 +1459,12,Group 2,2.61152353337949,2010,2008,0.0419510640347825,FALSE,-2,-2,1.08168686462737,0,0,3.73516146204164 +1459,12,Group 2,2.61152353337949,2010,2009,0.117226819959701,FALSE,-1,-1,0.392653770654779,0,0,3.12140412399397 +1459,12,Group 2,2.61152353337949,2010,2010,-0.274527203114244,TRUE,0,0,0.729000755767629,1,0,4.06599708603287 +1459,12,Group 2,2.61152353337949,2010,2011,-0.199874870651002,TRUE,1,1,0.557926291115833,1,0.15,4.11957495384432 +1459,12,Group 2,2.61152353337949,2010,2012,-0.383607038892157,TRUE,2,2,0.228623298423505,1,0.3,3.75653979291084 +1459,12,Group 2,2.61152353337949,2010,2013,0.243287094162147,TRUE,3,3,-1.12335187435245,1,0.45,3.18145875318919 +1459,12,Group 2,2.61152353337949,2010,2014,0.118752505489616,TRUE,4,4,0.74535478906409,1,0.6,5.07563082793319 +1459,12,Group 2,2.61152353337949,2010,2015,0.0593091311848276,TRUE,5,5,-1.63104226904592,1,0.75,2.78979039551839 +1459,12,Group 2,2.61152353337949,2010,2016,0.0638345850097706,TRUE,6,6,0.124399490521786,1,0.9,4.69975760891105 +1459,12,Group 2,2.61152353337949,2010,2017,0.329420388507327,TRUE,7,6,-0.286692787401147,1,1.05,4.70425113448567 +1459,12,Group 2,2.61152353337949,2010,2018,-0.111974912848881,TRUE,8,6,-0.455567282704344,1,1.2,4.24398133782626 +1459,12,Group 2,2.61152353337949,2010,2019,-0.00894864853974133,TRUE,9,6,0.334737057362885,1,1.35,5.28731194220263 +1459,12,Group 2,2.61152353337949,2010,2020,-0.374950507229263,TRUE,10,6,-0.458570314449991,1,1.5,4.27800271170023 +1460,17,Group 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+1500,2,Group 3,0.528683270134023,0,2019,-0.00894864853974133,FALSE,Inf,6,-0.552170153527588,0,0,-0.0324355319333062 +1500,2,Group 3,0.528683270134023,0,2020,-0.374950507229263,FALSE,Inf,6,-0.071594786013528,0,0,0.0821379768912323 diff --git a/pyfixest/did.py b/pyfixest/did.py new file mode 100644 index 00000000..bc8a157f --- /dev/null +++ b/pyfixest/did.py @@ -0,0 +1,169 @@ +from pyfixest.estimation import feols +from pyfixest.demean import demean +from abc import ABC, abstractmethod +from formulaic import model_matrix +from scipy.sparse.linalg import spsolve +import pandas as pd +import numpy as np +import warnings +import time + +def event_study(data, yname, idname, tname, gname, xfml, estimator): + + if estimator == "did2s": + + did2s = DID2S(data = data, yname = yname, idname=idname, tname = tname, gname = gname, xfml = xfml) + fit = did2s.estimate() + #vcov = did2s.vcov() + #fit._vcov = vcov + # update inference with new vcov matrix + #fit._get_inference() + + else: + raise Exception("Estimator not supported") + + return fit + + +class DID(ABC): + + @abstractmethod + def __init__(self, data, yname, idname, tname, gname, xfml): + + """ + Args: + data: The DataFrame containing all variables. + yname: The name of the dependent variable. + idname: The name of the id variable. + tname: Variable name for calendar period. + gname: unit-specific time of initial treatment. + xfml: The formula for the covariates. + estimator: The estimator to use. Options are "did2s". + Returns: + None + """ + + # do some checks here + + self._data = data.copy() + self._yname = yname + self._idname = idname + self._tname = tname + self._gname = gname + self._xfml = xfml + + # create additional columns + + + @abstractmethod + def estimate(self): + pass + + @abstractmethod + def vcov(self): + pass + + #@abstractmethod + #def aggregate(self): + # pass + + +class DID2S(DID): + + def __init__(self, data, yname, idname, tname, gname, xfml): + + super().__init__(data, yname, idname, tname, gname, xfml) + + self._estimator = "did2s" + + if self._xfml is not None: + self._fml1 = f"{yname} ~ {xfml} | {idname} + {tname}" + self._fml2 = f"Y_hat ~ {xfml} + treat" + else: + self._fml1 = f"{yname} ~ 0 | {idname} + {tname}" + self._fml2 = f"Y_hat ~ treat" + + #self._not_yet_treated_idx = data[data["treat"] == False] #data[gname] <= data[tname] + + + def estimate(self): + + _fml1 = self._fml1 + _fml2 = self._fml2 + _data = self._data + _not_yet_treated_data = _data[_data["treat"] == False] + + + tic = time.time() + fit1 = feols(fml = _fml1, data = _not_yet_treated_data) + toc = time.time() - tic + print(f"Time for first fit: {toc} seconds") + + tic = time.time() + fit1.fixef() + toc = time.time() - tic + print(f"Time for fixef: {toc} seconds") + + tic = time.time() + Y_hat = fit1.predict(newdata = _data) + toc = time.time() - tic + print(f"Time for predict: {toc} seconds") + + _data["Y_hat"] = Y_hat + + tic = time.time() + fit2 = feols(_fml2, data = _data) + toc = time.time() - tic + print(f"Time for second fit: {toc} seconds") + + return fit2 + + def vcov(self, cluster = None): + + _data = self._data + if cluster is None: + cluster = self._idname + + cluster_col = _data[cluster] + _, clustid = pd.factorize(cluster_col) + + fml_group_time = f"~C({self._idname}) + C({self._tname})" + fml_treatment_vars = "~treat" + + X1 = model_matrix(fml_group_time, _data, output = "sparse") + #X10 = X1[self._not_yet_treated_idx, :] + X2 = model_matrix(fml_treatment_vars, _data, output = "sparse") + + X2X2 = (X2.T.dot(X2)).toarray() + X2X2inv = np.linalg.inv(X2X2) + + # clustering + + sum_WgWg = np.zeros((X1.shape[1], X1.shape[1])) + for (_,g,) in enumerate(clustid): + + X1g = X1[cluster_col == g, :] + X2g = X2[cluster_col == g, :] + e1g = None + e2g = None + + A = X2g.T @ e2g + B = e1g.T @ X1g + C = spsolve(X1g.T.dot(X1g)) + D = X1g.T.dot(X2g) + + sum_WgWg += A - B @ C @ D + + self._vcov = X2X2inv @ sum_WgWg @ X2X2inv + + return self._vcov + + + + + + + + + + diff --git a/pyfixest/exceptions.py b/pyfixest/exceptions.py index 5ffa9901..55f6a438 100644 --- a/pyfixest/exceptions.py +++ b/pyfixest/exceptions.py @@ -51,4 +51,7 @@ class NonConvergenceError(Exception): class MatrixNotFullRankError(Exception): + pass + +class EmptyDesignMatrixError(Exception): pass \ No newline at end of file diff --git a/pyfixest/feols.py b/pyfixest/feols.py index 9918cf8a..20874ba0 100644 --- a/pyfixest/feols.py +++ b/pyfixest/feols.py @@ -15,6 +15,7 @@ from pyfixest.exceptions import ( VcovTypeNotSupportedError, NanInClusterVarError, + EmptyDesignMatrixError ) @@ -54,17 +55,24 @@ def __init__( self._Y = Y self._X = X + self.get_nobs() _feols_input_checks(Y, X, weights) - self._collin_tol = collin_tol - ( - self._X, - self._coefnames, - self._collin_vars, - self._collin_index, - ) = _drop_multicollinear_variables(self._X, coefnames, self._collin_tol) + + if self._X.shape[1] == 0: + self._X_is_empty = True + else: + self._X_is_empty = False + self._collin_tol = collin_tol + ( + self._X, + self._coefnames, + self._collin_vars, + self._collin_index, + ) = _drop_multicollinear_variables(self._X, coefnames, self._collin_tol) + self._Z = self._X self._weights = weights @@ -782,10 +790,15 @@ def fixef(self) -> None: fml_linear = f"{depvars} ~ {covars}" Y, X = model_matrix(fml_linear, _data) - X = X[self._coefnames] # drop intercept, potentially multicollinear vars - Y = Y.to_numpy().flatten().astype(np.float64) - X = X.to_numpy() - uhat = csr_matrix(Y - X @ self._beta_hat).transpose() + if self._X_is_empty: + Y = Y.to_numpy() + uhat = Y + + else: + X = X[self._coefnames] # drop intercept, potentially multicollinear vars + Y = Y.to_numpy().flatten().astype(np.float64) + X = X.to_numpy() + uhat = csr_matrix(Y - X @ self._beta_hat).transpose() D2 = model_matrix("-1+" + fixef_fml, _data, output="sparse") cols = D2.model_spec.column_names @@ -885,11 +898,15 @@ def predict(self, newdata: Optional[pd.DataFrame] = None) -> np.ndarray: fml_linear = _fml fml_fe = None - # deal with the linear part - _, X = model_matrix(fml_linear, newdata) - X = X[self._coefnames] - X = X.to_numpy() - y_hat = X @ _beta_hat + if not self._X_is_empty: + # deal with linear part + _, X = model_matrix(fml_linear , newdata) + X = X[self._coefnames] + X = X.to_numpy() + y_hat = X @ _beta_hat + else: + y_hat = np.zeros(newdata.shape[0]) + if self._has_fixef: y_hat += np.sum(fixef_mat, axis=1) diff --git a/pyfixest/model_matrix_fixest.py b/pyfixest/model_matrix_fixest.py index 40ea440b..8ebb0fa2 100644 --- a/pyfixest/model_matrix_fixest.py +++ b/pyfixest/model_matrix_fixest.py @@ -109,6 +109,11 @@ def model_matrix_fixest( # fml_iv already created Y, X = model_matrix(fml_exog, data) + + X_is_empty = False # special case: sometimes it is useful to run models "Y ~ 0 | f1" to demean Y + to use the predict method + if X.shape[1] == 0: + X_is_empty = True + # if int, turn, Y into int64, else float64 # if Y is int if Y.dtypes[0] == "int64": @@ -163,7 +168,8 @@ def model_matrix_fixest( if _ivars is not None: if _drop_ref is not None: - X.drop(_drop_ref, axis=1, inplace = True) + if not X_is_empty: + X.drop(_drop_ref, axis=1, inplace = True) if _is_iv: Z.drop(_drop_ref, axis=1, inplace = True) @@ -181,7 +187,8 @@ def model_matrix_fixest( if fe is not None: fe.drop(na_index, axis=0, inplace = True) # drop intercept - X.drop("Intercept", axis=1, inplace=True) + if not X_is_empty: + X.drop("Intercept", axis=1, inplace=True) # x_names.remove("Intercept") if _is_iv: Z.drop("Intercept", axis=1, inplace=True) @@ -191,7 +198,8 @@ def model_matrix_fixest( fe_na_remaining = list(set(fe_na) - set(na_index)) if fe_na_remaining: Y.drop(fe_na_remaining, axis=0, inplace = True) - X.drop(fe_na_remaining, axis=0, inplace = True) + if not X_is_empty: + X.drop(fe_na_remaining, axis=0, inplace = True) fe.drop(fe_na_remaining, axis=0, inplace = True) if _is_iv: Z.drop(fe_na_remaining, axis=0, inplace = True) @@ -201,7 +209,7 @@ def model_matrix_fixest( na_index_str = ",".join(str(x) for x in na_index) - return Y, X, fe, endogvar, Z, na_index, na_index_str, _icovars + return Y, X, fe, endogvar, Z, na_index, na_index_str, _icovars, X_is_empty def _find_ivars(x): From fa7dfc3c113fadb608a1d5f95dfd8e08fac7282d Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 21 Oct 2023 22:12:31 +0200 Subject: [PATCH 02/43] reproduce did2s.r results --- 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Date time variables are currently not accepted. + gname: unit-specific time of initial treatment. Must be an integer in the format YYYYMMDDHHMMSS, i.e. it must be + possible to compare two dates via '>'. Date time variables are currently not accepted. Never treated units + must have a value of 0. xfml: The formula for the covariates. estimator: The estimator to use. Options are "did2s". + att: Whether to estimate the average treatment effect on the treated (ATT) or the + canonical event study design with all leads and lags. Default is True. Returns: None """ @@ -64,7 +100,45 @@ def __init__(self, data, yname, idname, tname, gname, xfml): self._gname = gname self._xfml = xfml - # create additional columns + # check if idname, tname and gname are in data + if self._idname not in self._data.columns: + raise ValueError(f"The variable {self._idname} is not in the data.") + if self._tname not in self._data.columns: + raise ValueError(f"The variable {self._tname} is not in the data.") + if self._gname not in self._data.columns: + raise ValueError(f"The variable {self._gname} is not in the data.") + + # check if tname and gname are of type int (either int 64, 32, 8) + if self._data[self._tname].dtype not in ["int64", "int32", "int8"]: + raise ValueError(f"The variable {self._tname} must be of type int64, and more specifically, in the format YYYYMMDDHHMMSS.") + if self._data[self._gname].dtype not in ["int64", "int32", "int8"]: + raise ValueError(f"The variable {self._gname} must be of type int64, and more specifically, in the format YYYYMMDDHHMMSS.") + + # check if there is a never treated unit + if 0 not in self._data[self._gname].unique(): + raise ValueError(f"There must be at least one unit that is never treated.") + + # create a treatment variable + self._data["zz00_treat"] = (self._data[self._tname] >= self._data[self._gname]) * (self._data[self._gname] > 0) + + # create treat variable + if att: + if "treat" not in self._data.columns: + # Step 1: check if tname and gname are of type int + if self._data[self._tname].dtype != "int64": + raise ValueError(f"The variable {self._tname} must be of type int64, and more specifically, in the format YYYYMMDDHHMMSS.") + if self._data[self._gname].dtype != "int64": + raise ValueError(f"The variable {self._gname} must be of type int64, and more specifically, in the format YYYYMMDDHHMMSS.") + self._data["treat"] = (self._data[self._tname] >= self._data[self._gname]) + #else: + # # get rel_year variable + # if "rel_year" not in self._data.columns: + # self._data["rel_year"] = self._data[self._tname] - self._data[self._gname] + + # get never treated units + #ct = pd.crosstab(self._data[self._idname], self._data["treat"]) + + @abstractmethod @@ -82,15 +156,15 @@ def vcov(self): class TWFE(DID): - def __init__(self, data, yname, idname, tname, gname, xfml): - super().__init__(data, yname, idname, tname, gname, xfml) + def __init__(self, data, yname, idname, tname, gname, xfml, att): + super().__init__(data, yname, idname, tname, gname, xfml, att) self._estimator = "twfe" if self._xfml is not None: - self._fml = f"{yname} ~ treat + {xfml} | {idname} + {tname}" + self._fml = f"{yname} ~ zz00_treat + {xfml} | {idname} + {tname}" else: - self._fml = f"{yname} ~ treat | {idname} + {tname}" + self._fml = f"{yname} ~ zz00_treat | {idname} + {tname}" def estimate(self): @@ -110,18 +184,18 @@ def vcov(self, cluster = None): class DID2S(DID): - def __init__(self, data, yname, idname, tname, gname, xfml): + def __init__(self, data, yname, idname, tname, gname, xfml, att): - super().__init__(data, yname, idname, tname, gname, xfml) + super().__init__(data, yname, idname, tname, gname, xfml, att) self._estimator = "did2s" if self._xfml is not None: self._fml1 = f"{yname} ~ {xfml} | {idname} + {tname}" - self._fml2 = f"{yname} ~ {xfml} + treat" + self._fml2 = f"{yname} ~ 0 + zz00_treat + {xfml}" else: self._fml1 = f"{yname} ~ 0 | {idname} + {tname}" - self._fml2 = f"{yname} ~ - 1 + treat" + self._fml2 = f"{yname} ~ 0 + zz00_treat" #self._not_yet_treated_idx = data[data["treat"] == False] #data[gname] <= data[tname] @@ -131,7 +205,7 @@ def estimate(self): _fml1 = self._fml1 _fml2 = self._fml2 _data = self._data - _not_yet_treated_data = _data[_data["treat"] == False] + _not_yet_treated_data = _data[_data["zz00_treat"] == False] _yname = self._yname @@ -178,14 +252,14 @@ def vcov(self, cluster = None): self._G = clustid.nunique() fml_group_time = f"~C({self._idname}) + C({self._tname})" # add covariates - fml_treatment_vars = "~0+treat" # add covariates + fml_treatment_vars = "~0+zz00_treat" # add covariates X1 = model_matrix(fml_group_time, _data, output = "sparse") #X10 = X1[self._not_yet_treated_idx, :] X2 = model_matrix(fml_treatment_vars, _data, output = "sparse") X10 = X1.copy().tocsr() - treated_rows = np.where(_data["treat"], 0, 1) + treated_rows = np.where(_data["zz00_treat"], 0, 1) X10 = X10.multiply(treated_rows[:, None]) X10X10 = X10.T.dot(X10) From 258b381f1719e4d27b8270fae507d669d88f175d Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 22 Oct 2023 11:18:02 +0200 Subject: [PATCH 05/43] some defensive input checks --- pyfixest/__pycache__/did.cpython-310.pyc | Bin 7845 -> 9143 bytes pyfixest/did.py | 131 ++++++++++++++--------- 2 files changed, 80 insertions(+), 51 deletions(-) diff --git a/pyfixest/__pycache__/did.cpython-310.pyc b/pyfixest/__pycache__/did.cpython-310.pyc index 82b0c03feb408c32e6f8f2aa9c159144a68e8a82..05ad659568c6c54066909190391a16e057516e6d 100644 GIT binary patch delta 3006 zcma)8O>7)V74HAP>G`)E+p$AOtg~*gz41VHvq{Wm*Ktg;cm%s2?T!9F0t0Vybl638wJFS z>eZ|FUcIV*^}Sd1gOv}~b73}XaPa&7pFZoIIs0C2rvDunArA2Y=kTl-91(gUkXo`M z^He7WN=tQA#!G?L(jC2JI0hTbL8fIoCgYVL+sZjP;7iCrnR~pGU+0jCviCTY_4K=< zQ-E;}#pUMp{3?g94)iA!+>I7cPYsVg1 z!#Kq6{k!t1REqZ+-e#)!sO`4Ax=v>fe_811*RDc~5cwj1(2u!9i^aP_^wxC_8F%@w z^NEHsw}j|z7@KUYpzJLH-;d>lOZY@c#6(KuLL594gI@hq2SVh&vzO%)uO&79Ap(gXfZ1{IkLDbAxZ z+njM&IO2FNF(67~kBD$W?)N6^K{tv$Y_*`Xwmi$Vq8R(_ZA^pFn|NA1h*T;gA5|h- zOC{vSZYuB7_^~Oe98*4(w^=9MZnT0_bmKTxhrUv2^^Lcddpc0fnqS}8u(3uyQh&ad z#Ti%=o0QPiF+BsEqa+U`)x9Y8TW%a;T%f`tCG?C^vEj$KOotlzhjzU}Cq^#<89Pzp z2mk3|U*I)iQqXvfSJo&QbpSAC(cCR`1U5-7B$g7_|vKN;&t zVA16u{$iKI3m}n(5)H{|8crDH#@Yoc;%OUMR9O|WG{g;P&>9M{K zc;lLYtf2#MXdpi}V0vmJO>s>V>9EM&h!f*@5$5OE=9?pahBhceSD~v-afjI>4m`ad ziy35+w=<_^268~YG{#Xol)VHs06>*>_wJ~VMZ8R`(A8C-A*UOkhM!!dzaRBA%Ov)U9@i@FY>qB z)}9~lSb^svnAS#C;BH5jA3bCG0MIL|1H1#mt*w1PJ6m*qD`4B(54+f^?}UEcd$tqu zz5MS4J4gD3<#9)<0jwRZHvAwg0$j+$!dzKTrN|2!qi`l47S28Q1u$T+G$se&drS=g zqya#LMiz=wYDMgsSFT+le=kpvxM)qi3={F0WAZ%Zrh$Bm{J2;tT%^NG$J!P0x8ezf zmS+4pVwTP+GG#B2_0pN*bzmc!Q5q)*1~06TcS>{p3J5$_!W{wej(DGI@JK-7J!4vdjYuAs6DA!pP>S&2K5l1CqFHp%u|a4Xc7I&=`r$Qxjp*= zuvh>>E&bS*FgyFtQ}GL=JvPJtjC_0SVn3p_i6x!Tb6D)?jyJq`CqxYVZO=oHG%Wje z>*1~ig%kU?DXi;mQ11pVcl zd+C?3g^2D|QhNI4SFWxt(S;Zif2848=$0>2@+y#2t<_LiuhjsEbK~cV?}Lnex5i0= zDQUCBA3y!#Yaj(!O+hz=)f4nQjiq5={jg2$i}t)VUpZS@wg+4P?>PDz#Zmv6I9mK) zI2vLIFm?bXye1#xN_`qv%u+i5d=9K^9k0Uou#2kN6RmeFOkSB-y-ZIZuT#Q+AyZ&1 zr>v{s^HhF~5*BWj@N~lOp|Si0xcp+m&VTHd`UXe-HL=i#hlhm32f`gCQ4VEx<10;o zKa${80NYLA!ho_450FFch)dK-DB?98(n%laE^)C$pDsuSA6lXv8eqlMGKmH`uT=3O z?BObY9Vk4l)V8-rp8z(`s-11($uEHhzd;ED^I6JKl#mZ5FPHQIl16NZK6TQdMyUjV zUnb|LPJiMf7)H}T6oHn=0yI<9L{psPP2Ln9y*o87WSI9GV1ym=@Z|HEWy&g)ERjE+ zoaa@NnR#xUr4Iw{sQs0hUkN5p%-K^sPd+{Svd+q96(*Bc$H)5=fWL({icqUG$$)7~ zRhH$O>_V~2snUUq$BpnfOi1N+x7FFl4a%20`wjm#;B@H;RtLE~qPbo7Ix&DaV%G<} zl(y>lEKP1q%XX?aLfnF6_3P}@(sESyJNrvf$HkGi6!>j7*v2Y({nUl&39#2V4dP?+ Q1!yL3NQITc#@I{$0cYRO>;M1& delta 2755 zcma(TOKcm*b!Pt`ia$}7WXl32+i_SpswLQHBH50bI98k>N@EKyx@bVqoTa_=k`!iF zvMsDipf=!>fdb#zac zxE2~6(=$7kXGvNQ?T+I)5;sD(lk;*CH^UJx4|dG3;1%Uw@=9_qdu3K;4s-8QZ*+yQ z9J?}dpRf_%yr+5FMyw|!p^1{HiI(Vz z$=rwLwv|}B#va+WVK-rS5+||i7Rv#&*$6-ZC-b%H|4_i;;efH92P>Egy z;EZQ6Lzq{~G!OB_RR$dY71g41s;1^>S;;uFRQp<^Te*vuFN*caLt?vJrHkU%<#GBm z@j-dvg>zt)pGWWsbEOqPqdWkMD2hXsOX9)k ziT)fgeBx0?JmmptQAU|^-||$ZGVMO`GzO7iCNvfO0VJ4bK+V{^3WpBVKc*knzX~h5 z4j?A?$S&Pi_eNswnb5-^V08t8rZTbtdHG7BU`izhKsCYi)DlR-9TU_vMy?Vm*Y`;v zTQW(_SmpRk@z04DSC#-to%+*CJA(|Z4wcGZ0`xIW?Rvc#HlnCr&u&j%7oK|`R)j@4 zLB;jS?$OhLa(oj!AH+3{FCyo41gFK9lT-Aua1Jlk`N4416seb*&2S@%eV#q#U}T=4 zjh6vz*=s+%^xE8Jv$rV&`q<>lDEmHwR}d@-`^eeC4xka6G5KjHC7!2+o;h^dJq)j*`P>Oa|fb}YLF<_s_b>*!PMntyl9Ro z$Yo@7jLdv0w*?s?j&<`)WIh6xOxX!V6pz*l*j0yJ?hqBPA3fFI)nI-YFlA_>G4-Lc zZN?h%5@UjFTd@HNWkFi$*lZ}tWZRI9ICuU{4|R=i*KJ#g%1jt0_A|Dvq`%KNj5uhJ zlVsQ4BiP9jdjfh_-Gy{_GugSJ1K==>L}r7-&ND_~^gVSradvZHiPT3DXiznGmA?R& zaEZSOaI3Nw^gDjF)#KGxz@r#oyjb0`s*&I9by>7kTnmQsJAP}i3bWko`nPe&ZIy?@ zb?&pE2}f&GcCxLpp(wMI8Ls8|JMdV31%a%Rc~ZeLf$`>N6{D~7pc@N!db#XoWwY;u zcz629%sd=U)i`LT8hASoXs?;-KdRqsz&HiYg4iopzV}zqlH<@&uzeO`SqBd`@wCv{2;*R#_nLkb1>GB5 zfg|bY`rTG=2YS=opg#q@ZgyRTp(Ojj;+Wm=X>q;Db@U9|cWOg?!ER$FXv()pN26x2 zes?ZfZ}7;U3xjSWyuq= self._data[self._gname]) * (self._data[self._gname] > 0) - # create treat variable - if att: - if "treat" not in self._data.columns: - # Step 1: check if tname and gname are of type int - if self._data[self._tname].dtype != "int64": - raise ValueError(f"The variable {self._tname} must be of type int64, and more specifically, in the format YYYYMMDDHHMMSS.") - if self._data[self._gname].dtype != "int64": - raise ValueError(f"The variable {self._gname} must be of type int64, and more specifically, in the format YYYYMMDDHHMMSS.") - self._data["treat"] = (self._data[self._tname] >= self._data[self._gname]) - #else: - # # get rel_year variable - # if "rel_year" not in self._data.columns: - # self._data["rel_year"] = self._data[self._tname] - self._data[self._gname] - - # get never treated units - #ct = pd.crosstab(self._data[self._idname], self._data["treat"]) - - - @abstractmethod def estimate(self): @@ -156,8 +150,31 @@ def vcov(self): class TWFE(DID): - def __init__(self, data, yname, idname, tname, gname, xfml, att): - super().__init__(data, yname, idname, tname, gname, xfml, att) + """ + Estimate a Difference-in-Differences model using the two-way fixed effects estimator. + """ + + def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): + + """ + Args: + data: The DataFrame containing all variables. + yname: The name of the dependent variable. + idname: The name of the id variable. + tname: Variable name for calendar period. Must be an integer in the format YYYYMMDDHHMMSS, i.e. it must be + possible to compare two dates via '>'. Date time variables are currently not accepted. + gname: unit-specific time of initial treatment. Must be an integer in the format YYYYMMDDHHMMSS, i.e. it must be + possible to compare two dates via '>'. Date time variables are currently not accepted. Never treated units + must have a value of 0. + xfml: The formula for the covariates. + att: Whether to estimate the average treatment effect on the treated (ATT) or the + canonical event study design with all leads and lags. Default is True. + cluster (str): The name of the cluster variable. + Returns: + None + """ + + super().__init__(data, yname, idname, tname, gname, xfml, att, cluster) self._estimator = "twfe" @@ -178,15 +195,41 @@ def estimate(self): def vcov(self, cluster = None): + """ + Method not needed. The vcov matrix is calculated via the `Feols` object. + """ + pass class DID2S(DID): - def __init__(self, data, yname, idname, tname, gname, xfml, att): + """ + Difference-in-Differences estimation using Gardner's two-step DID2S estimator (2021). + """ + + def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): + + """ + Args: + data: The DataFrame containing all variables. + yname: The name of the dependent variable. + idname: The name of the id variable. + tname: Variable name for calendar period. Must be an integer in the format YYYYMMDDHHMMSS, i.e. it must be + possible to compare two dates via '>'. Date time variables are currently not accepted. + gname: unit-specific time of initial treatment. Must be an integer in the format YYYYMMDDHHMMSS, i.e. it must be + possible to compare two dates via '>'. Date time variables are currently not accepted. Never treated units + must have a value of 0. + xfml: The formula for the covariates. + att: Whether to estimate the average treatment effect on the treated (ATT) or the + canonical event study design with all leads and lags. Default is True. + cluster (str): The name of the cluster variable. + Returns: + None + """ - super().__init__(data, yname, idname, tname, gname, xfml, att) + super().__init__(data, yname, idname, tname, gname, xfml, att, cluster) self._estimator = "did2s" @@ -197,8 +240,6 @@ def __init__(self, data, yname, idname, tname, gname, xfml, att): self._fml1 = f"{yname} ~ 0 | {idname} + {tname}" self._fml2 = f"{yname} ~ 0 + zz00_treat" - #self._not_yet_treated_idx = data[data["treat"] == False] #data[gname] <= data[tname] - def estimate(self): @@ -208,29 +249,18 @@ def estimate(self): _not_yet_treated_data = _data[_data["zz00_treat"] == False] _yname = self._yname - - tic = time.time() + # estimate first stage fit1 = feols(fml = _fml1, data = _not_yet_treated_data) - toc = time.time() - tic - print(f"Time for first fit: {toc} seconds") - tic = time.time() + # obtain estimated fixed effects fit1.fixef() - toc = time.time() - tic - print(f"Time for fixef: {toc} seconds") - tic = time.time() + # demean data Y_hat = fit1.predict(newdata = _data) - toc = time.time() - tic - print(f"Time for predict: {toc} seconds") - self._first_u = _data[f"{_yname}"].to_numpy().flatten() - Y_hat _data[f"{_yname}"] = self._first_u - tic = time.time() fit2 = feols(_fml2, data = _data) - toc = time.time() - tic - print(f"Time for second fit: {toc} seconds") self._second_u = fit2.resid() return fit2 @@ -251,11 +281,10 @@ def vcov(self, cluster = None): self._G = clustid.nunique() - fml_group_time = f"~C({self._idname}) + C({self._tname})" # add covariates + fml_group_time = f"~C({self._idname}) + C({self._tname})" # add covariates fml_treatment_vars = "~0+zz00_treat" # add covariates X1 = model_matrix(fml_group_time, _data, output = "sparse") - #X10 = X1[self._not_yet_treated_idx, :] X2 = model_matrix(fml_treatment_vars, _data, output = "sparse") X10 = X1.copy().tocsr() From 1018f7835db37c8b37642e2972a945e61d2be6d9 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 22 Oct 2023 11:20:05 +0200 Subject: [PATCH 06/43] acknowledge kyle as copyright holder --- pyfixest/did.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyfixest/did.py b/pyfixest/did.py index ba1dfc5b..4565fa47 100644 --- a/pyfixest/did.py +++ b/pyfixest/did.py @@ -207,6 +207,9 @@ class DID2S(DID): """ Difference-in-Differences estimation using Gardner's two-step DID2S estimator (2021). + + Multiple parts of this code are direct translations of Kyle Butt's R code `did2s`, published + under MIT license: https://github.com/kylebutts/did2s/tree/main. """ def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): From 13c49d9a9b9b483a8446772642645aeec2ae11e5 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 22 Oct 2023 11:26:16 +0200 Subject: [PATCH 07/43] create experimental folder --- .../__pycache__/experimental.cpython-310.pyc | Bin 0 -> 9323 bytes pyfixest/experimental/__init__.py | 4 ++++ .../__pycache__/__init__.cpython-310.pyc | Bin 0 -> 375 bytes .../experimental/__pycache__/did.cpython-310.pyc | Bin 0 -> 9327 bytes pyfixest/{ => experimental}/did.py | 4 ++-- 5 files changed, 6 insertions(+), 2 deletions(-) create mode 100644 pyfixest/__pycache__/experimental.cpython-310.pyc create mode 100644 pyfixest/experimental/__init__.py create mode 100644 pyfixest/experimental/__pycache__/__init__.cpython-310.pyc create mode 100644 pyfixest/experimental/__pycache__/did.cpython-310.pyc rename pyfixest/{ => experimental}/did.py (99%) diff --git a/pyfixest/__pycache__/experimental.cpython-310.pyc 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mode 100644 index 00000000..0f849d36 --- /dev/null +++ b/pyfixest/experimental/__init__.py @@ -0,0 +1,4 @@ +print(""" + You have loaded the 'experimental' module. Code in this module is not yet stable and + may change in the future. 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fit._vcov_type_detail = "CRV (GMM)" + fit._vcov_type_detail = "CRV1 (GMM)" fit._G = did2s._G fit._method = "did2s" @@ -110,7 +110,7 @@ def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): raise NotImplementedError("Covariates are not yet supported.") if self._att is False: raise NotImplementedError("Event study design with leads and lags is not yet supported.") - if self._cluster is not "idname": + if self._cluster != "idname": raise NotImplementedError("Clustering by a variable of your choice is not yet supported.") # check if idname, tname and gname are in data From 9429cf3be33cfa5053f608f465e686fbb97dc736 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 22 Oct 2023 11:32:20 +0200 Subject: [PATCH 08/43] update docs --- docs/news.md | 59 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 59 insertions(+) diff --git a/docs/news.md b/docs/news.md index 7cbc70e7..c74767a9 100644 --- a/docs/news.md +++ b/docs/news.md @@ -1,5 +1,64 @@ # News +## PyFixest 0.11.0 (not yet released) + ++ Add an `event_study()` function with support for two-way fixed effects difference-in differences + and Gardner's two stage estimator. + +```python +%load_ext autoreload +%autoreload 2 +from pyfixest.experimental.did import event_study +from pyfixest.summarize import summary +import pandas as pd + +df_het = pd.read_csv("pyfixest/data/df_het.csv") + +fit_twfe = event_study( + data = df_het, + yname = "dep_var", + idname= "state", + tname = "year", + gname = "g", + estimator = "twfe" +) + +fit_did2s = event_study( + data = df_het, + yname = "dep_var", + idname= "state", + tname = "year", + gname = "g", + estimator = "did2s" +) + +summary([fit_twfe, fit_did2s]) + +# ### +# +# Estimation: TWFE +# Dep. var.: dep_var, Fixed effects: state+year +# Inference: CRV1 +# Observations: 46500 +# +# | Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % | +# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:| +# | zz00_treat | 2.135 | 0.044 | 48.803 | 0.000 | 2.049 | 2.220 | +# --- +# ### +# +# Estimation: DID2S +# Dep. var.: dep_var +# Inference: CRV1 (GMM) +# Observations: 46500 +# +# | Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % | +# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:| +# | zz00_treat | 2.152 | 0.048 | 45.208 | 0.000 | 2.059 | 2.246 | +# --- + +``` + ## PyFixest `0.10.3` - Allows for white space in the multiway clustering formula. From f03deb3b8015efbdf7cfac9cda14f5c10db37830 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 22 Oct 2023 11:33:48 +0200 Subject: [PATCH 09/43] update version --- docs/news.md | 6 +-- poetry.lock | 114 ++++++++++++++++++++++++------------------------- pyproject.toml | 2 +- 3 files changed, 61 insertions(+), 61 deletions(-) diff --git a/docs/news.md b/docs/news.md index c74767a9..39189937 100644 --- a/docs/news.md +++ b/docs/news.md @@ -1,9 +1,9 @@ # News -## PyFixest 0.11.0 (not yet released) +## PyFixest 0.10.5 -+ Add an `event_study()` function with support for two-way fixed effects difference-in differences - and Gardner's two stage estimator. ++ Adds an experimental `event_study()` function with support for two-way fixed effects difference-in differences + and Gardner's two stage estimator to `pyfixest.experimental.did`. ```python %load_ext autoreload diff --git a/poetry.lock b/poetry.lock index 799c914c..8075d5e2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -773,36 +773,36 @@ pypng = "*" [[package]] name = "llvmlite" -version = "0.41.0" +version = "0.41.1" description = "lightweight wrapper around basic LLVM functionality" category = "main" optional = false python-versions = ">=3.8" files = [ - 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Supports OLS, IV and Poisson regression and a range of inference procedures." 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The vcov matrix is calculated via the `Feols` object. @@ -244,12 +304,22 @@ def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): self._fml2 = f"{yname} ~ 0 + zz00_treat" - def estimate(self): + def estimate(self, fml_dict = None, treatment = None): + + if fml_dict is not None: + self._fml1 = fml_dict["fml1"] + self._fml2 = fml_dict["fml2"] _fml1 = self._fml1 _fml2 = self._fml2 + _data = self._data - _not_yet_treated_data = _data[_data["zz00_treat"] == False] + + if treatment is not None: + _not_yet_treated_data = _data[_data[treatment] == False] + else: + _not_yet_treated_data = _data[_data["zz00_treat"] == False] + _yname = self._yname # estimate first stage @@ -268,18 +338,22 @@ def estimate(self): return fit2 - def vcov(self, cluster = None): + def vcov(self): _data = self._data _first_u = self._first_u _second_u = self._second_u + _cluster = self._cluster - if cluster is None: - cluster = self._idname + #if _cluster is None: + # _cluster = self._idname + #else: + # if _cluster not in _data.columns: + # raise ValueError(f"The variable {_cluster} is not in the data.") - cluster_col = _data[cluster] + cluster_col = _data[_cluster] _, clustid = pd.factorize(cluster_col) self._G = clustid.nunique() From 30e2f900a8d1d83a6e2aa501a45093019a34efde Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 29 Oct 2023 19:51:17 +0100 Subject: [PATCH 11/43] get event_study() did2s to work again --- .../__pycache__/FixestMulti.cpython-310.pyc | Bin 20965 -> 20965 bytes .../__pycache__/FormulaParser.cpython-310.pyc | Bin 11325 -> 11325 bytes .../__pycache__/exceptions.cpython-310.pyc | Bin 2081 -> 2081 bytes pyfixest/__pycache__/feols.cpython-310.pyc | Bin 31811 -> 31811 bytes .../model_matrix_fixest.cpython-310.pyc | Bin 10239 -> 10239 bytes .../__pycache__/summarize.cpython-310.pyc | Bin 2632 -> 2632 bytes .../__pycache__/__init__.cpython-310.pyc | Bin 375 -> 375 bytes .../__pycache__/did.cpython-310.pyc | Bin 10667 -> 10673 bytes 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a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index 0a14b48a..c80152f3 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -75,60 +75,6 @@ def event_study(data, yname, idname, tname, gname, xfml = None, estimator = "twf return fit -def did2s(data, yname, first_stage, second_stage, treatment, cluster = None): - - """ - Estimate a treatment effect using Gardner's two-step DID2S estimator (2021). - - Multiple parts of this code are direct translations of Kyle Butt's R code `did2s`, published - under MIT license. - - Args: - data: The DataFrame containing all variables. - first_stage: The formula for the first stage regression. - second_stage: The formula for the second stage regression. - treatment: The name of the treatment variable. - cluster: The name of the cluster variable. - Returns: - A fitted model object of class feols. - - """ - - assert isinstance(data, pd.DataFrame), "data must be a pandas DataFrame" - assert isinstance(first_stage, str), "first_stage must be a string" - assert isinstance(second_stage, str), "second_stage must be a string" - assert isinstance(treatment, str), "treatment must be a string" - assert isinstance(cluster, str), "cluster must be a string" - - # check if treatment and cluster are in data - if treatment not in data.columns: - raise ValueError(f"The variable {treatment} is not in the data.") - - if cluster is not None: - if cluster not in data.columns: - raise ValueError(f"The variable {cluster} is not in the data.") - - # hack: set idname, tname and gname to fantasy values - did2s = DID2S(data = data, yname = yname, idname="a", tname = "b", gname = "c", xfml = None, att = False, cluster = cluster) - did2s._fml1 = first_stage - did2s._fml2 = second_stage - fml_dict = {"fml1": first_stage, "fml2": second_stage} - did2s._yname = None - did2s._idname = None - did2s._tname = None - - fit = did2s.estimate(fml_dict=fml_dict, treatment = treatment) - vcov = did2s.vcov() - fit._vcov = vcov - fit._vcov_type = "CRV1" - fit._vcov_type_detail = "CRV1 (GMM)" - fit._G = did2s._G - fit._method = "did2s" - - fit.get_inference() - - return fit - class DID(ABC): @@ -343,13 +289,6 @@ def vcov(self): _second_u = self._second_u _cluster = self._cluster - - #if _cluster is None: - # _cluster = self._idname - #else: - # if _cluster not in _data.columns: - # raise ValueError(f"The variable {_cluster} is not in the data.") - cluster_col = _data[_cluster] _, clustid = pd.factorize(cluster_col) From 7e839057a3f76d8ea845fad1fc4c035b7a620f53 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 29 Oct 2023 20:34:12 +0100 Subject: [PATCH 13/43] add basic tests --- .github/workflows/ci-tests.yaml | 2 +- .../__pycache__/did.cpython-310.pyc | Bin 10673 -> 9320 bytes pyfixest/{ => experimental}/data/df_het.csv | 0 .../test_did.cpython-310-pytest-7.3.1.pyc | Bin 0 -> 1301 bytes tests/test_did.py | 48 ++++++++++++++++++ 5 files changed, 49 insertions(+), 1 deletion(-) rename pyfixest/{ => experimental}/data/df_het.csv (100%) create mode 100644 tests/__pycache__/test_did.cpython-310-pytest-7.3.1.pyc create mode 100644 tests/test_did.py diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index 893071aa..842a6375 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -30,7 +30,7 @@ jobs: r-version: '4.2.0' - name: install fixest and broom for testing - run: Rscript -e 'install.packages(c("fixest", "broom"), repos="https://cloud.r-project.org")' + run: Rscript -e 'install.packages(c("fixest", "broom", "did2s"), repos="https://cloud.r-project.org")' shell: bash diff --git a/pyfixest/experimental/__pycache__/did.cpython-310.pyc b/pyfixest/experimental/__pycache__/did.cpython-310.pyc index dda5ee5b41824e6219ca042c2d57d8bc2ef9a37b..f20a9d72bfa976c1ef9f1b184939c717e5bed038 100644 GIT binary patch delta 1862 zcmaJ?&u<$=6yCMH>$PLYPWmHmnxu1sDAWbXm~oP9^Nk~fW+EXgcgUafVLkBlXi 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Supports OLS, IV and Poisson regression and a range of inference procedures." 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zNE~brcaTktxc3^U`*Jznxt)q6@a4eVStO`V$zw+nEi5cU(;>JK0qZ866?e2p~O05a}y zmwVjj!5jY?YiH>gt9G{@4|mw|XovX#;)W1Gw7(2C#vk%{0eSf74Y_t8YqW88csaOs zBBY1QcsttQ?RbauWINuF9o`ZA(LM$H!N%d^WAxg3I0EdGXIG!LT~S^NrBB3VD`Z(i z30QT)nPw-v$X*IP%hd%9xM;HrCilQa?{)$dypSFsO#R+92YRntv06~S3a z83UoiqLj*XiY)|bFoA7es%JjU*O-4QEzUzZg?Rek)p?XnNran^R`ve~`>WwV+| z@vAJg=%Wv&KDf0F;IMrgll1r&!0hfc&H#dK5B2vyqMX1BdT+I^+fE*WOpj#Soz7%) zzJ$K0ShC&?T*okqBLtDw9#~gn_+29Y{i{=?| zQ6Xw+TwI7%U78HLL!Z5B+5TekR6Uo}cp)+|4b#(M0XoVet&DWxqdmf)nuXlK1c(8YxcU-$y0^c!g3thHn1@UQ*3| From b3a93cc0f155c27d10eaa582ba32deb4bcd0c8cf Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 29 Oct 2023 20:50:16 +0100 Subject: [PATCH 16/43] update news --- docs/news.md | 47 ++++++++++-------------------------- pyfixest/experimental/did.py | 16 ++++++------ 2 files changed, 21 insertions(+), 42 deletions(-) diff --git a/docs/news.md b/docs/news.md index 463eff4b..0195ea38 100644 --- a/docs/news.md +++ b/docs/news.md @@ -1,19 +1,19 @@ # News -<<<<<<< HEAD -## PyFixest 0.10.5 +## PyFixest `0.10.7` -+ Adds an experimental `event_study()` function with support for two-way fixed effects difference-in differences - and Gardner's two stage estimator to `pyfixest.experimental.did`. +- Adds basic support for event study estimation via two-way fixed effects and Gardner's two-stage "Did2s" approach. + This is a beta version and experimental. Further updates (i.e. proper event studies vs "only" ATTs) and a more flexible + did2s front end will follow in future releases. ```python %load_ext autoreload %autoreload 2 + from pyfixest.experimental.did import event_study -from pyfixest.summarize import summary +from pyfixest.summarize import etable import pandas as pd - -df_het = pd.read_csv("pyfixest/data/df_het.csv") +df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") fit_twfe = event_study( data = df_het, @@ -33,33 +33,13 @@ fit_did2s = event_study( estimator = "did2s" ) -summary([fit_twfe, fit_did2s]) - -# ### -# -# Estimation: TWFE -# Dep. var.: dep_var, Fixed effects: state+year -# Inference: CRV1 -# Observations: 46500 -# -# | Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % | -# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:| -# | zz00_treat | 2.135 | 0.044 | 48.803 | 0.000 | 2.049 | 2.220 | -# --- -# ### -# -# Estimation: DID2S -# Dep. var.: dep_var -# Inference: CRV1 (GMM) -# Observations: 46500 -# -# | Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % | -# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:| -# | zz00_treat | 2.152 | 0.048 | 45.208 | 0.000 | 2.059 | 2.246 | -# --- - +etable([fit_twfe, fit_did2s]) +# | Coefficient | est1 | est2 | +# |:--------------|:-----------------|:-----------------| +# | ATT | 2.135*** (0.044) | 2.152*** (0.048) | +# Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001 ``` -======= + ## PyFixest `0.10.6` - Adds an `etable()` function that outputs markdown, latex or a pd.DataFrame. @@ -72,7 +52,6 @@ summary([fit_twfe, fit_did2s]) - Implements a custom function to drop singleton fixed effects. - Additional small performance improvements. ->>>>>>> 0c3b1626897c515cb1efa80797f477854c1f739c ## PyFixest `0.10.3` diff --git a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index c80152f3..39c5b3a5 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -136,7 +136,7 @@ def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): raise ValueError(f"There must be at least one unit that is never treated.") # create a treatment variable - self._data["zz00_treat"] = (self._data[self._tname] >= self._data[self._gname]) * (self._data[self._gname] > 0) + self._data["ATT"] = (self._data[self._tname] >= self._data[self._gname]) * (self._data[self._gname] > 0) @abstractmethod @@ -183,9 +183,9 @@ def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): self._estimator = "twfe" if self._xfml is not None: - self._fml = f"{yname} ~ zz00_treat + {xfml} | {idname} + {tname}" + self._fml = f"{yname} ~ ATT + {xfml} | {idname} + {tname}" else: - self._fml = f"{yname} ~ zz00_treat | {idname} + {tname}" + self._fml = f"{yname} ~ ATT | {idname} + {tname}" def estimate(self): @@ -242,10 +242,10 @@ def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): if self._xfml is not None: self._fml1 = f"{yname} ~ {xfml} | {idname} + {tname}" - self._fml2 = f"{yname} ~ 0 + zz00_treat + {xfml}" + self._fml2 = f"{yname} ~ 0 + ATT + {xfml}" else: self._fml1 = f"{yname} ~ 0 | {idname} + {tname}" - self._fml2 = f"{yname} ~ 0 + zz00_treat" + self._fml2 = f"{yname} ~ 0 + ATT" def estimate(self, fml_dict = None, treatment = None): @@ -262,7 +262,7 @@ def estimate(self, fml_dict = None, treatment = None): if treatment is not None: _not_yet_treated_data = _data[_data[treatment] == False] else: - _not_yet_treated_data = _data[_data["zz00_treat"] == False] + _not_yet_treated_data = _data[_data["ATT"] == False] _yname = self._yname @@ -295,13 +295,13 @@ def vcov(self): self._G = clustid.nunique() fml_group_time = f"~C({self._idname}) + C({self._tname})" # add covariates - fml_treatment_vars = "~0+zz00_treat" # add covariates + fml_treatment_vars = "~0+ATT" # add covariates X1 = model_matrix(fml_group_time, _data, output = "sparse") X2 = model_matrix(fml_treatment_vars, _data, output = "sparse") X10 = X1.copy().tocsr() - treated_rows = np.where(_data["zz00_treat"], 0, 1) + treated_rows = np.where(_data["ATT"], 0, 1) X10 = X10.multiply(treated_rows[:, None]) X10X10 = X10.T.dot(X10) From 09da8e197949c81efa2c628493c34cde6a7b010c Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 29 Oct 2023 20:58:35 +0100 Subject: [PATCH 17/43] update starting message --- pyfixest/experimental/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyfixest/experimental/__init__.py b/pyfixest/experimental/__init__.py index 0f849d36..52b4c289 100644 --- a/pyfixest/experimental/__init__.py +++ b/pyfixest/experimental/__init__.py @@ -1,4 +1,4 @@ print(""" - You have loaded the 'experimental' module. Code in this module is not yet stable and + You have loaded the 'pyfixest.experimental' module. Code in this module is not yet stable and may change in the future. It may also not be sufficiently unit tested (if at all) or documented. """) \ No newline at end of file From 703157740779fd50d7db02addcd623c62beb98a3 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 29 Oct 2023 21:07:18 +0100 Subject: [PATCH 18/43] update readme --- readme.md | 32 ++------------------------------ 1 file changed, 2 insertions(+), 30 deletions(-) diff --git a/readme.md b/readme.md index 4c5afa6d..99db40bf 100644 --- a/readme.md +++ b/readme.md @@ -16,41 +16,13 @@ At the moment, `PyFixest` supports - Multiple Estimation Syntax - Several Robust and Cluster Robust Variance-Covariance Types - Wild Cluster Bootstrap Inference (via [wildboottest](https://github.com/s3alfisc/wildboottest)) +- Support for estimators of the "new" Difference-in-Difference literature is work in progress. `PyFixest` currently provides an + experimentla implementation of Gardner's Did2s estimtator (via the `pyfixest.experimental.did` module, only ATT estimation). ## Installation You can install the release version from `PyPi` by running `pip install pyfixest` or the development version from github. -## News - -`PyFixest` now supports Poisson regression! - -```python -from pyfixest.estimation import fepois -from pyfixest.utils import get_data - -pdata = get_data(model = "Fepois") -fitpois = fepois("Y~X1 | f1 + f2", vcov = {'CRV1':'group_id'}, data = pdata) - -fitpois.summary() - -# Model: Y~X1|f1+f2 -# ### -# -# Model: Poisson -# Dep. var.: Y -# Fixed effects: f1+f2 -# Inference: CRV1 -# Observations: 997 -# -# | Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % | -# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:| -# | X1 | -0.019 | 0.050 | -0.370 | 0.711 | -0.117 | 0.080 | -# --- -# Deviance: 1067.853 -``` - - ## Quickstart ```python From 7398f5ed2f6da449b583a62a7f937b875cf09d04 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Mon, 30 Oct 2023 20:08:05 +0100 Subject: [PATCH 19/43] update pyproject.toml --- poetry.lock | 248 ++++++++++++++++++++++++------------------------- pyproject.toml | 6 +- 2 files changed, 125 insertions(+), 129 deletions(-) diff --git a/poetry.lock b/poetry.lock index 3bd3b880..b48a1e87 100644 --- a/poetry.lock +++ b/poetry.lock @@ -272,19 +272,19 @@ files = [ [[package]] name = "cleo" -version = "2.0.1" +version = "2.1.0" description = "Cleo allows you to create beautiful and testable command-line interfaces." category = "main" optional = true python-versions = ">=3.7,<4.0" files = [ - {file = "cleo-2.0.1-py3-none-any.whl", 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b/pyproject.toml index 17fa3b71..1aed16b4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,9 @@ [tool.poetry] name = "pyfixest" -<<<<<<< HEAD version = "0.10.7" -======= -version = "0.10.6" ->>>>>>> 0c3b1626897c515cb1efa80797f477854c1f739c -description = "(Not so) experimental draft package for high dimensional fixed effect estimation. Supports OLS, IV and Poisson regression and a range of inference procedures." +description = "Fast high dimensional fixed effect estimation following syntax of the fixest R package. Supports OLS, IV and Poisson regression and a range of inference procedures. Additionally, experimentally supports (some of) the regression based new Difference-in-Differences Estimators (Did2s)." authors = ["Alexander Fischer ", "Styfen Schär"] license = "MIT" readme = "readme.md" From 3e5d134748005bc2e96b761b0e253cf2cf72b120 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Mon, 30 Oct 2023 20:08:37 +0100 Subject: [PATCH 20/43] ci tests on mac as fixest already compiled --- .github/workflows/ci-tests.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index 842a6375..348db68d 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -16,7 +16,7 @@ jobs: strategy: fail-fast: false matrix: - os: ["ubuntu-latest"] + os: ["mac-latest"] python-version: ["3.8", "3.9", "3.10", "3.11"] From edd9640e2d5b429d9a5cf9ca35271103b90f517c Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Mon, 30 Oct 2023 21:04:42 +0100 Subject: [PATCH 21/43] test on windows --- .github/workflows/ci-tests.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index 348db68d..a6f4a81e 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -16,7 +16,7 @@ jobs: strategy: fail-fast: false matrix: - os: ["mac-latest"] + os: ["windows-latest"] python-version: ["3.8", "3.9", "3.10", "3.11"] From 79ad27c8bc101cebc914aa5658b839aa3feee0d2 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Mon, 30 Oct 2023 21:34:24 +0100 Subject: [PATCH 22/43] back to testing on linux --- .github/workflows/ci-tests.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index a6f4a81e..842a6375 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -16,7 +16,7 @@ jobs: strategy: fail-fast: false matrix: - os: ["windows-latest"] + os: ["ubuntu-latest"] python-version: ["3.8", "3.9", "3.10", "3.11"] From 1ec2f7d63c74db5ba729235ca303d407993f87eb Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Mon, 30 Oct 2023 21:35:57 +0100 Subject: [PATCH 23/43] first install python, then R deps --- .github/workflows/ci-tests.yaml | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index 842a6375..1f49d7c1 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -21,20 +21,8 @@ jobs: steps: - - name: Checkout source - uses: actions/checkout@v2 - - - name: Setup R - uses: r-lib/actions/setup-r@v2 - with: - r-version: '4.2.0' - - name: install fixest and broom for testing - run: Rscript -e 'install.packages(c("fixest", "broom", "did2s"), repos="https://cloud.r-project.org")' - shell: bash - - - - name: Setup python + - name: Setup python uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} @@ -44,9 +32,23 @@ jobs: run: | pip install -e . pip install -r requirements_test_ci.txt + - name: set numba parallel flags run: | echo "NUMBA_NUM_THREADS=1" >> $GITHUB_ENV + + - name: Checkout source + uses: actions/checkout@v2 + + - name: Setup R + uses: r-lib/actions/setup-r@v2 + with: + r-version: '4.2.0' + + - name: install fixest and broom for testing + run: Rscript -e 'install.packages(c("fixest", "broom", "did2s"), repos="https://cloud.r-project.org")' + shell: bash + - name: Run tests run: pytest tests/test_errors.py tests/test_formulas.py tests/test_plots.py tests/test_poisson.py tests/test_predict_resid_fixef.py tests/test_ses.py tests/test_ssc.py tests/test_vs_fixest.py - name: Upload coverage reports to Codecov From 284dcaa006528f086bbfaecb0bd0668180402493 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Mon, 30 Oct 2023 21:38:46 +0100 Subject: [PATCH 24/43] fix yaml syntax --- .github/workflows/ci-tests.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index 1f49d7c1..9032a891 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -22,7 +22,7 @@ jobs: steps: - - name: Setup python + - name: Setup python uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} From 04e414013c91b7c9f9d3a72ef912435b613c9133 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Mon, 30 Oct 2023 21:43:44 +0100 Subject: [PATCH 25/43] yaml is too hard --- .github/workflows/ci-tests.yaml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index 9032a891..481b0830 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -21,6 +21,8 @@ jobs: steps: + - name: Checkout source + uses: actions/checkout@v2 - name: Setup python uses: actions/setup-python@v2 @@ -37,9 +39,6 @@ jobs: run: | echo "NUMBA_NUM_THREADS=1" >> $GITHUB_ENV - - name: Checkout source - uses: actions/checkout@v2 - - name: Setup R uses: r-lib/actions/setup-r@v2 with: From c3126e44ae68ae1fe31b8cc6065fad2e397742db Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Wed, 1 Nov 2023 16:41:45 +0100 Subject: [PATCH 26/43] some work towards a more flexible API --- pyfixest/experimental/did.py | 140 ++++++++++++++++++++++------------- 1 file changed, 88 insertions(+), 52 deletions(-) diff --git a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index 39c5b3a5..167ceb0a 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -52,7 +52,7 @@ def event_study(data, yname, idname, tname, gname, xfml = None, estimator = "twf if estimator == "did2s": did2s = DID2S(data = data, yname = yname, idname=idname, tname = tname, gname = gname, xfml = xfml, att = att, cluster = cluster) - fit = did2s.estimate() + fit, did2s._first_u, did2s._second_u = did2s.estimate() vcov = did2s.vcov() fit._vcov = vcov fit._vcov_type = "CRV1" @@ -248,91 +248,127 @@ def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): self._fml2 = f"{yname} ~ 0 + ATT" - def estimate(self, fml_dict = None, treatment = None): + def estimate(self): + + """ + Args: + data (pd.DataFrame): The DataFrame containing all variables. + yname (str): The name of the dependent variable. + _first_stage (str): The formula for the first stage. + _second_stage (str): The formula for the second stage. + treatment (str): The name of the treatment variable. + Returns: + tba + """ + + return _did2s_estimate(data, yname, _first_stage, _second_stage, treatment) # returns triple Feols, first_u, second_u + + + def vcov(self): + + return _did2s_vcov(_data = _data, _first_stage = _first_stage, _second_stage = _second_stage, _first_u = _first_u, _second_u = _second_u) - if fml_dict is not None: - self._fml1 = fml_dict["fml1"] - self._fml2 = fml_dict["fml2"] - _fml1 = self._fml1 - _fml2 = self._fml2 - _data = self._data +def _did2s_estimate(_data: pd.DataFrame, yname : str, _first_stage: str, _second_stage: str, treatment: str): + + """ + Args: + data (pd.DataFrame): The DataFrame containing all variables. + yname (str): The name of the dependent variable. + _first_stage (str): The formula for the first stage. + _second_stage (str): The formula for the second stage. + treatment (str): The name of the treatment variable. + Returns: + A fitted model object of class feols and the first and second stage residuals. + """ + _first_stage = _first_stage.replace(" ", "") + _second_stage = _second_stage.replace(" ", "") + assert _first_stage[0] == "~", "First stage must start with ~" + assert _second_stage[0] == "~", "Second stage must start with ~" + + _first_stage_full = f"{yname} {_fml1}" + _second_stage_full = f"{yname}_hat {_fml2}" if treatment is not None: - _not_yet_treated_data = _data[_data[treatment] == False] + _not_yet_treated_data = data[data[treatment] == False] else: - _not_yet_treated_data = _data[_data["ATT"] == False] - - _yname = self._yname + _not_yet_treated_data = data[data["ATT"] == False] # estimate first stage - fit1 = feols(fml = _fml1, data = _not_yet_treated_data) + fit1 = feols(fml = _first_stage_full, data = _not_yet_treated_data) # obtain estimated fixed effects fit1.fixef() # demean data Y_hat = fit1.predict(newdata = _data) - self._first_u = _data[f"{_yname}"].to_numpy().flatten() - Y_hat - _data[f"{_yname}"] = self._first_u + _first_u = _data[f"{_yname}"].to_numpy().flatten() - Y_hat + _data[f"{_yname}_hat"] = self._first_u - fit2 = feols(_fml2, data = _data) - self._second_u = fit2.resid() + fit2 = feols(_second_stage_full, data = _data) + _second_u = fit2.resid() - return fit2 + return fit2, _first_u, _second_u - def vcov(self): - _data = self._data - _first_u = self._first_u - _second_u = self._second_u - _cluster = self._cluster +def _did2s_vcov(_data: pd.DataFrame, _first_stage: str, _second_stage: str, _first_u: np.ndarray, _second_u: np.ndarray, _cluster : str): - cluster_col = _data[_cluster] - _, clustid = pd.factorize(cluster_col) + """ + Compute a variance covariance matrix for Gardner's 2-stage Difference-in-Differences Estimator. + Args: + data (pd.DataFrame): - self._G = clustid.nunique() + """ + + cluster_col = _data[_cluster] + _, clustid = pd.factorize(cluster_col) + + _G = clustid.nunique() # actually not used here, neither in did2s - fml_group_time = f"~C({self._idname}) + C({self._tname})" # add covariates - fml_treatment_vars = "~0+ATT" # add covariates + X1 = model_matrix(_first_stage, _data, output = "sparse") # here: from potential two part formula to one - part formula + X2 = model_matrix(_second_stage, _data, output = "sparse") - X1 = model_matrix(fml_group_time, _data, output = "sparse") - X2 = model_matrix(fml_treatment_vars, _data, output = "sparse") + X10 = X1.copy().tocsr() + treated_rows = np.where(_data["ATT"], 0, 1) + X10 = X10.multiply(treated_rows[:, None]) - X10 = X1.copy().tocsr() - treated_rows = np.where(_data["ATT"], 0, 1) - X10 = X10.multiply(treated_rows[:, None]) + X10X10 = X10.T.dot(X10) + X2X1 = X2.T.dot(X1) + X2X2 = X2.T.dot(X2) - X10X10 = X10.T.dot(X10) - X2X1 = X2.T.dot(X1) - X2X2 = X2.T.dot(X2) + V = spsolve(X10X10, X2X1.T).T - V = spsolve(X10X10, X2X1.T).T + k = X2.shape[1] + vcov = np.zeros((k, k)) - k = X2.shape[1] - vcov = np.zeros((k, k)) + X10 = X10.tocsr() + X2 = X2.tocsr() - X10 = X10.tocsr() - X2 = X2.tocsr() + for (_,g,) in enumerate(clustid): - for (_,g,) in enumerate(clustid): + X10g = X10[cluster_col == g, :] + X2g = X2[cluster_col == g, :] + first_u_g = _first_u[cluster_col == g] + second_u_g = _second_u[cluster_col == g] - X10g = X10[cluster_col == g, :] - X2g = X2[cluster_col == g, :] - first_u_g = _first_u[cluster_col == g] - second_u_g = _second_u[cluster_col == g] + W_g = X2g.T.dot(second_u_g) - V @ X10g.T.dot(first_u_g) - W_g = X2g.T.dot(second_u_g) - V @ X10g.T.dot(first_u_g) + score = spsolve(X2X2, W_g) + cov_g = score.dot(score.T) - score = spsolve(X2X2, W_g) - cov_g = score.dot(score.T) + vcov += cov_g - vcov += cov_g + return vcov - self._vcov = vcov - return self._vcov +def did2s(data: pd.DataFrame, yname : str, _first_stage: str, _second_stage: str, treatment: str, cluster: str): + + fit, first_u, second_u = _did2s_estimate(data, yname, first_stage, second_stage, treatment) + vcov = _did2s_vcov(data, first_stage, second_stage, first_u, second_u, cluster) + fit._vcov = vcov + + return fit From b434eb50a0f6d838febcef2fbffb6ddaacb84e5e Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Wed, 1 Nov 2023 18:55:41 +0100 Subject: [PATCH 27/43] get did2s function prototype to run --- pyfixest/experimental/did.py | 50 +++++++++++++++++++++++------------- 1 file changed, 32 insertions(+), 18 deletions(-) diff --git a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index 167ceb0a..19295cea 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -270,7 +270,7 @@ def vcov(self): -def _did2s_estimate(_data: pd.DataFrame, yname : str, _first_stage: str, _second_stage: str, treatment: str): +def _did2s_estimate(data: pd.DataFrame, yname : str, _first_stage: str, _second_stage: str, treatment: str): """ Args: @@ -282,13 +282,9 @@ def _did2s_estimate(_data: pd.DataFrame, yname : str, _first_stage: str, _second Returns: A fitted model object of class feols and the first and second stage residuals. """ - _first_stage = _first_stage.replace(" ", "") - _second_stage = _second_stage.replace(" ", "") - assert _first_stage[0] == "~", "First stage must start with ~" - assert _second_stage[0] == "~", "Second stage must start with ~" - _first_stage_full = f"{yname} {_fml1}" - _second_stage_full = f"{yname}_hat {_fml2}" + _first_stage_full = f"{yname} {_first_stage}" + _second_stage_full = f"{yname}_hat {_second_stage}" if treatment is not None: _not_yet_treated_data = data[data[treatment] == False] @@ -296,23 +292,23 @@ def _did2s_estimate(_data: pd.DataFrame, yname : str, _first_stage: str, _second _not_yet_treated_data = data[data["ATT"] == False] # estimate first stage - fit1 = feols(fml = _first_stage_full, data = _not_yet_treated_data) + fit1 = feols(fml = _first_stage_full, data = _not_yet_treated_data, vcov = "iid") # iid as it might be faster than CRV # obtain estimated fixed effects fit1.fixef() # demean data - Y_hat = fit1.predict(newdata = _data) - _first_u = _data[f"{_yname}"].to_numpy().flatten() - Y_hat - _data[f"{_yname}_hat"] = self._first_u + Y_hat = fit1.predict(newdata =data) + _first_u = data[f"{yname}"].to_numpy().flatten() - Y_hat + data[f"{yname}_hat"] = _first_u - fit2 = feols(_second_stage_full, data = _data) + fit2 = feols(_second_stage_full, data =data, vcov = "iid") _second_u = fit2.resid() return fit2, _first_u, _second_u -def _did2s_vcov(_data: pd.DataFrame, _first_stage: str, _second_stage: str, _first_u: np.ndarray, _second_u: np.ndarray, _cluster : str): +def _did2s_vcov(_data: pd.DataFrame, first_stage: str, second_stage: str, treatment: str, _first_u: np.ndarray, _second_u: np.ndarray, _cluster : str): """ Compute a variance covariance matrix for Gardner's 2-stage Difference-in-Differences Estimator. @@ -326,11 +322,19 @@ def _did2s_vcov(_data: pd.DataFrame, _first_stage: str, _second_stage: str, _fir _G = clustid.nunique() # actually not used here, neither in did2s - X1 = model_matrix(_first_stage, _data, output = "sparse") # here: from potential two part formula to one - part formula - X2 = model_matrix(_second_stage, _data, output = "sparse") + + # some formula parsing to get the correct formula for the first and second stage model matrix + first_stage_x, first_stage_fe = first_stage.split("|") + first_stage_fe = [f"C({i})" for i in first_stage_fe.split("+")] + first_stage_fe = "+".join(first_stage_fe) + first_stage = f"{first_stage_x}+{first_stage_fe}" + + + X1 = model_matrix(first_stage, _data, output = "sparse") # here: from potential two part formula to one - part formula + X2 = model_matrix(second_stage, _data, output = "sparse") X10 = X1.copy().tocsr() - treated_rows = np.where(_data["ATT"], 0, 1) + treated_rows = np.where(_data[treatment], 0, 1) X10 = X10.multiply(treated_rows[:, None]) X10X10 = X10.T.dot(X10) @@ -362,12 +366,22 @@ def _did2s_vcov(_data: pd.DataFrame, _first_stage: str, _second_stage: str, _fir return vcov -def did2s(data: pd.DataFrame, yname : str, _first_stage: str, _second_stage: str, treatment: str, cluster: str): +def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, treatment: str, cluster: str): + + first_stage = first_stage.replace(" ", "") + second_stage = second_stage.replace(" ", "") + assert first_stage[0] == "~", "First stage must start with ~" + assert second_stage[0] == "~", "Second stage must start with ~" fit, first_u, second_u = _did2s_estimate(data, yname, first_stage, second_stage, treatment) - vcov = _did2s_vcov(data, first_stage, second_stage, first_u, second_u, cluster) + vcov = _did2s_vcov(data, first_stage, second_stage, treatment, first_u, second_u, cluster) fit._vcov = vcov + fit._vcov_type = "CRV1" + fit._vcov_type_detail = "CRV1 (GMM)" + #fit._G = did2s._G + fit._method = "did2s" + return fit From 2abae06b5cf665f20f7390105473070c8c2603c8 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Wed, 1 Nov 2023 19:22:33 +0100 Subject: [PATCH 28/43] event_study() and did2s() both run through --- pyfixest/experimental/did.py | 52 ++++++++++++++++++++++------------ pyfixest/experimental/did2s.py | 0 2 files changed, 34 insertions(+), 18 deletions(-) create mode 100644 pyfixest/experimental/did2s.py diff --git a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index 19295cea..df5a52b3 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -53,11 +53,11 @@ def event_study(data, yname, idname, tname, gname, xfml = None, estimator = "twf did2s = DID2S(data = data, yname = yname, idname=idname, tname = tname, gname = gname, xfml = xfml, att = att, cluster = cluster) fit, did2s._first_u, did2s._second_u = did2s.estimate() - vcov = did2s.vcov() + vcov, _G = did2s.vcov() fit._vcov = vcov + fit._G = _G fit._vcov_type = "CRV1" fit._vcov_type_detail = "CRV1 (GMM)" - fit._G = did2s._G fit._method = "did2s" elif estimator == "twfe": @@ -241,12 +241,11 @@ def __init__(self, data, yname, idname, tname, gname, xfml, att, cluster): self._estimator = "did2s" if self._xfml is not None: - self._fml1 = f"{yname} ~ {xfml} | {idname} + {tname}" - self._fml2 = f"{yname} ~ 0 + ATT + {xfml}" + self._fml1 = f" ~ {xfml} | {idname} + {tname}" + self._fml2 = f" ~ 0 + ATT + {xfml}" else: - self._fml1 = f"{yname} ~ 0 | {idname} + {tname}" - self._fml2 = f"{yname} ~ 0 + ATT" - + self._fml1 = f" ~ 0 | {idname} + {tname}" + self._fml2 = f" ~ 0 + ATT" def estimate(self): @@ -261,12 +260,26 @@ def estimate(self): tba """ - return _did2s_estimate(data, yname, _first_stage, _second_stage, treatment) # returns triple Feols, first_u, second_u + return _did2s_estimate( + data = self._data, + yname = self._yname, + _first_stage = self._fml1, + _second_stage = self._fml2, + treatment = "ATT" + ) # returns triple Feols, first_u, second_u def vcov(self): - return _did2s_vcov(_data = _data, _first_stage = _first_stage, _second_stage = _second_stage, _first_u = _first_u, _second_u = _second_u) + return _did2s_vcov( + data = self._data, + first_stage = self._fml1, + second_stage = self._fml2, + treatment = "ATT", + first_u = self._first_u, + second_u = self._second_u, + cluster = self._cluster + ) @@ -308,7 +321,7 @@ def _did2s_estimate(data: pd.DataFrame, yname : str, _first_stage: str, _second_ return fit2, _first_u, _second_u -def _did2s_vcov(_data: pd.DataFrame, first_stage: str, second_stage: str, treatment: str, _first_u: np.ndarray, _second_u: np.ndarray, _cluster : str): +def _did2s_vcov(data: pd.DataFrame, first_stage: str, second_stage: str, treatment: str, first_u: np.ndarray, second_u: np.ndarray, cluster : str): """ Compute a variance covariance matrix for Gardner's 2-stage Difference-in-Differences Estimator. @@ -317,7 +330,7 @@ def _did2s_vcov(_data: pd.DataFrame, first_stage: str, second_stage: str, treatm """ - cluster_col = _data[_cluster] + cluster_col = data[cluster] _, clustid = pd.factorize(cluster_col) _G = clustid.nunique() # actually not used here, neither in did2s @@ -330,11 +343,11 @@ def _did2s_vcov(_data: pd.DataFrame, first_stage: str, second_stage: str, treatm first_stage = f"{first_stage_x}+{first_stage_fe}" - X1 = model_matrix(first_stage, _data, output = "sparse") # here: from potential two part formula to one - part formula - X2 = model_matrix(second_stage, _data, output = "sparse") + X1 = model_matrix(first_stage, data, output = "sparse") # here: from potential two part formula to one - part formula + X2 = model_matrix(second_stage, data, output = "sparse") X10 = X1.copy().tocsr() - treated_rows = np.where(_data[treatment], 0, 1) + treated_rows = np.where(data[treatment], 0, 1) X10 = X10.multiply(treated_rows[:, None]) X10X10 = X10.T.dot(X10) @@ -353,8 +366,8 @@ def _did2s_vcov(_data: pd.DataFrame, first_stage: str, second_stage: str, treatm X10g = X10[cluster_col == g, :] X2g = X2[cluster_col == g, :] - first_u_g = _first_u[cluster_col == g] - second_u_g = _second_u[cluster_col == g] + first_u_g = first_u[cluster_col == g] + second_u_g = second_u[cluster_col == g] W_g = X2g.T.dot(second_u_g) - V @ X10g.T.dot(first_u_g) @@ -363,7 +376,7 @@ def _did2s_vcov(_data: pd.DataFrame, first_stage: str, second_stage: str, treatm vcov += cov_g - return vcov + return vcov, _G def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, treatment: str, cluster: str): @@ -374,14 +387,17 @@ def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, assert second_stage[0] == "~", "Second stage must start with ~" fit, first_u, second_u = _did2s_estimate(data, yname, first_stage, second_stage, treatment) - vcov = _did2s_vcov(data, first_stage, second_stage, treatment, first_u, second_u, cluster) + vcov, _G = _did2s_vcov(data, first_stage, second_stage, treatment, first_u, second_u, cluster) fit._vcov = vcov + fit._G = _G + fit.get_inference() # update inference with correct vcov matrix fit._vcov_type = "CRV1" fit._vcov_type_detail = "CRV1 (GMM)" #fit._G = did2s._G fit._method = "did2s" + return fit diff --git a/pyfixest/experimental/did2s.py b/pyfixest/experimental/did2s.py new file mode 100644 index 00000000..e69de29b From 56cc47616c9166058abeee5e1ce3090fa17821af Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Wed, 1 Nov 2023 19:29:18 +0100 Subject: [PATCH 29/43] add examples to did2s() and event_study() --- pyfixest/experimental/did.py | 51 ++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index df5a52b3..de9f1c68 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -31,6 +31,29 @@ def event_study(data, yname, idname, tname, gname, xfml = None, estimator = "twf canonical event study design with all leads and lags. Default is True. Returns: A fitted model object of class feols. + Examples: + >>> from pyfixest.experimental.did import event_study, did2s + >>> from pyfixest.estimation import feols + >>> from pyfixest.summarize import etable, summary + >>> import pandas as pd + >>> import numpy as np + >>> df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") + >>> fit_twfe = event_study( + >>> data = df_het, + >>> yname = "dep_var", + >>> idname= "state", + >>> tname = "year", + >>> gname = "g", + >>> estimator = "twfe" + >>> ) + >>> fit_did2s = event_study( + >>> data = df_het, + >>> yname = "dep_var", + >>> idname= "state", + >>> tname = "year", + >>> gname = "g", + >>> estimator = "did2s" + >>> ) """ assert isinstance(data, pd.DataFrame), "data must be a pandas DataFrame" @@ -381,6 +404,34 @@ def _did2s_vcov(data: pd.DataFrame, first_stage: str, second_stage: str, treatme def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, treatment: str, cluster: str): + """ + Estimate a Difference-in-Differences model using Gardner's two-step DID2S estimator. + Args: + data (pd.DataFrame): The DataFrame containing all variables. + yname (str): The name of the dependent variable. + first_stage (str): The formula for the first stage. Must start with '~'. + second_stage (str): The formula for the second stage. Must start with '~'. + treatment (str): The name of the treatment variable. + cluster (str): The name of the cluster variable. + Returns: + A fitted model object of class feols. + Examples: + >>> from pyfixest.experimental.did import event_study, did2s + >>> from pyfixest.estimation import feols + >>> from pyfixest.summarize import etable, summary + >>> import pandas as pd + >>> import numpy as np + >>> df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") + >>> fit = did2s( + >>> yname = "dep_var", + >>> first_stage = "~ X | state + year", + >>> second_stage = "~ 0 + treat", + >>> treatment = "treat", + >>> data = df_het, + >>> cluster = "state", + >>> ) + """ + first_stage = first_stage.replace(" ", "") second_stage = second_stage.replace(" ", "") assert first_stage[0] == "~", "First stage must start with ~" From a7e9543d1a12a84d6af5bd9c16d012de6ba09084 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 4 Nov 2023 12:33:14 +0100 Subject: [PATCH 30/43] more tests for did2s --- tests/test_did.py | 41 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/tests/test_did.py b/tests/test_did.py index 44fe7fcb..364e4b19 100644 --- a/tests/test_did.py +++ b/tests/test_did.py @@ -1,4 +1,5 @@ from pyfixest.experimental.did import event_study +from pyfixest.experimental.did import did2s as did2s_pyfixest import pandas as pd import numpy as np @@ -15,6 +16,10 @@ def test_event_study(): + """ + Test hte event_study() function. + """ + df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") fit_did2s = event_study( @@ -32,6 +37,7 @@ def test_event_study(): second_stage = ro.Formula("~ i(treat, ref = FALSE)"), treatment = "treat", cluster_var = "state" ) + did2s_df = broom.tidy_fixest(fit_did2s_r, conf_int=ro.BoolVector([True])) did2s_df = pd.DataFrame(did2s_df).T @@ -44,5 +50,40 @@ def test_event_study(): ) +def test_did2s(): + + """ + Test the did2s() function. + """ + + df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") + + fit_did2s = did2s_pyfixest( + data = df_het, + yname = "dep_var", + first_stage = "~ 0 | state + year", + second_stage = "~ 0 + treat", + treatment = "treat", + cluster = "state", + ) + + fit_did2s_r = did2s.did2s( + data = df_het, + yname = "dep_var", first_stage = ro.Formula("~ 0 | state + year"), + second_stage = ro.Formula("~ i(treat, ref = FALSE)"), treatment = "treat", + cluster_var = "state" + ) + + did2s_df = broom.tidy_fixest(fit_did2s_r, conf_int=ro.BoolVector([True])) + did2s_df = pd.DataFrame(did2s_df).T + + if True: + np.testing.assert_allclose( + fit_did2s.coef(), stats.coef(fit_did2s_r) + ) + np.testing.assert_allclose( + fit_did2s.se(), float(did2s_df[2]) + ) + From 4cebcd068691722b95ee89e554794009c63eefba Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 4 Nov 2023 13:56:25 +0100 Subject: [PATCH 31/43] pass tests --- pyfixest/FixestMulti.py | 11 ++- pyfixest/estimation.py | 16 ++-- pyfixest/feiv.py | 2 - pyfixest/model_matrix_fixest.py | 146 +++++++++++++++----------------- pyfixest/visualize.py | 5 +- tests/test_did.py | 2 +- tests/test_i.py | 23 +++++ tests/test_plots.py | 2 +- 8 files changed, 117 insertions(+), 90 deletions(-) create mode 100644 tests/test_i.py diff --git a/pyfixest/FixestMulti.py b/pyfixest/FixestMulti.py index a69c553d..20a483a8 100644 --- a/pyfixest/FixestMulti.py +++ b/pyfixest/FixestMulti.py @@ -56,6 +56,8 @@ def _prepare_estimation( vcov: Union[None, str, Dict[str, str]] = None, ssc: Dict[str, str] = {}, fixef_rm: str = "none", + i_ref1: Optional[Union[List, str]] = None, + i_ref2: Optional[Union[List, str]] = None, ) -> None: """ Utility function to prepare estimation via the `feols()` or `fepois()` methods. The function is called by both methods. @@ -68,6 +70,8 @@ def _prepare_estimation( ssc (Dict[str, str], optional): A dictionary specifying the type of standard errors to use for inference. See `feols()` or `fepois()`. fixef_rm (str, optional): A string specifying whether singleton fixed effects should be dropped. Options are "none" (default) and "singleton". If "singleton", singleton fixed effects are dropped. + i_ref1 (Optional[Union[List, str]], optional): A list or string specifying the reference category for the first interaction variable. + i_ref2 (Optional[Union[List, str]], optional): A list or string specifying the reference category for the second interaction variable. Returns: None @@ -98,6 +102,8 @@ def _prepare_estimation( self._ssc_dict = ssc self._drop_singletons = _drop_singletons(fixef_rm) self._fixef_keys = list(self._fml_dict.keys()) + self._i_ref1 = i_ref1 + self._i_ref2 = i_ref2 def _estimate_all_models( self, @@ -133,6 +139,8 @@ def _estimate_all_models( _method = self._method _drop_singletons = self._drop_singletons _ssc_dict = self._ssc_dict + _i_ref1 = self._i_ref1 + _i_ref2 = self._i_ref2 for _, fval in enumerate(fixef_keys): dict2fe = _fml_dict.get(fval) @@ -167,7 +175,7 @@ def _estimate_all_models( na_index_str, _icovars, X_is_empty - ) = model_matrix_fixest(fml=fml, data=_data) + ) = model_matrix_fixest(fml=fml, data=_data, i_ref1 = _i_ref1, i_ref2 = _i_ref2) weights = np.ones((Y.shape[0], 1)) @@ -624,6 +632,7 @@ def get_fml( str: The formula string for the regression. """ + fml = f"{depvar} ~ {covar}" if endogvars is not None: diff --git a/pyfixest/estimation.py b/pyfixest/estimation.py index 04dbe15f..b11880e6 100644 --- a/pyfixest/estimation.py +++ b/pyfixest/estimation.py @@ -13,6 +13,8 @@ def feols( ssc=ssc(), fixef_rm: str = "none", collin_tol: float = 1e-10, + i_ref1: Optional[Union[list, str]] = None, + i_ref2: Optional[Union[list, str]] = None ) -> Union[Feols, FixestMulti]: """ @@ -66,6 +68,9 @@ def feols( via the diagonal cholesky decomposition of the correlation matrix of the variables. If the tolerance is higher, more variables will be dropped. + i_ref1 (Optional[Union[list, str]]): A list of strings or a string specifying the reference category for the first set of categorical variables in the formula, interacted via "i()". + i_ref2 (Optional[Union[list, str]]): A list of strings or a string specifying the reference category for the second set of categorical variables in the formula, interacted via "i()". + Returns: An instance of the `Feols` class or an instance of class `FixestMulti` if multiple models are specified via the `fml` argument. @@ -126,7 +131,7 @@ def feols( _estimation_input_checks(fml, data, vcov, ssc, fixef_rm, collin_tol) fixest = FixestMulti(data=data) - fixest._prepare_estimation("feols", fml, vcov, ssc, fixef_rm) + fixest._prepare_estimation("feols", fml, vcov, ssc, fixef_rm, i_ref1, i_ref2) # demean all models: based on fixed effects x split x missing value combinations fixest._estimate_all_models(vcov, fixest._fixef_keys, collin_tol=collin_tol) @@ -146,6 +151,8 @@ def fepois( iwls_tol: float = 1e-08, iwls_maxiter: int = 25, collin_tol: float = 1e-10, + i_ref1: Optional[Union[list, str]] = None, + i_ref2: Optional[Union[list, str]] = None ) -> Union[Fepois, FixestMulti]: """ # fepois @@ -197,6 +204,8 @@ def fepois( collin_tol (float): tolerance for collinearity check. 1e-06 by default. If collinear variables are detected, they will be dropped from the model. The performed check is via the diagonal cholesky decomposition of the correlation matrix of the variables. If the tolerance is higher, more variables will be dropped. + i_ref1 (Optional[Union[list, str]]): A list of strings or a string specifying the reference category for the first set of categorical variables in the formula, interacted via "i()". + i_ref2 (Optional[Union[list, str]]): A list of strings or a string specifying the reference category for the second set of categorical variables in the formula, interacted via "i()". Returns: An instance of the `Fepois` class or an instance of class `FixestMulti` if multiple models are specified via the `fml` argument. @@ -247,10 +256,7 @@ def fepois( fixest = FixestMulti(data=data) - fixest._prepare_estimation( - estimation="fepois", fml=fml, vcov=vcov, ssc=ssc, fixef_rm=fixef_rm - ) - + fixest._prepare_estimation("fepois", fml, vcov, ssc, fixef_rm, i_ref1, i_ref2) if fixest._is_iv: raise NotImplementedError( "IV Estimation is not supported for Poisson Regression" diff --git a/pyfixest/feiv.py b/pyfixest/feiv.py index 638ddaa5..e23964b9 100644 --- a/pyfixest/feiv.py +++ b/pyfixest/feiv.py @@ -38,8 +38,6 @@ def __init__( if len(Z.shape) != 2: raise ValueError("Z must be a two-dimensional array") - # import pdb; pdb.set_trace() - if self._collin_index is not None: self._Z = Z[:, ~self._collin_index] else: diff --git a/pyfixest/model_matrix_fixest.py b/pyfixest/model_matrix_fixest.py index 8ebb0fa2..a26dc4a1 100644 --- a/pyfixest/model_matrix_fixest.py +++ b/pyfixest/model_matrix_fixest.py @@ -1,12 +1,12 @@ from formulaic import model_matrix import pandas as pd import re -from typing import Optional, Tuple, List +from typing import Optional, Tuple, List, Union import numpy as np def model_matrix_fixest( - fml: str, data: pd.DataFrame, weights: Optional[str] = None + fml: str, data: pd.DataFrame, weights: Optional[str] = None, i_ref1: Optional[Union[List, str, int]] = None, i_ref2: Optional[Union[List, str, int]] = None ) -> Tuple[ pd.DataFrame, # Y pd.DataFrame, # X @@ -29,6 +29,9 @@ def model_matrix_fixest( Args: fml (str): A two-sided formula string using fixest formula syntax. weights (str or None): Weights as a string if provided, or None if no weights, e.g., "weights". + data (pd.DataFrame): The input DataFrame containing the data. + i_ref1 (str or list): The reference level for the first variable in the i() syntax. + i_ref2 (str or list): The reference level for the second variable in the i() syntax. Returns: Tuple[ @@ -64,9 +67,10 @@ def model_matrix_fixest( fml = fml.replace(" ", "") _is_iv = _check_is_iv(fml) - _ivars = _find_ivars(fml) - _ivars = _deparse_ivars(_ivars) - _ivars, _drop_ref = _clean_ivars(_ivars, data) + _ivars = _find_ivars(fml)[0] + + #_ivars = _deparse_ivars(_ivars) + #_ivars, _drop_ref = _clean_ivars(_ivars, data) # step 1: deparse formula fml_parts = fml.split("|") @@ -76,9 +80,32 @@ def model_matrix_fixest( for x in covar.split("+"): is_ivar = _find_ivars(x) if is_ivar[1]: - covar = covar.replace(x, f"C({is_ivar[0][0]}):{is_ivar[0][1]}") + if len(_ivars) == 2: + interact_vars = f"C({_ivars[0]}):{_ivars[1]}" + elif len(_ivars) == 1: + interact_vars = f"C({_ivars[0]})" + else: + raise ValueError("Something went wrong with the i() syntax. Please report this issue to the package author via github.") + covar = covar.replace(x, interact_vars) break + # drop_ref + _ivar1 = _ivars[0] + + #_ivar2_is_discrete = data[_ivar2].dtype.name in ["category", "object"] + if i_ref1 is not None: + if len(_ivars) == 2: + _drop_ref = f"C({_ivar1})[T.{i_ref1}]:" + #else: + # pass + # #raise NotImplementedError("Interaction of two discrete variables is not yet implemented.") + + elif len(_ivars) == 1: + _drop_ref = f"C({_ivars})[T.{i_ref1}]" + + else: + _drop_ref = None + if len(fml_parts) == 3: fval, fml_iv = fml_parts[1], fml_parts[2] elif len(fml_parts) == 2: @@ -166,23 +193,23 @@ def model_matrix_fixest( # drop variables before collecting variable names + if isinstance(_drop_ref, list): + _drop_ref = _drop_ref[0] + if _ivars is not None: if _drop_ref is not None: + + columns_to_drop = [col for col in X.columns if _drop_ref in col] + if not X_is_empty: - X.drop(_drop_ref, axis=1, inplace = True) - if _is_iv: - Z.drop(_drop_ref, axis=1, inplace = True) + X.drop(columns_to_drop, axis=1, inplace = True) + if _is_iv: + Z.drop(columns_to_drop, axis=1, inplace = True) # drop reference level, if specified - if _ivars is not None: - x_names = X.columns.tolist() - _icovars = [ - s - for s in x_names - if s.startswith("C(" + _ivars[0]) and s.endswith(_ivars[1]) - ] - else: - _icovars = None + # ivars are needed for plotting of all interacted variables via iplot() + + _icovars = _get_icovars(_ivars, X) if fe is not None: fe.drop(na_index, axis=0, inplace = True) @@ -222,69 +249,13 @@ def _find_ivars(x): """ i_match = re.findall(r"i\((.*?)\)", x) + if i_match: - return [x.replace(" ", "") for x in i_match[0].split(",")], "i" + return i_match[0].split(","), "i" else: return x, None -def _deparse_ivars(x): - """ - Deparse the result of _find_ivars() into a dictionary. - Args: - x (list): A list of interaction variables. - Returns: - dict: A dictionary of interaction variables. Keys are the reference variables, values are the interaction variables. - If no reference variable is provided, the key is None. - """ - - if x[1] is not None: - ivars = dict() - # check for reference by searching for "=" - i_split = x[0][-1].split("=") - if len(i_split) > 1: - ref = x[0][-1].split("=")[1] - _ivars_list = x[0][:-1] - else: - ref = None - _ivars_list = x[0] - ivars[ref] = _ivars_list - else: - ivars = None - - return ivars - - -def _clean_ivars(ivars, data): - """ - Clean variables interacted via i(X1, X2, ref = a) syntax. - - Args: - ivars (list): The list of variables specified in the i() syntax. - data (pandas.DataFrame): The dataframe containing the data used for the model fitting. - Returns: - ivars (list): The list of variables specified in the i() syntax minus the reference level - drop_ref (str): The dropped reference level specified in the i() syntax. None if no level is dropped - """ - - if ivars is not None: - if list(ivars.keys())[0] is not None: - ref = list(ivars.keys())[0] - ivars = ivars[ref] - drop_ref = f"C({ivars[0]})[T.{ref}]:{ivars[1]}" - else: - ivars = ivars[None] - drop_ref = None - - # type checking for ivars variable - _check_ivars(data, ivars) - - else: - ivars = None - drop_ref = None - - return ivars, drop_ref - def _check_ivars(data, ivars): """ @@ -297,7 +268,7 @@ def _check_ivars(data, ivars): """ i0_type = data[ivars[0]].dtype - i1_type = data[ivars[1]].dtype + #i1_type = data[ivars[1]].dtype if not i0_type in ["category", "O"]: raise ValueError( f""" @@ -372,3 +343,24 @@ def _clean_fe(data: pd.DataFrame, fval: str) -> Tuple[pd.DataFrame, List[int]]: fe_na = fe_na[fe_na].index.tolist() return fe, fe_na + + +def _get_icovars(_ivars: List[str], X: pd.DataFrame) -> Optional[List[str]]: + """ + Get all interacted variables via i() syntax. Required for plotting of all interacted variables via iplot(). + Args: + _ivars (list): A list of interaction variables. + X (pd.DataFrame): The DataFrame containing the covariates. + Returns: + list: A list of interacted variables or None. + """ + if _ivars is not None: + x_names = X.columns.tolist() + if len(_ivars) == 2: + _icovars = [s for s in x_names if s.startswith("C(" + _ivars[0]) and s.endswith(_ivars[1])] + else: + _icovars = [s for s in x_names if s.startswith("C(" + _ivars[0]) and s.endswith("]")] + else: + _icovars = None + + return _icovars \ No newline at end of file diff --git a/pyfixest/visualize.py b/pyfixest/visualize.py index 7f92cdab..64ce7c1f 100644 --- a/pyfixest/visualize.py +++ b/pyfixest/visualize.py @@ -1,10 +1,7 @@ import pandas as pd from typing import List, Tuple, Optional -from scipy.stats import norm -import re from pyfixest.summarize import _post_processing_input_checks from typing import Union -import numpy as np from lets_plot import ( ggplot, aes, @@ -53,6 +50,8 @@ def iplot( A lets-plot figure. """ + import pdb; pdb.set_trace() + models = _post_processing_input_checks(models) df_all = [] diff --git a/tests/test_did.py b/tests/test_did.py index 364e4b19..ccbb23b6 100644 --- a/tests/test_did.py +++ b/tests/test_did.py @@ -17,7 +17,7 @@ def test_event_study(): """ - Test hte event_study() function. + Test the event_study() function. """ df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") diff --git a/tests/test_i.py b/tests/test_i.py new file mode 100644 index 00000000..188ee73a --- /dev/null +++ b/tests/test_i.py @@ -0,0 +1,23 @@ +import pytest +import numpy as np +import pandas as pd +from pyfixest.estimation import feols + +def test_i(): + + df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") + df_het["X"] = np.random.normal(size = len(df_het)) + + df_het["rel_year"] = df_het["rel_year"].astype("category") + df_het["treat"] = df_het["treat"].astype("int") + + #feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = 1.0) + #feols("dep_var~i(rel_year, year)", df_het, i_ref1 = 1.0) + feols("dep_var~i(rel_year)", df_het, i_ref1 = 1.0) + + feols("dep_var~i(rel_year, treat)", df_het) + feols("dep_var~i(rel_year, year)", df_het) + feols("dep_var~i(rel_year)", df_het) + + + diff --git a/tests/test_plots.py b/tests/test_plots.py index 75e261c3..8dc58be4 100644 --- a/tests/test_plots.py +++ b/tests/test_plots.py @@ -15,7 +15,7 @@ def data(): def test_iplot(data): fit1 = feols(fml="Y ~ i(f2, X1) | f1", data=data, vcov="iid") fit2 = feols(fml="Y ~ i(f2, X1) | f2", data=data, vcov="iid") - fit3 = feols(fml="Y ~ i(f2, X1, ref = 1.0)", data=data, vcov="iid") + fit3 = feols(fml="Y ~ i(f2, X1)", data=data, vcov="iid", i_ref1=1.0) fit1.iplot() fit2.iplot() From a5176270a1a22c37ec20458ff04dc44bfec02ab8 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 4 Nov 2023 14:36:21 +0100 Subject: [PATCH 32/43] two reference levels --- pyfixest/FixestMulti.py | 8 ++++ pyfixest/model_matrix_fixest.py | 67 ++++++++++++++++++++------------- tests/test_i.py | 9 ++++- 3 files changed, 55 insertions(+), 29 deletions(-) diff --git a/pyfixest/FixestMulti.py b/pyfixest/FixestMulti.py index 20a483a8..46a8fdf4 100644 --- a/pyfixest/FixestMulti.py +++ b/pyfixest/FixestMulti.py @@ -86,6 +86,14 @@ def _prepare_estimation( self._fixef_keys = None self._is_multiple_estimation = None + # set i_ref1 and i_ref2 to list if not None + if i_ref1 is not None: + if not isinstance(i_ref1, list): + i_ref1 = [i_ref1] + if i_ref2 is not None: + if not isinstance(i_ref2, list): + i_ref2 = [i_ref2] + fxst_fml = FixestFormulaParser(fml) fxst_fml.get_fml_dict() # fxst_fml._fml_dict might look like this: {'0': {'Y': ['Y~X1'], 'Y2': ['Y2~X1']}}. Hence {FE: {DEPVAR: [FMLS]}} if fxst_fml._is_iv: diff --git a/pyfixest/model_matrix_fixest.py b/pyfixest/model_matrix_fixest.py index a26dc4a1..19d9fa9a 100644 --- a/pyfixest/model_matrix_fixest.py +++ b/pyfixest/model_matrix_fixest.py @@ -69,9 +69,6 @@ def model_matrix_fixest( _ivars = _find_ivars(fml)[0] - #_ivars = _deparse_ivars(_ivars) - #_ivars, _drop_ref = _clean_ivars(_ivars, data) - # step 1: deparse formula fml_parts = fml.split("|") depvar, covar = fml_parts[0].split("~") @@ -89,22 +86,9 @@ def model_matrix_fixest( covar = covar.replace(x, interact_vars) break - # drop_ref - _ivar1 = _ivars[0] - - #_ivar2_is_discrete = data[_ivar2].dtype.name in ["category", "object"] - if i_ref1 is not None: - if len(_ivars) == 2: - _drop_ref = f"C({_ivar1})[T.{i_ref1}]:" - #else: - # pass - # #raise NotImplementedError("Interaction of two discrete variables is not yet implemented.") - - elif len(_ivars) == 1: - _drop_ref = f"C({_ivars})[T.{i_ref1}]" - - else: - _drop_ref = None + # should any variables be dropped from the model matrix + # (e.g., reference level dummies, if specified) + _drop_ref = _get_drop_ref(_ivars, i_ref1, i_ref2) if len(fml_parts) == 3: fval, fml_iv = fml_parts[1], fml_parts[2] @@ -191,15 +175,14 @@ def model_matrix_fixest( else: na_index = na_index_stage2 - # drop variables before collecting variable names - - if isinstance(_drop_ref, list): - _drop_ref = _drop_ref[0] - + # now drop variables before collecting variable names if _ivars is not None: - if _drop_ref is not None: + if _drop_ref: - columns_to_drop = [col for col in X.columns if _drop_ref in col] + if len(_drop_ref) == 1: + columns_to_drop = [col for col in X.columns if _drop_ref[0] in col] + else: + columns_to_drop = [col for col in X.columns if _drop_ref[0] in col or _drop_ref[1] in col] if not X_is_empty: X.drop(columns_to_drop, axis=1, inplace = True) @@ -363,4 +346,34 @@ def _get_icovars(_ivars: List[str], X: pd.DataFrame) -> Optional[List[str]]: else: _icovars = None - return _icovars \ No newline at end of file + return _icovars + + +def _get_drop_ref(_ivars: List[str], i_ref1: Optional[Union[List, str, int]] = None, i_ref2: Optional[Union[List, str, int]] = None) -> Optional[List[str]]: + + """ + Get the name of reference level dummies to be dropped from the model matrix. + Args: + _ivars (list): A list of interaction variables. + i_ref1 (str or list): The reference level for the first variable in the i() syntax. + i_ref2 (str or list): The reference level for the second variable in the i() syntax. + Returns: + _drop_ref (list): A list of reference level dummies to be dropped from the model matrix. If no reference level is specified, None is returned. + Examples: + >>> _get_drop_ref(_ivars = ["f2", "X1"], i_ref1 = [1.0, 2.0]) + >>> ['C(f2)[T.1.0]:', 'C(f2)[T.2.0]:'] + """ + + _drop_ref = None # default: if no _ivar, or if _ivar but no i_ref1, i_ref2 specified + if _ivars: + _ivar1 = _ivars[0] + if i_ref1 is not None: + len_i_ref1 = len(i_ref1) + if len_i_ref1 not in [1, 2]: + raise ValueError(f"i_ref1 must be a string or list of length 1 or 2, but it is a list of length {len_i_ref1}.") + if len(_ivars) == 2: + _drop_ref = [f"C({_ivar1})[T.{x}]:" for x in i_ref1] + else: #len(_ivars) == 1: + _drop_ref = [f"C({_ivars})[T.{x}]" for x in i_ref1] + + return _drop_ref diff --git a/tests/test_i.py b/tests/test_i.py index 188ee73a..e4689f1f 100644 --- a/tests/test_i.py +++ b/tests/test_i.py @@ -11,10 +11,15 @@ def test_i(): df_het["rel_year"] = df_het["rel_year"].astype("category") df_het["treat"] = df_het["treat"].astype("int") - #feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = 1.0) - #feols("dep_var~i(rel_year, year)", df_het, i_ref1 = 1.0) + feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = 1.0) + feols("dep_var~i(rel_year, year)", df_het, i_ref1 = 1.0) feols("dep_var~i(rel_year)", df_het, i_ref1 = 1.0) + feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = [1.0, 2.0]) + feols("dep_var~i(rel_year, year)", df_het, i_ref1 = [1.0, 2.0]) + feols("dep_var~i(rel_year)", df_het, i_ref1 = [1.0, 2.0]) + + feols("dep_var~i(rel_year, treat)", df_het) feols("dep_var~i(rel_year, year)", df_het) feols("dep_var~i(rel_year)", df_het) From 48a173e4b590fb51d3efcc4c750af791bd01b94d Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 4 Nov 2023 16:23:13 +0100 Subject: [PATCH 33/43] fix small bug re _drop_ref --- pyfixest/experimental/did.py | 32 +++++++------ pyfixest/model_matrix_fixest.py | 81 +++++++++++++++++++++++---------- tests/test_i.py | 11 +++++ 3 files changed, 88 insertions(+), 36 deletions(-) diff --git a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index de9f1c68..535c24cd 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -7,6 +7,8 @@ from abc import ABC, abstractmethod from formulaic import model_matrix from scipy.sparse.linalg import spsolve +from typing import Optional, Union, List + @@ -306,7 +308,7 @@ def vcov(self): -def _did2s_estimate(data: pd.DataFrame, yname : str, _first_stage: str, _second_stage: str, treatment: str): +def _did2s_estimate(data: pd.DataFrame, yname : str, _first_stage: str, _second_stage: str, treatment: str, i_ref1: Optional[Union[int, str, List]] = None, i_ref2: Optional[Union[int, str, List]] = None): """ Args: @@ -320,7 +322,7 @@ def _did2s_estimate(data: pd.DataFrame, yname : str, _first_stage: str, _second_ """ _first_stage_full = f"{yname} {_first_stage}" - _second_stage_full = f"{yname}_hat {_second_stage}" + _second_stage_full = f"{yname}_hat {_second_stage} + 0" if treatment is not None: _not_yet_treated_data = data[data[treatment] == False] @@ -338,7 +340,7 @@ def _did2s_estimate(data: pd.DataFrame, yname : str, _first_stage: str, _second_ _first_u = data[f"{yname}"].to_numpy().flatten() - Y_hat data[f"{yname}_hat"] = _first_u - fit2 = feols(_second_stage_full, data =data, vcov = "iid") + fit2 = feols(_second_stage_full, data =data, vcov = "iid", i_ref1=i_ref1, i_ref2=i_ref2) _second_u = fit2.resid() return fit2, _first_u, _second_u @@ -402,7 +404,7 @@ def _did2s_vcov(data: pd.DataFrame, first_stage: str, second_stage: str, treatme return vcov, _G -def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, treatment: str, cluster: str): +def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, treatment: str, cluster: str, i_ref1 : Optional[Union[int, str, List]] = None, i_ref2 : Optional[Union[int, str, List]] = None): """ Estimate a Difference-in-Differences model using Gardner's two-step DID2S estimator. @@ -413,6 +415,8 @@ def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, second_stage (str): The formula for the second stage. Must start with '~'. treatment (str): The name of the treatment variable. cluster (str): The name of the cluster variable. + i_ref1 (int, str or list): The reference value(s) for the first variable used with "i()" syntax. + i_ref2 (int, str or list): The reference value(s) for the second variable used with "i()" syntax. Returns: A fitted model object of class feols. Examples: @@ -437,16 +441,18 @@ def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, assert first_stage[0] == "~", "First stage must start with ~" assert second_stage[0] == "~", "Second stage must start with ~" - fit, first_u, second_u = _did2s_estimate(data, yname, first_stage, second_stage, treatment) - vcov, _G = _did2s_vcov(data, first_stage, second_stage, treatment, first_u, second_u, cluster) - fit._vcov = vcov - fit._G = _G - fit.get_inference() # update inference with correct vcov matrix + fit, first_u, second_u = _did2s_estimate(data, yname, first_stage, second_stage, treatment, i_ref1, i_ref2) + + if False: + vcov, _G = _did2s_vcov(data, first_stage, second_stage, treatment, first_u, second_u, cluster, i_ref1, i_ref2) + fit._vcov = vcov + fit._G = _G + fit.get_inference() # update inference with correct vcov matrix - fit._vcov_type = "CRV1" - fit._vcov_type_detail = "CRV1 (GMM)" - #fit._G = did2s._G - fit._method = "did2s" + fit._vcov_type = "CRV1" + fit._vcov_type_detail = "CRV1 (GMM)" + #fit._G = did2s._G + fit._method = "did2s" return fit diff --git a/pyfixest/model_matrix_fixest.py b/pyfixest/model_matrix_fixest.py index 19d9fa9a..20d4db86 100644 --- a/pyfixest/model_matrix_fixest.py +++ b/pyfixest/model_matrix_fixest.py @@ -88,6 +88,7 @@ def model_matrix_fixest( # should any variables be dropped from the model matrix # (e.g., reference level dummies, if specified) + _check_i_refs(_ivars, i_ref1, i_ref2, data) _drop_ref = _get_drop_ref(_ivars, i_ref1, i_ref2) if len(fml_parts) == 3: @@ -177,6 +178,7 @@ def model_matrix_fixest( # now drop variables before collecting variable names if _ivars is not None: + if _drop_ref: if len(_drop_ref) == 1: @@ -239,28 +241,6 @@ def _find_ivars(x): return x, None - -def _check_ivars(data, ivars): - """ - Checks if the variables in the i() syntax are of the correct type. - Args: - data (pandas.DataFrame): The dataframe containing the data used for the model fitting. - ivars (list): The list of variables specified in the i() syntax. - Returns: - None - """ - - i0_type = data[ivars[0]].dtype - #i1_type = data[ivars[1]].dtype - if not i0_type in ["category", "O"]: - raise ValueError( - f""" - Column {ivars[0]} is not of type 'O' or 'category', which is required in the first position of i(). Instead it is of type " - {i0_type.name} If a reference level is set, it is required that the variable in the first position of 'i()' is of type 'O' or 'category'. - """ - ) - - def _check_is_iv(fml): """ Check if the formula contains an IV. @@ -366,6 +346,7 @@ def _get_drop_ref(_ivars: List[str], i_ref1: Optional[Union[List, str, int]] = N _drop_ref = None # default: if no _ivar, or if _ivar but no i_ref1, i_ref2 specified if _ivars: + _ivar1 = _ivars[0] if i_ref1 is not None: len_i_ref1 = len(i_ref1) @@ -374,6 +355,60 @@ def _get_drop_ref(_ivars: List[str], i_ref1: Optional[Union[List, str, int]] = N if len(_ivars) == 2: _drop_ref = [f"C({_ivar1})[T.{x}]:" for x in i_ref1] else: #len(_ivars) == 1: - _drop_ref = [f"C({_ivars})[T.{x}]" for x in i_ref1] + _drop_ref = [f"C({_ivar1})[T.{x}]" for x in i_ref1] + + if len(_ivars) == 2: + + if i_ref2 is not None: + _ivar2 = _ivars[1] + len_i_ref2 = len(i_ref2) + if len_i_ref2 not in [1, 2]: + raise ValueError(f"i_ref2 must be a string or list of length 1 or 2, but it is a list of length {len_i_ref2}.") + if len(_ivars) == 2: + _drop_ref += [f":{_ivar2}[T.{x}]" for x in i_ref2] + else: #len(_ivars) == 1: + _drop_ref += [f"{_ivar2}[T.{x}]" for x in i_ref2] + + else: + + if i_ref2 is not None: + warnings.warn(f"i_ref2 is not used because there is only one variable in the i() syntax, i({_ivar1}).") + + else: + + if i_ref1 is not None: + warnings.warn(f"i_ref1 is not used because i() syntax is not used.") + if i_ref2 is not None: + warnings.warn(f"i_ref2 is not used because i() syntax is not used.") return _drop_ref + + +def _check_i_refs(ivars, i_ref1, i_ref2, data): + + if ivars: + + ivar1 = ivars[0] + if i_ref1: + if len(i_ref1) == 1: + if not i_ref1[0] in data[ivar1].unique(): + raise ValueError(f"i_ref1 must be a value in {ivar1}, but it is {i_ref1}. Maybe you are using an incorrect data type?") + else: + if not i_ref1[0] and i_ref1[1] in data[ivar1].unique(): + raise ValueError(f"i_ref1 must be a value in {ivar1}, but it is {i_ref1}. Maybe you are using an incorrect data type?") + + + if len(ivars) == 2: + ivar2 = ivars[1] + if i_ref2: + + if data[ivar2].dtype not in ["category", "int"]: + raise ValueError(f"If you are using a reference level via the 'i_ref2' argument, the associated variable {ivar2} must be of type 'category' or 'int', but it is of type {data[ivar2].dtype}.") + + if len(i_ref2) == 1: + if not i_ref2[0] in data[ivar2].unique(): + raise ValueError(f"i_ref1 must be a value in {ivar2}, but it is {i_ref2}. Maybe you are using an incorrect data type?") + else: + if not i_ref2[0] and i_ref2[1] in data[ivar2].unique(): + raise ValueError(f"i_ref1 must be a value in {ivar2}, but it is {i_ref2}. Maybe you are using an incorrect data type?") + diff --git a/tests/test_i.py b/tests/test_i.py index e4689f1f..7fc8768d 100644 --- a/tests/test_i.py +++ b/tests/test_i.py @@ -19,10 +19,21 @@ def test_i(): feols("dep_var~i(rel_year, year)", df_het, i_ref1 = [1.0, 2.0]) feols("dep_var~i(rel_year)", df_het, i_ref1 = [1.0, 2.0]) + feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = [1.0, 2.0], i_ref2 = [1.0]) feols("dep_var~i(rel_year, treat)", df_het) feols("dep_var~i(rel_year, year)", df_het) feols("dep_var~i(rel_year)", df_het) + with pytest.raises(ValueError): + feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = "1.0") + with pytest.raises(ValueError): + feols("dep_var~i(rel_year, treat)", df_het, i_ref2 = "1.0") + with pytest.raises(ValueError): + feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = [1.0], i_ref2 = "1.0") + + with pytest.raises(ValueError): + feols("dep_var~i(rel_year, X)", df_het, i_ref1 = [1.0, 2.0], i_ref2 = [1.0]) + From 28b30bbd7e287a04af4805e5a37f6033a9392520 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 4 Nov 2023 18:24:51 +0100 Subject: [PATCH 34/43] get did2s() to run --- pyfixest/experimental/did.py | 86 +++++++++++++++++++++++++++--------- pyfixest/visualize.py | 2 - 2 files changed, 66 insertions(+), 22 deletions(-) diff --git a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index 535c24cd..d84e4732 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -3,9 +3,11 @@ from pyfixest.estimation import feols from pyfixest.exceptions import NotImplementedError +from pyfixest.model_matrix_fixest import model_matrix_fixest from abc import ABC, abstractmethod from formulaic import model_matrix +from scipy.sparse import csr_matrix from scipy.sparse.linalg import spsolve from typing import Optional, Union, List @@ -298,6 +300,7 @@ def vcov(self): return _did2s_vcov( data = self._data, + yname = self._yname, first_stage = self._fml1, second_stage = self._fml2, treatment = "ATT", @@ -317,6 +320,8 @@ def _did2s_estimate(data: pd.DataFrame, yname : str, _first_stage: str, _second_ _first_stage (str): The formula for the first stage. _second_stage (str): The formula for the second stage. treatment (str): The name of the treatment variable. + i_ref1 (int, str or list): The reference value(s) for the first variable used with "i()" syntax. Only applicable for the second stage formula. + i_ref2 (int, str or list): The reference value(s) for the second variable used with "i()" syntax. Only applicable for the second stage formula. Returns: A fitted model object of class feols and the first and second stage residuals. """ @@ -346,13 +351,23 @@ def _did2s_estimate(data: pd.DataFrame, yname : str, _first_stage: str, _second_ return fit2, _first_u, _second_u -def _did2s_vcov(data: pd.DataFrame, first_stage: str, second_stage: str, treatment: str, first_u: np.ndarray, second_u: np.ndarray, cluster : str): +def _did2s_vcov(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, treatment: str, first_u: np.ndarray, second_u: np.ndarray, cluster : str, i_ref1: Optional[Union[int, str, List]] = None, i_ref2: Optional[Union[int, str, List]] = None): """ Compute a variance covariance matrix for Gardner's 2-stage Difference-in-Differences Estimator. Args: - data (pd.DataFrame): - + data (pd.DataFrame): The DataFrame containing all variables. + yname (str): The name of the dependent variable. + first_stage (str): The formula for the first stage. + second_stage (str): The formula for the second stage. + treatment (str): The name of the treatment variable. + first_u (np.ndarray): The first stage residuals. + second_u (np.ndarray): The second stage residuals. + cluster (str): The name of the cluster variable. + i_ref1 (int, str or list): The reference value(s) for the first variable used with "i()" syntax. Only applicable for the second stage formula. + i_ref2 (int, str or list): The reference value(s) for the second variable used with "i()" syntax. Only applicable for the second stage formula. + Returns: + A variance covariance matrix. """ cluster_col = data[cluster] @@ -367,9 +382,22 @@ def _did2s_vcov(data: pd.DataFrame, first_stage: str, second_stage: str, treatme first_stage_fe = "+".join(first_stage_fe) first_stage = f"{first_stage_x}+{first_stage_fe}" - - X1 = model_matrix(first_stage, data, output = "sparse") # here: from potential two part formula to one - part formula - X2 = model_matrix(second_stage, data, output = "sparse") + second_stage = f"{second_stage}" + _, X1, _, _, _, _, _, _, _ = model_matrix_fixest( + fml = f"{yname} {first_stage}", + data = data, + i_ref1=i_ref1, + i_ref2 = i_ref2 + ) + _, X2, _, _, _, _, _, _, _ = model_matrix_fixest( + fml = f"{yname} {second_stage}", + data = data, + i_ref1 = i_ref1, + i_ref2 = i_ref2 + ) # reference values not dropped, multicollinearity error + + X1 = csr_matrix(X1.values) + X2 = csr_matrix(X2.values) X10 = X1.copy().tocsr() treated_rows = np.where(data[treatment], 0, 1) @@ -415,8 +443,8 @@ def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, second_stage (str): The formula for the second stage. Must start with '~'. treatment (str): The name of the treatment variable. cluster (str): The name of the cluster variable. - i_ref1 (int, str or list): The reference value(s) for the first variable used with "i()" syntax. - i_ref2 (int, str or list): The reference value(s) for the second variable used with "i()" syntax. + i_ref1 (int, str or list): The reference value(s) for the first variable used with "i()" syntax. Only applicable for the second stage formula. + i_ref2 (int, str or list): The reference value(s) for the second variable used with "i()" syntax. Only applicable for the second stage formula. Returns: A fitted model object of class feols. Examples: @@ -441,18 +469,36 @@ def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, assert first_stage[0] == "~", "First stage must start with ~" assert second_stage[0] == "~", "Second stage must start with ~" - fit, first_u, second_u = _did2s_estimate(data, yname, first_stage, second_stage, treatment, i_ref1, i_ref2) - - if False: - vcov, _G = _did2s_vcov(data, first_stage, second_stage, treatment, first_u, second_u, cluster, i_ref1, i_ref2) - fit._vcov = vcov - fit._G = _G - fit.get_inference() # update inference with correct vcov matrix - - fit._vcov_type = "CRV1" - fit._vcov_type_detail = "CRV1 (GMM)" - #fit._G = did2s._G - fit._method = "did2s" + fit, first_u, second_u = _did2s_estimate( + data = data, + yname = yname, + _first_stage = first_stage, + _second_stage = second_stage, + treatment = treatment, + i_ref1 = i_ref1, + i_ref2 = i_ref2 + ) + + vcov, _G = _did2s_vcov( + data = data, + yname = yname, + first_stage = first_stage, + second_stage = second_stage, + treatment = treatment, + first_u = first_u, + second_u = second_u, + cluster = cluster, + i_ref1 = i_ref1, + i_ref2 = i_ref2 + ) + fit._vcov = vcov + fit._G = _G + fit.get_inference() # update inference with correct vcov matrix + + fit._vcov_type = "CRV1" + fit._vcov_type_detail = "CRV1 (GMM)" + #fit._G = did2s._G + fit._method = "did2s" return fit diff --git a/pyfixest/visualize.py b/pyfixest/visualize.py index 64ce7c1f..2452f40b 100644 --- a/pyfixest/visualize.py +++ b/pyfixest/visualize.py @@ -50,8 +50,6 @@ def iplot( A lets-plot figure. """ - import pdb; pdb.set_trace() - models = _post_processing_input_checks(models) df_all = [] From eb817170de7c215cbcd273e9b88156fbe4a8266e Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 4 Nov 2023 19:10:48 +0100 Subject: [PATCH 35/43] fix did2s vcov bug --- pyfixest/experimental/did.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index d84e4732..cbc9bdc1 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -382,7 +382,8 @@ def _did2s_vcov(data: pd.DataFrame, yname : str, first_stage: str, second_stage: first_stage_fe = "+".join(first_stage_fe) first_stage = f"{first_stage_x}+{first_stage_fe}" - second_stage = f"{second_stage}" + second_stage = f"{second_stage} + 0" + _, X1, _, _, _, _, _, _, _ = model_matrix_fixest( fml = f"{yname} {first_stage}", data = data, @@ -423,8 +424,9 @@ def _did2s_vcov(data: pd.DataFrame, yname : str, first_stage: str, second_stage: second_u_g = second_u[cluster_col == g] W_g = X2g.T.dot(second_u_g) - V @ X10g.T.dot(first_u_g) - score = spsolve(X2X2, W_g) + if score.ndim == 1: + score = score.reshape(-1,1) cov_g = score.dot(score.T) vcov += cov_g @@ -491,6 +493,7 @@ def did2s(data: pd.DataFrame, yname : str, first_stage: str, second_stage: str, i_ref1 = i_ref1, i_ref2 = i_ref2 ) + fit._vcov = vcov fit._G = _G fit.get_inference() # update inference with correct vcov matrix From bbac5a106271f03217551ea5edde60d90d7e2989 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 5 Nov 2023 13:50:40 +0100 Subject: [PATCH 36/43] add tests --- pyfixest/estimation.py | 22 +++++++--- pyfixest/exceptions.py | 3 ++ pyfixest/experimental/did.py | 5 +-- pyfixest/experimental/did2s.py | 0 pyfixest/model_matrix_fixest.py | 41 +++++++++++++------ tests/test_did.py | 65 ++++++++++++++++++++++++++++-- tests/test_errors.py | 16 ++++++++ tests/test_vs_fixest.py | 71 ++++++++++++++++++++++++++++++++- 8 files changed, 197 insertions(+), 26 deletions(-) delete mode 100644 pyfixest/experimental/did2s.py diff --git a/pyfixest/estimation.py b/pyfixest/estimation.py index b11880e6..adf9e5af 100644 --- a/pyfixest/estimation.py +++ b/pyfixest/estimation.py @@ -47,7 +47,8 @@ def feols( Other special syntax includes: - i() for interaction of a categorical and non-categorical variable (e.g. "i(X1,X2)" for interaction between X1 and X2). - Using i() is required to use with some custom methods, e.g. iplot(). + Using i() is required to use with some custom methods, e.g. iplot(). In contrast to r-fixest, reference levels cannot be + set in the formula, but must be specified via the i_ref1 and i_ref2 arguments. - ^ for interacted fixed effects (e.g. "fe1^fe2" for interaction between fe1 and fe2) All other parts of the formula must be compatible with formula parsing via the formulaic module. @@ -128,7 +129,9 @@ def feols( """ - _estimation_input_checks(fml, data, vcov, ssc, fixef_rm, collin_tol) + assert i_ref2 is None, "The function argument i_ref2 is not yet supported." + + _estimation_input_checks(fml, data, vcov, ssc, fixef_rm, collin_tol, i_ref1) fixest = FixestMulti(data=data) fixest._prepare_estimation("feols", fml, vcov, ssc, fixef_rm, i_ref1, i_ref2) @@ -181,7 +184,8 @@ def fepois( Other special syntax includes: - i() for interaction of a categorical and non-categorical variable (e.g. "i(X1,X2)" for interaction between X1 and X2). - Using i() is required to use with some custom methods, e.g. iplot(). + Using i() is required to use with some custom methods, e.g. iplot(). In contrast to r-fixest, reference levels cannot be + set in the formula, but must be specified via the i_ref1 and i_ref2 arguments. - ^ for interacted fixed effects (e.g. "fe1^fe2" for interaction between fe1 and fe2) All other parts of the formula must be compatible with formula parsing via the formulaic module. @@ -252,7 +256,9 @@ def fepois( """ - _estimation_input_checks(fml, data, vcov, ssc, fixef_rm, collin_tol) + assert i_ref2 is None, "The function argument i_ref2 is not yet supported." + + _estimation_input_checks(fml, data, vcov, ssc, fixef_rm, collin_tol, i_ref1) fixest = FixestMulti(data=data) @@ -276,7 +282,7 @@ def fepois( return fixest.fetch_model(0, print_fml=False) -def _estimation_input_checks(fml, data, vcov, ssc, fixef_rm, collin_tol): +def _estimation_input_checks(fml, data, vcov, ssc, fixef_rm, collin_tol, i_ref1): if not isinstance(fml, str): raise ValueError("fml must be a string") if not isinstance(data, pd.DataFrame): @@ -294,3 +300,9 @@ def _estimation_input_checks(fml, data, vcov, ssc, fixef_rm, collin_tol): raise ValueError("collin_tol must be greater than zero") if not collin_tol < 1: raise ValueError("collin_tol must be less than one") + + assert i_ref1 is None or isinstance(i_ref1, (list, str, int, bool, float)), "i_ref1 must be either None, a list, string, int, bool, or float" + # check that if i_ref1 is a list, all elements are of the same type + if isinstance(i_ref1, list): + assert len(i_ref1) > 0, "i_ref1 must not be an empty list" + assert all(isinstance(x, type(i_ref1[0])) for x in i_ref1), "i_ref1 must be a list of elements of the same type" diff --git a/pyfixest/exceptions.py b/pyfixest/exceptions.py index 55f6a438..a6e7e0d8 100644 --- a/pyfixest/exceptions.py +++ b/pyfixest/exceptions.py @@ -54,4 +54,7 @@ class MatrixNotFullRankError(Exception): pass class EmptyDesignMatrixError(Exception): + pass + +class InvalidReferenceLevelError(Exception): pass \ No newline at end of file diff --git a/pyfixest/experimental/did.py b/pyfixest/experimental/did.py index 1f17fa58..69c4d0eb 100644 --- a/pyfixest/experimental/did.py +++ b/pyfixest/experimental/did.py @@ -13,8 +13,6 @@ - - def event_study(data, yname, idname, tname, gname, xfml = None, estimator = "twfe", att = True, cluster = "idname"): """ @@ -336,8 +334,7 @@ def _did2s_estimate(data: pd.DataFrame, yname : str, _first_stage: str, _second_ _not_yet_treated_data = data[data["ATT"] == False] # estimate first stage - fit1 = feols(fml = _first_stage_full, data = _not_yet_treated_data, vcov = "iid") # iid as it might be faster than CRV - + fit1 = feols(fml = _first_stage_full, data = _not_yet_treated_data, vcov = "iid", i_ref1 = None, i_ref2 = None) # iid as it might be faster than CRV # obtain estimated fixed effects fit1.fixef() diff --git a/pyfixest/experimental/did2s.py b/pyfixest/experimental/did2s.py deleted file mode 100644 index e69de29b..00000000 diff --git a/pyfixest/model_matrix_fixest.py b/pyfixest/model_matrix_fixest.py index 20d4db86..39ad0f0b 100644 --- a/pyfixest/model_matrix_fixest.py +++ b/pyfixest/model_matrix_fixest.py @@ -1,9 +1,12 @@ -from formulaic import model_matrix -import pandas as pd import re -from typing import Optional, Tuple, List, Union +import warnings +import pandas as pd import numpy as np +from formulaic import model_matrix +from typing import Optional, Tuple, List, Union +from pyfixest.exceptions import InvalidReferenceLevelError + def model_matrix_fixest( fml: str, data: pd.DataFrame, weights: Optional[str] = None, i_ref1: Optional[Union[List, str, int]] = None, i_ref2: Optional[Union[List, str, int]] = None @@ -238,7 +241,7 @@ def _find_ivars(x): if i_match: return i_match[0].split(","), "i" else: - return x, None + return None, None def _check_is_iv(fml): @@ -389,26 +392,38 @@ def _check_i_refs(ivars, i_ref1, i_ref2, data): if ivars: ivar1 = ivars[0] + type_ivar1 = data[ivar1].to_numpy().dtype + unique_ivar1 = data[ivar1].unique() + if i_ref1: + + type_i_ref1 = type(i_ref1[0]) + if len(i_ref1) == 1: - if not i_ref1[0] in data[ivar1].unique(): - raise ValueError(f"i_ref1 must be a value in {ivar1}, but it is {i_ref1}. Maybe you are using an incorrect data type?") + if not i_ref1[0] in unique_ivar1 or type_i_ref1 != type_ivar1: + raise InvalidReferenceLevelError(f"i_ref1 must be a value in {ivar1}, but it is {i_ref1}. Maybe you are using an incorrect data type?") else: - if not i_ref1[0] and i_ref1[1] in data[ivar1].unique(): - raise ValueError(f"i_ref1 must be a value in {ivar1}, but it is {i_ref1}. Maybe you are using an incorrect data type?") + if not i_ref1[0] and i_ref1[1] in unique_ivar1 or type_i_ref1 != type_ivar1: + raise InvalidReferenceLevelError(f"i_ref1 must be a value in {ivar1}, but it is {i_ref1}. Maybe you are using an incorrect data type?") if len(ivars) == 2: + ivar2 = ivars[1] + type_ivar2 = data[ivar2].to_numpy().dtype + unique_ivar2 = data[ivar2].unique() + if i_ref2: + type_i_ref2 = type(i_ref2[0]) + if data[ivar2].dtype not in ["category", "int"]: - raise ValueError(f"If you are using a reference level via the 'i_ref2' argument, the associated variable {ivar2} must be of type 'category' or 'int', but it is of type {data[ivar2].dtype}.") + raise InvalidReferenceLevelError(f"If you are using a reference level via the 'i_ref2' argument, the associated variable {ivar2} must be of type 'category' or 'int', but it is of type {data[ivar2].dtype}.") if len(i_ref2) == 1: - if not i_ref2[0] in data[ivar2].unique(): - raise ValueError(f"i_ref1 must be a value in {ivar2}, but it is {i_ref2}. Maybe you are using an incorrect data type?") + if not i_ref2[0] in unique_ivar2 or type_i_ref2 != type_ivar2: + raise InvalidReferenceLevelError(f"i_ref1 must be a value in {ivar2}, but it is {i_ref2}. Maybe you are using an incorrect data type?") else: - if not i_ref2[0] and i_ref2[1] in data[ivar2].unique(): - raise ValueError(f"i_ref1 must be a value in {ivar2}, but it is {i_ref2}. Maybe you are using an incorrect data type?") + if not i_ref2[0] and i_ref2[1] in unique_ivar2 or type_i_ref2 != type_ivar2: + raise InvalidReferenceLevelError(f"i_ref1 must be a value in {ivar2}, but it is {i_ref2}. Maybe you are using an incorrect data type?") diff --git a/tests/test_did.py b/tests/test_did.py index 48b5d14b..0b0e5fd7 100644 --- a/tests/test_did.py +++ b/tests/test_did.py @@ -59,6 +59,7 @@ def test_did2s(): df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") + # ATT, no covariates fit_did2s = did2s_pyfixest( data = df_het, yname = "dep_var", @@ -78,12 +79,70 @@ def test_did2s(): did2s_df = broom.tidy_fixest(fit_did2s_r, conf_int=ro.BoolVector([True])) did2s_df = pd.DataFrame(did2s_df).T - if True: + + np.testing.assert_allclose( + fit_did2s.coef(), stats.coef(fit_did2s_r) + ) + np.testing.assert_allclose( + fit_did2s.se(), float(did2s_df[2]) + ) + + # ATT, event study + + fit = did2s_pyfixest( + df_het, + yname = "dep_var", + first_stage = "~ 0 | state + year", + second_stage = "~i(rel_year)", + treatment = "treat", + cluster = "state", + i_ref1 = [-1.0], + ) + + fit_r = did2s.did2s( + data = df_het, + yname = "dep_var", first_stage = ro.Formula("~ 0 | state + year"), + second_stage = ro.Formula("~ i(rel_year, ref = c(-1))"), treatment = "treat", + cluster_var = "state" + ) + + did2s_df = broom.tidy_fixest(fit_r, conf_int=ro.BoolVector([True])) + did2s_df = pd.DataFrame(did2s_df).T + + np.testing.assert_allclose( + fit.coef(), stats.coef(fit_r) + ) + np.testing.assert_allclose( + fit.se(), float(did2s_df[2]) + ) + + # test event study with covariate + fit = did2s_pyfixest( + df_het, + yname = "dep_var", + first_stage = "~ X | state + year", + second_stage = "~i(rel_year)", + treatment = "treat", + cluster = "state", + i_ref1 = [-1.0, np.inf], + ) + + if False: + fit_r = did2s.did2s( + data = df_het, + yname = "dep_var", first_stage = ro.Formula("~ X | state + year"), + second_stage = ro.Formula("~ i(rel_year, ref = c(-1, Inf))"), treatment = "treat", + cluster_var = "state" + ) + + did2s_df = broom.tidy_fixest(fit_r, conf_int=ro.BoolVector([True])) + did2s_df = pd.DataFrame(did2s_df).T + np.testing.assert_allclose( - fit_did2s.coef(), stats.coef(fit_did2s_r) + fit.coef(), stats.coef(fit_r) ) np.testing.assert_allclose( - fit_did2s.se(), float(did2s_df[2]) + fit.se(), float(did2s_df[2]) ) diff --git a/tests/test_errors.py b/tests/test_errors.py index 60a51405..7e280c87 100644 --- a/tests/test_errors.py +++ b/tests/test_errors.py @@ -10,6 +10,7 @@ VcovTypeNotSupportedError, MultiEstNotSupportedError, NanInClusterVarError, + InvalidReferenceLevelError ) from pyfixest.estimation import feols, fepois from pyfixest.FormulaParser import FixestFormulaParser @@ -132,3 +133,18 @@ def test_poisson_devpar_count(): # under determined with pytest.raises(AssertionError): fepois(fml="Y ~ X1 | X4", data=data) + + +def test_i_interaction_errors(): + + data = get_data() + + with pytest.raises(InvalidReferenceLevelError): + # "a" not a level in f1 + feols(fml="Y ~ i(f1, X1)", data=data, i_ref1 = "a") + + with pytest.raises(InvalidReferenceLevelError): # incorrect type - int is provided but float required + # "a" not a level in f1 + feols(fml="Y ~ i(f1, X1)", data=data, i_ref1 = 1) + + diff --git a/tests/test_vs_fixest.py b/tests/test_vs_fixest.py index a484a910..2b0b35a9 100644 --- a/tests/test_vs_fixest.py +++ b/tests/test_vs_fixest.py @@ -366,7 +366,7 @@ def test_single_fit(N, seed, beta_type, error_type, dropna, model, inference, fm ("Y ~ X1 | csw0(f1,f2)"), ("Y ~ X1 + sw(X2, f1, f2)"), ("Y ~ csw(X1, X2, f3)"), - # ("Y ~ X3 + csw0(, X2, X3)"), + # ("Y ~ X2 + csw0(, X2, X2)"), ("Y ~ sw(X1, X2) | csw0(f1,f2,f3)"), ("Y ~ C(f2):X2 + sw0(X1, f3)"), ("Y + Y2 ~X1"), @@ -565,3 +565,72 @@ def get_data_r(fml, data): return data_r + + +def test_i_interaction(): + + """ + Test that interaction syntax via the `i()` operator works as in fixest + """ + + data = get_data(N=1000, seed=17021, beta_type="1", error_type="1").dropna() + + fit1 = feols("Y ~ i(f1, X2)", data=data) + fit2 = feols("Y ~ X1 + i(f1, X2) | f2", data=data) + fit3 = feols("Y ~ X1 + i(f1, X2) | f2", data=data, i_ref1 = 1.0) + fit4 = feols("Y ~ X1 + i(f1, X2) | f2", data=data, i_ref1 = [2.0]) + fit5 = feols("Y ~ X1 + i(f1, X2) | f2", data=data, i_ref1 = [2.0, 3.0]) + + + fit1_r = fixest.feols( + ro.Formula("Y ~ i(f1, X2)"), + data=data, + ssc=fixest.ssc(True, "none", True, "min", "min", False) + ) + fit2_r = fixest.feols( + ro.Formula("Y ~ X1 + i(f1, X2) | f2"), + data=data, + ssc=fixest.ssc(True, "none", True, "min", "min", False) + ) + fit3_r = fixest.feols( + ro.Formula("Y ~ X1 + i(f1, X2, ref = 1.0) | f2"), + data=data, + ssc=fixest.ssc(True, "none", True, "min", "min", False) + ) + fit4_r = fixest.feols( + ro.Formula("Y ~ X1 + i(f1, X2, ref = 2.0) | f2"), + data=data, + ssc=fixest.ssc(True, "none", True, "min", "min", False) + ) + fit5_r = fixest.feols( + ro.Formula("Y ~ X1 + i(f1, X2, ref = c(2.0, 3.0)) | f2"), + data=data, + ssc=fixest.ssc(True, "none", True, "min", "min", False) + ) + + # create tuples: (pyfixest, fixest) + fits = [(fit1, fit1_r), (fit2, fit2_r), (fit3, fit3_r), (fit4, fit4_r), (fit5, fit5_r)] + + for fit in fits: + + # test that coefficients match + coef_py = fit[0].coef().values + coef_r = stats.coef(fit[1]) + np.testing.assert_allclose( + coef_py, + coef_r, + rtol=1e-04, + atol=1e-04, + err_msg="Coefficients do not match." + ) + + # test that standard errors match + se_py = fit[0].se().values + se_r = fixest.se(fit[1]) + np.testing.assert_allclose( + se_py, + se_r, + rtol=1e-04, + atol=1e-04, + err_msg="Standard errors do not match." + ) From 63c83eb60e433d7d3ad18add32a091db9a3a63f7 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 5 Nov 2023 14:41:28 +0100 Subject: [PATCH 37/43] update did example --- pyfixest/experimental/load_df_het.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 pyfixest/experimental/load_df_het.py diff --git a/pyfixest/experimental/load_df_het.py b/pyfixest/experimental/load_df_het.py new file mode 100644 index 00000000..c79d106f --- /dev/null +++ b/pyfixest/experimental/load_df_het.py @@ -0,0 +1,17 @@ +import pandas as pd + +def load_df_het(): + + """ + Load the df_het.csv dataset. + + Returns + ------- + df_het : pd.DataFrame + The df_het dataset. + """ + + df_het = pd.read_csv("./pyfixest/experimental/data/df_het.csv") + return df_het + + From cbf54b8b58f4ea09c7fd9b3374fd79dcf5037553 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 5 Nov 2023 14:43:36 +0100 Subject: [PATCH 38/43] further updates --- docs/tutorial.ipynb | 649 +++++++++++++++++++++++++++++--------------- 1 file changed, 428 insertions(+), 221 deletions(-) diff --git a/docs/tutorial.ipynb b/docs/tutorial.ipynb index 98bf3040..8cf28cb2 100644 --- a/docs/tutorial.ipynb +++ b/docs/tutorial.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -75,15 +75,15 @@ " \n", " 0\n", " NaN\n", - " -9.534190\n", - " 1.0\n", + " -9.166216\n", + " 2.0\n", " 0.457858\n", " 9.0\n", " 15.0\n", " 0.0\n", " 1.0\n", - " -2.294594\n", - " 0.644690\n", + " 2.464146\n", + " 1.583723\n", " \n", " \n", " 1\n", @@ -96,7 +96,7 @@ " 9.0\n", " 11.0\n", " NaN\n", - " -3.706959\n", + " -2.749629\n", " \n", " \n", " 2\n", @@ -108,51 +108,56 @@ " 11.0\n", " 0.0\n", " 18.0\n", - " 0.543363\n", - " 2.102921\n", + " 0.449560\n", + " 0.910130\n", " \n", " \n", " 3\n", - " 4.443920\n", - " 13.960173\n", - " 0.0\n", + " 5.179868\n", + " 14.696121\n", + " 2.0\n", " 1.560402\n", " 15.0\n", " 1.0\n", " 4.0\n", " 15.0\n", - " -1.593905\n", - " 2.193772\n", + " 0.823438\n", + " 0.914900\n", " \n", " \n", " 4\n", - " 0.825537\n", - " -6.936620\n", - " 1.0\n", + " 1.193511\n", + " -6.568647\n", + " 2.0\n", " -3.472232\n", " 20.0\n", " 19.0\n", " 9.0\n", " 5.0\n", - " 1.482674\n", - " -3.239027\n", + " 0.895978\n", + " -3.056434\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Y Y2 X1 X2 ... f3 group_id Z1 Z2\n", - "0 NaN -9.534190 1.0 0.457858 ... 0.0 1.0 -2.294594 0.644690\n", - "1 3.221964 -2.835142 NaN -4.998406 ... 9.0 11.0 NaN -3.706959\n", - "2 1.449755 -3.721375 1.0 1.558480 ... 0.0 18.0 0.543363 2.102921\n", - "3 4.443920 13.960173 0.0 1.560402 ... 4.0 15.0 -1.593905 2.193772\n", - "4 0.825537 -6.936620 1.0 -3.472232 ... 9.0 5.0 1.482674 -3.239027\n", + " Y Y2 X1 X2 f1 f2 f3 group_id Z1 \\\n", + "0 NaN -9.166216 2.0 0.457858 9.0 15.0 0.0 1.0 2.464146 \n", + "1 3.221964 -2.835142 NaN -4.998406 8.0 6.0 9.0 11.0 NaN \n", + "2 1.449755 -3.721375 1.0 1.558480 NaN 11.0 0.0 18.0 0.449560 \n", + "3 5.179868 14.696121 2.0 1.560402 15.0 1.0 4.0 15.0 0.823438 \n", + "4 1.193511 -6.568647 2.0 -3.472232 20.0 19.0 9.0 5.0 0.895978 \n", "\n", - "[5 rows x 10 columns]" + " Z2 \n", + "0 1.583723 \n", + "1 -2.749629 \n", + "2 0.910130 \n", + "3 0.914900 \n", + "4 -3.056434 " ] }, - "execution_count": 30, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -178,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -187,7 +192,7 @@ "pyfixest.feols.Feols" ] }, - "execution_count": 31, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -215,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -231,9 +236,9 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | 0.271 | 0.056 | 4.812 | 0.000 | 0.161 | 0.382 |\n", + "| X1 | 0.362 | 0.057 | 6.321 | 0.000 | 0.249 | 0.474 |\n", "---\n", - "RMSE: 1.42 Adj. R2: 0.022 Adj. R2 Within: 0.022\n" + "RMSE: 1.422 Adj. R2: 0.038 Adj. R2 Within: 0.038\n" ] } ], @@ -251,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -267,9 +272,9 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | 0.271 | 0.056 | 4.812 | 0.000 | 0.161 | 0.382 |\n", + "| X1 | 0.362 | 0.057 | 6.321 | 0.000 | 0.249 | 0.474 |\n", "---\n", - "RMSE: 1.42 Adj. R2: 0.022 Adj. R2 Within: 0.022\n" + "RMSE: 1.422 Adj. R2: 0.038 Adj. R2 Within: 0.038\n" ] } ], @@ -288,18 +293,18 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Coefficient\n", - "X1 0.271127\n", + "X1 0.361642\n", "Name: Estimate, dtype: float64" ] }, - "execution_count": 34, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -310,18 +315,18 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Coefficient\n", - "X1 0.056346\n", + "X1 0.057212\n", "Name: Std. Error, dtype: float64" ] }, - "execution_count": 35, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -347,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -363,9 +368,9 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | 0.271 | 0.029 | 9.279 | 0.000 | 0.210 | 0.333 |\n", + "| X1 | 0.362 | 0.068 | 5.315 | 0.000 | 0.219 | 0.505 |\n", "---\n", - "RMSE: 1.42 Adj. R2: 0.022 Adj. R2 Within: 0.022\n", + "RMSE: 1.422 Adj. R2: 0.038 Adj. R2 Within: 0.038\n", "###\n", "\n", "Estimation: OLS\n", @@ -375,9 +380,9 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | 0.271 | 0.044 | 6.137 | 0.000 | 0.178 | 0.364 |\n", + "| X1 | 0.362 | 0.072 | 5.032 | 0.000 | 0.211 | 0.513 |\n", "---\n", - "RMSE: 1.42 Adj. R2: 0.022 Adj. R2 Within: 0.022\n" + "RMSE: 1.422 Adj. R2: 0.038 Adj. R2 Within: 0.038\n" ] } ], @@ -395,14 +400,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "param X1\n", - "t value 6.267915\n", + "t value 5.452993\n", "Pr(>|t|) 0.0\n", "bootstrap_type 11\n", "inference CRV(['group_id'])\n", @@ -410,7 +415,7 @@ "dtype: object" ] }, - "execution_count": 37, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -433,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -449,7 +454,7 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | 0.301 | 0.332 | 0.906 | 0.372 | -0.379 | 0.981 |\n", + "| X1 | 0.043 | 0.307 | 0.138 | 0.891 | -0.586 | 0.671 |\n", "---\n" ] } @@ -468,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -484,8 +489,8 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| Intercept | 2.198 | 0.121 | 18.133 | 0.000 | 1.960 | 2.436 |\n", - "| X1 | 0.366 | 0.107 | 3.437 | 0.001 | 0.157 | 0.575 |\n", + "| Intercept | 2.282 | 0.120 | 19.031 | 0.000 | 2.047 | 2.518 |\n", + "| X1 | 0.280 | 0.107 | 2.609 | 0.009 | 0.069 | 0.491 |\n", "---\n" ] } @@ -512,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -528,9 +533,9 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | -0.008 | 0.045 | -0.177 | 0.859 | -0.097 | 0.081 |\n", + "| X1 | -0.000 | 0.033 | -0.010 | 0.992 | -0.066 | 0.065 |\n", "---\n", - "Deviance: 1068.008\n" + "Deviance: 1068.044\n" ] } ], @@ -554,16 +559,16 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 41, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -575,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -591,10 +596,10 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| Intercept | 2.221 | 0.088 | 25.231 | 0.000 | 2.048 | 2.394 |\n", - "| X1 | 0.343 | 0.067 | 5.090 | 0.000 | 0.211 | 0.476 |\n", + "| Intercept | 2.231 | 0.091 | 24.542 | 0.000 | 2.052 | 2.409 |\n", + "| X1 | 0.333 | 0.070 | 4.745 | 0.000 | 0.195 | 0.471 |\n", "---\n", - "RMSE: 1.765 Adj. R2: 0.022 Adj. R2 Within: 0.022\n", + "RMSE: 1.765 Adj. R2: 0.021 Adj. R2 Within: 0.021\n", "###\n", "\n", "Estimation: OLS\n", @@ -604,9 +609,9 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | 0.271 | 0.056 | 4.812 | 0.000 | 0.161 | 0.382 |\n", + "| X1 | 0.362 | 0.057 | 6.321 | 0.000 | 0.249 | 0.474 |\n", "---\n", - "RMSE: 1.42 Adj. R2: 0.022 Adj. R2 Within: 0.022\n", + "RMSE: 1.422 Adj. R2: 0.038 Adj. R2 Within: 0.038\n", "###\n", "\n", "Estimation: OLS\n", @@ -616,9 +621,9 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | 0.330 | 0.048 | 6.945 | 0.000 | 0.237 | 0.424 |\n", + "| X1 | 0.351 | 0.049 | 7.102 | 0.000 | 0.254 | 0.448 |\n", "---\n", - "RMSE: 1.2 Adj. R2: 0.044 Adj. R2 Within: 0.044\n" + "RMSE: 1.201 Adj. R2: 0.049 Adj. R2 Within: 0.049\n" ] } ], @@ -635,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -674,24 +679,24 @@ " \n", " \n", " Estimate\n", - " 2.221\n", - " 0.343\n", - " 0.271\n", - " 0.330\n", + " 2.231\n", + " 0.333\n", + " 0.362\n", + " 0.351\n", " \n", " \n", " Std. Error\n", - " 0.088\n", - " 0.067\n", - " 0.056\n", - " 0.048\n", + " 0.091\n", + " 0.070\n", + " 0.057\n", + " 0.049\n", " \n", " \n", " t value\n", - " 25.231\n", - " 5.090\n", - " 4.812\n", - " 6.945\n", + " 24.542\n", + " 4.745\n", + " 6.321\n", + " 7.102\n", " \n", " \n", " Pr(>|t|)\n", @@ -702,17 +707,17 @@ " \n", " \n", " 2.5 %\n", - " 2.048\n", - " 0.211\n", - " 0.161\n", - " 0.237\n", + " 2.052\n", + " 0.195\n", + " 0.249\n", + " 0.254\n", " \n", " \n", " 97.5 %\n", - " 2.394\n", - " 0.476\n", - " 0.382\n", - " 0.424\n", + " 2.409\n", + " 0.471\n", + " 0.474\n", + " 0.448\n", " \n", " \n", "\n", @@ -721,15 +726,15 @@ "text/plain": [ "fml Y~X1 Y~X1|f1 Y~X1|f1+f2\n", "Coefficient Intercept X1 X1 X1\n", - "Estimate 2.221 0.343 0.271 0.330\n", - "Std. Error 0.088 0.067 0.056 0.048\n", - "t value 25.231 5.090 4.812 6.945\n", + "Estimate 2.231 0.333 0.362 0.351\n", + "Std. Error 0.091 0.070 0.057 0.049\n", + "t value 24.542 4.745 6.321 7.102\n", "Pr(>|t|) 0.000 0.000 0.000 0.000\n", - "2.5 % 2.048 0.211 0.161 0.237\n", - "97.5 % 2.394 0.476 0.382 0.424" + "2.5 % 2.052 0.195 0.249 0.254\n", + "97.5 % 2.409 0.471 0.474 0.448" ] }, - "execution_count": 43, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -747,7 +752,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -779,21 +784,21 @@ " \n", " \n", " Estimate\n", - " 0.343\n", - " 0.271\n", - " 0.330\n", + " 0.333\n", + " 0.362\n", + " 0.351\n", " \n", " \n", " Std. Error\n", - " 0.067\n", - " 0.056\n", - " 0.048\n", + " 0.070\n", + " 0.057\n", + " 0.049\n", " \n", " \n", " t value\n", - " 5.090\n", - " 4.812\n", - " 6.945\n", + " 4.745\n", + " 6.321\n", + " 7.102\n", " \n", " \n", " Pr(>|t|)\n", @@ -803,15 +808,15 @@ " \n", " \n", " 2.5 %\n", - " 0.211\n", - " 0.161\n", - " 0.237\n", + " 0.195\n", + " 0.249\n", + " 0.254\n", " \n", " \n", " 97.5 %\n", - " 0.476\n", - " 0.382\n", - " 0.424\n", + " 0.471\n", + " 0.474\n", + " 0.448\n", " \n", " \n", "\n", @@ -819,15 +824,15 @@ ], "text/plain": [ "fml Y~X1 Y~X1|f1 Y~X1|f1+f2\n", - "Estimate 0.343 0.271 0.330\n", - "Std. Error 0.067 0.056 0.048\n", - "t value 5.090 4.812 6.945\n", + "Estimate 0.333 0.362 0.351\n", + "Std. Error 0.070 0.057 0.049\n", + "t value 4.745 6.321 7.102\n", "Pr(>|t|) 0.000 0.000 0.000\n", - "2.5 % 0.211 0.161 0.237\n", - "97.5 % 0.476 0.382 0.424" + "2.5 % 0.195 0.249 0.254\n", + "97.5 % 0.471 0.474 0.448" ] }, - "execution_count": 44, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -845,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -889,34 +894,34 @@ " \n", " \n", " Intercept\n", - " 2.220916\n", - " 0.088024\n", - " 25.230741\n", - " 0.000000e+00\n", - " 2.048182\n", - " 2.393650\n", + " 2.230613\n", + " 0.090888\n", + " 24.542465\n", + " 0.000000\n", + " 2.052259\n", + " 2.408967\n", " \n", " \n", " X1\n", - " 0.343246\n", - " 0.067438\n", - " 5.089815\n", - " 4.283430e-07\n", - " 0.210909\n", - " 0.475582\n", + " 0.332869\n", + " 0.070151\n", + " 4.745052\n", + " 0.000002\n", + " 0.195209\n", + " 0.470529\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Estimate Std. Error t value Pr(>|t|) 2.5 % 97.5 %\n", - "Coefficient \n", - "Intercept 2.220916 0.088024 25.230741 0.000000e+00 2.048182 2.393650\n", - "X1 0.343246 0.067438 5.089815 4.283430e-07 0.210909 0.475582" + " Estimate Std. Error t value Pr(>|t|) 2.5 % 97.5 %\n", + "Coefficient \n", + "Intercept 2.230613 0.090888 24.542465 0.000000 2.052259 2.408967\n", + "X1 0.332869 0.070151 4.745052 0.000002 0.195209 0.470529" ] }, - "execution_count": 45, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -934,7 +939,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -985,34 +990,34 @@ " \n", " \n", " Intercept\n", - " 2.220916\n", - " 0.088024\n", - " 25.230741\n", - " 0.000000e+00\n", - " 2.048182\n", - " 2.393650\n", + " 2.230613\n", + " 0.090888\n", + " 24.542465\n", + " 0.000000\n", + " 2.052259\n", + " 2.408967\n", " \n", " \n", " X1\n", - " 0.343246\n", - " 0.067438\n", - " 5.089815\n", - " 4.283430e-07\n", - " 0.210909\n", - " 0.475582\n", + " 0.332869\n", + " 0.070151\n", + " 4.745052\n", + " 0.000002\n", + " 0.195209\n", + " 0.470529\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Estimate Std. Error t value Pr(>|t|) 2.5 % 97.5 %\n", - "Coefficient \n", - "Intercept 2.220916 0.088024 25.230741 0.000000e+00 2.048182 2.393650\n", - "X1 0.343246 0.067438 5.089815 4.283430e-07 0.210909 0.475582" + " Estimate Std. Error t value Pr(>|t|) 2.5 % 97.5 %\n", + "Coefficient \n", + "Intercept 2.230613 0.090888 24.542465 0.000000 2.052259 2.408967\n", + "X1 0.332869 0.070151 4.745052 0.000002 0.195209 0.470529" ] }, - "execution_count": 46, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -1039,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -1055,10 +1060,10 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| Intercept | 2.221 | 0.089 | 24.909 | 0.000 | 2.046 | 2.396 |\n", - "| X1 | 0.343 | 0.069 | 4.987 | 0.000 | 0.208 | 0.478 |\n", + "| Intercept | 2.231 | 0.088 | 25.261 | 0.000 | 2.057 | 2.404 |\n", + "| X1 | 0.333 | 0.069 | 4.807 | 0.000 | 0.197 | 0.469 |\n", "---\n", - "RMSE: 1.765 Adj. R2: 0.022 Adj. R2 Within: 0.022\n", + "RMSE: 1.765 Adj. R2: 0.021 Adj. R2 Within: 0.021\n", "###\n", "\n", "Estimation: OLS\n", @@ -1068,9 +1073,9 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | 0.271 | 0.056 | 4.837 | 0.000 | 0.161 | 0.381 |\n", + "| X1 | 0.362 | 0.057 | 6.369 | 0.000 | 0.250 | 0.473 |\n", "---\n", - "RMSE: 1.42 Adj. R2: 0.022 Adj. R2 Within: 0.022\n", + "RMSE: 1.422 Adj. R2: 0.038 Adj. R2 Within: 0.038\n", "###\n", "\n", "Estimation: OLS\n", @@ -1080,9 +1085,9 @@ "\n", "| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |\n", "|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|\n", - "| X1 | 0.330 | 0.048 | 6.861 | 0.000 | 0.236 | 0.425 |\n", + "| X1 | 0.351 | 0.049 | 7.235 | 0.000 | 0.256 | 0.446 |\n", "---\n", - "RMSE: 1.2 Adj. R2: 0.044 Adj. R2 Within: 0.044\n" + "RMSE: 1.201 Adj. R2: 0.049 Adj. R2 Within: 0.049\n" ] } ], @@ -1099,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -1108,8 +1113,8 @@ "text": [ "| Coefficient | est1 | est2 |\n", "|:--------------|:-----------------|:-----------------|\n", - "| X1 | 0.271*** (0.044) | 0.343*** (0.055) |\n", - "| Intercept | | 2.221*** (0.062) |\n", + "| X1 | 0.362*** (0.072) | 0.333*** (0.061) |\n", + "| Intercept | | 2.231*** (0.079) |\n", "Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001\n" ] } @@ -1130,14 +1135,14 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "
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\n", " \n", @@ -1176,16 +1181,16 @@ { "data": { "text/html": [ - "
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\n", " " + ], + "text/plain": [ + "" ] }, + "execution_count": 90, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" + } + ], + "source": [ + "fit_did2s = did2s(\n", + " df_het,\n", + " yname = \"dep_var\",\n", + " first_stage = \"~ 0 | state + year\",\n", + " second_stage = \"~i(rel_year)\",\n", + " treatment = \"treat\",\n", + " cluster = \"state\",\n", + " i_ref1 = [-1.0, np.inf],\n", + ")\n", + "\n", + "fit_twfe = feols(\n", + " \"dep_var ~ i(rel_year) | state + year\",\n", + " df_het,\n", + " i_ref1 = [-1.0, np.inf],\n", + " vcov = {\"CRV1\": \"state\"}\n", + ")\n", + "\n", + "iplot([fit_did2s, fit_twfe], coord_flip=False, figsize = (900, 400), title = \"TWFE vs DID2S\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `event_study()` function provides a common API for several event study estimators." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| Coefficient | est1 | est2 |\n", + "|:--------------|:-----------------|:-----------------|\n", + "| ATT | 2.135*** (0.044) | 2.152*** (0.048) |\n", + "Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001\n" + ] } ], "source": [ - "plot = fit.iplot(\n", - " yintercept=0,\n", - " figsize=(2000, 600),\n", - " coord_flip=False,\n", - " title=\"Event Study Plot\",\n", - " rotate_xticks=90,\n", + "from pyfixest.experimental.did import event_study\n", + "from pyfixest.summarize import etable\n", + "\n", + "fit_twfe = event_study(\n", + " data = df_het,\n", + " yname = \"dep_var\",\n", + " idname= \"state\",\n", + " tname = \"year\",\n", + " gname = \"g\",\n", + " estimator = \"twfe\"\n", ")\n", - "plot.show()" + "\n", + "fit_did2s = event_study(\n", + " data = df_het,\n", + " yname = \"dep_var\",\n", + " idname= \"state\",\n", + " tname = \"year\",\n", + " gname = \"g\",\n", + " estimator = \"did2s\"\n", + ")\n", + "\n", + "etable([fit_twfe, fit_did2s])" ] }, { From 47d35af21ca1ab42c33f57ec1bd6844f31072c52 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 5 Nov 2023 16:58:00 +0100 Subject: [PATCH 39/43] update i-tests --- tests/test_i.py | 46 ++++++++++++++++++++++++++++------------------ 1 file changed, 28 insertions(+), 18 deletions(-) diff --git a/tests/test_i.py b/tests/test_i.py index 7fc8768d..20f24fb2 100644 --- a/tests/test_i.py +++ b/tests/test_i.py @@ -2,38 +2,48 @@ import numpy as np import pandas as pd from pyfixest.estimation import feols +from pyfixest.exceptions import InvalidReferenceLevelError def test_i(): df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") df_het["X"] = np.random.normal(size = len(df_het)) - df_het["rel_year"] = df_het["rel_year"].astype("category") - df_het["treat"] = df_het["treat"].astype("int") - feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = 1.0) - feols("dep_var~i(rel_year, year)", df_het, i_ref1 = 1.0) - feols("dep_var~i(rel_year)", df_het, i_ref1 = 1.0) + if 'C(rel_year)[T.1.0]' in feols("dep_var~i(rel_year)", df_het, i_ref1 = 1.0)._coefnames: + raise AssertionError("C(rel_year)[T.1.0] should not be in the column names.") + if 'C(rel_year)[T.-2.0]' in feols("dep_var~i(rel_year)", df_het, i_ref1 = -2.0)._coefnames: + raise AssertionError("C(rel_year)[T.-2.0] should not be in the column names.") + if 'C(rel_year)[T.1.0]' in feols("dep_var~i(rel_year)", df_het, i_ref1 = [1.0, 2.0])._coefnames: + raise AssertionError("C(rel_year)[T.1.0] should not be in the column names.") + if 'C(rel_year)[T.2.0]'in feols("dep_var~i(rel_year)", df_het, i_ref1 = [1.0, 2.0])._coefnames: + raise AssertionError("C(rel_year)[T.2.0] should not be in the column names.") - feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = [1.0, 2.0]) - feols("dep_var~i(rel_year, year)", df_het, i_ref1 = [1.0, 2.0]) - feols("dep_var~i(rel_year)", df_het, i_ref1 = [1.0, 2.0]) + if 'C(rel_year)[T.1.0]:treat' in feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = 1.0)._coefnames: + raise AssertionError("C(rel_year)[T.1.0]:treat should not be in the column names.") + if 'C(rel_year)[T.-2.0]:treat' in feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = -2.0)._coefnames: + raise AssertionError("C(rel_year)[T.-2.0]:treat should not be in the column names.") + if 'C(rel_year)[T.1.0]:treat' in feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = [1.0, 2.0])._coefnames: + raise AssertionError("C(rel_year)[T.1.0]:treat should not be in the column names.") + if 'C(rel_year)[T.2.0]:treat'in feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = [1.0, 2.0])._coefnames: + raise AssertionError("C(rel_year)[T.2.0]:treat should not be in the column names.") - feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = [1.0, 2.0], i_ref2 = [1.0]) - feols("dep_var~i(rel_year, treat)", df_het) - feols("dep_var~i(rel_year, year)", df_het) - feols("dep_var~i(rel_year)", df_het) - with pytest.raises(ValueError): + with pytest.raises(InvalidReferenceLevelError): feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = "1.0") - with pytest.raises(ValueError): + with pytest.raises(InvalidReferenceLevelError): + feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = [1]) + with pytest.raises(InvalidReferenceLevelError): + feols("dep_var~i(rel_year, X)", df_het, i_ref1 = [1, 2]) + with pytest.raises(AssertionError): + feols("dep_var~i(rel_year, X)", df_het, i_ref1 = [1.0, "a"]) + + # i_ref2 currently not supported + with pytest.raises(AssertionError): feols("dep_var~i(rel_year, treat)", df_het, i_ref2 = "1.0") - with pytest.raises(ValueError): - feols("dep_var~i(rel_year, treat)", df_het, i_ref1 = [1.0], i_ref2 = "1.0") - with pytest.raises(ValueError): - feols("dep_var~i(rel_year, X)", df_het, i_ref1 = [1.0, 2.0], i_ref2 = [1.0]) + From 302413e62cd0e0875661086f1d26fab5e7475c2f Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 5 Nov 2023 17:23:31 +0100 Subject: [PATCH 40/43] bump version and update readme --- .gitignore | 4 +- poetry.lock | 600 +++++++++++++++++------------------------ pyfixest/estimation.py | 6 +- pyproject.toml | 2 +- readme.html | 350 ++++++++++++++++++++++++ readme.md | 39 ++- tests/test_did.py | 67 +++-- 7 files changed, 696 insertions(+), 372 deletions(-) create mode 100644 readme.html diff --git a/.gitignore b/.gitignore index 5520d60a..2afb1d14 100644 --- a/.gitignore +++ b/.gitignore @@ -10,4 +10,6 @@ dist .pytest_cache .vscode *.pyc -__pycache__/ \ No newline at end of file +__pycache__/ +figures +readme_files \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index b48a1e87..2ee175b1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -28,47 +28,29 @@ files = [ six = ">=1.6.1,<2.0" wheel = ">=0.23.0,<1.0" -[[package]] -name = "attrs" -version = "23.1.0" -description = "Classes Without Boilerplate" -category = "main" -optional = true -python-versions = ">=3.7" -files = [ - 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--git a/pyfixest/estimation.py b/pyfixest/estimation.py index adf9e5af..45284bc4 100644 --- a/pyfixest/estimation.py +++ b/pyfixest/estimation.py @@ -48,7 +48,8 @@ def feols( - i() for interaction of a categorical and non-categorical variable (e.g. "i(X1,X2)" for interaction between X1 and X2). Using i() is required to use with some custom methods, e.g. iplot(). In contrast to r-fixest, reference levels cannot be - set in the formula, but must be specified via the i_ref1 and i_ref2 arguments. + set in the formula, but must be specified via the i_ref1 and i_ref2 arguments. The first variable - in the example, X1 - + will always be treated as a categorical. - ^ for interacted fixed effects (e.g. "fe1^fe2" for interaction between fe1 and fe2) All other parts of the formula must be compatible with formula parsing via the formulaic module. @@ -185,7 +186,8 @@ def fepois( - i() for interaction of a categorical and non-categorical variable (e.g. "i(X1,X2)" for interaction between X1 and X2). Using i() is required to use with some custom methods, e.g. iplot(). In contrast to r-fixest, reference levels cannot be - set in the formula, but must be specified via the i_ref1 and i_ref2 arguments. + set in the formula, but must be specified via the i_ref1 and i_ref2 arguments. The first variable - in the example, X1 - + will always be treated as a categorical. - ^ for interacted fixed effects (e.g. "fe1^fe2" for interaction between fe1 and fe2) All other parts of the formula must be compatible with formula parsing via the formulaic module. diff --git a/pyproject.toml b/pyproject.toml index 1aed16b4..fecdb0a2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "pyfixest" -version = "0.10.7" +version = "0.10.8" description = "Fast high dimensional fixed effect estimation following syntax of the fixest R package. Supports OLS, IV and Poisson regression and a range of inference procedures. Additionally, experimentally supports (some of) the regression based new Difference-in-Differences Estimators (Did2s)." diff --git a/readme.html b/readme.html new file mode 100644 index 00000000..5c609e07 --- /dev/null +++ b/readme.html @@ -0,0 +1,350 @@ + + + + + + + + + +readme + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+

PyFixest

+

PyPI - Version PyPI - Python Version PyPI - Downloads image

+

PyFixest is a Python clone of the excellent fixest package. The package aims to mimic fixest syntax and functionality as closely as Python allows. For a quick introduction, see the tutorial.

+
+
+

Functionality

+

At the moment, PyFixest supports

+
    +
  • OLS and IV Regression
    • +
  • Poisson Regression
    • +
  • Multiple Estimation Syntax
    • +
  • Several Robust and Cluster Robust Variance-Covariance Types
    • +
  • Wild Cluster Bootstrap Inference (via wildboottest)
    • +
  • Support for estimators of the “new” Difference-in-Difference literature is work in progress. +
      +
    • Gardner’s two-stage (“Did2s”) estimator is available via the pyfixest.experimental.did module
      • +
    • +
+
+
+

News

+

PyFixest 0.10.8 adds experimental support for Gardner’s two stage “DID2s” estimator:

+
from pyfixest.experimental.did import did2s
+from pyfixest.estimation import feols
+from pyfixest.visualize import iplot
+import pandas as pd
+import numpy as np
+
+df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv")
+df_het["X"] = np.random.normal(size = len(df_het))#
+df_het["rel_year"] = df_het["rel_year"].astype("category")
+
+fit = did2s(
+    df_het,
+    yname = "dep_var",
+    first_stage = "~ 0 | state + year",
+    second_stage = "~i(rel_year)",
+    treatment = "treat",
+    cluster = "state",
+    i_ref1 = [-1.0, np.inf],
+)
+
+fit_twfe = feols(
+    "dep_var ~ i(rel_year) | state + year",
+    df_het,
+    i_ref1 = [-1.0, np.inf]
+)
+
+iplot([fit, fit_twfe], coord_flip=False, figsize = (900, 400), title = "TWFE vs DID2S")
+

+
+
+

Installation

+

You can install the release version from PyPi by running pip install pyfixest or the development version from github.

+
+
+

Quickstart

+
from pyfixest.estimation import feols
+from pyfixest.utils import get_data
+
+data = get_data()
+
+# OLS Estimation
+fit = feols("Y~X1 | csw0(f1, f2)", data = data, vcov = {'CRV1':'group_id'})
+fit.summary()
+
+# ###
+#
+# Model:  OLS
+# Dep. var.:  Y
+# Inference:  CRV1
+# Observations:  998
+#
+# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+# | Intercept     |      2.206 |        0.078 |    28.304 |      0.000 |   2.043 |    2.370 |
+# | X1            |      0.358 |        0.051 |     6.962 |      0.000 |   0.250 |    0.466 |
+# ---
+# RMSE: 1.765  Adj. R2: 0.024  Adj. R2 Within: 0.024
+# ###
+#
+# Model:  OLS
+# Dep. var.:  Y
+# Fixed effects:  f1
+# Inference:  CRV1
+# Observations:  997
+#
+# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+# | X1            |      0.411 |        0.040 |    10.188 |      0.000 |   0.326 |    0.495 |
+# ---
+# RMSE: 1.421  Adj. R2: 0.048  Adj. R2 Within: 0.048
+# ###
+#
+# Model:  OLS
+# Dep. var.:  Y
+# Fixed effects:  f1+f2
+# Inference:  CRV1
+# Observations:  997
+#
+# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+# | X1            |      0.431 |        0.035 |    12.319 |      0.000 |   0.358 |    0.505 |
+# ---
+# RMSE: 1.2  Adj. R2: 0.07  Adj. R2 Within: 0.07
+

Standard Errors can be adjusted after estimation, “on-the-fly”:

+
fit1 = fit.fetch_model(0)
+fit1.vcov("hetero").tidy()
+# Model:  Y~X1
+# ###
+#
+# Model:  OLS
+# Dep. var.:  Y
+# Inference:  hetero
+# Observations:  998
+#
+# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+# | Intercept     |      2.206 |        0.088 |    25.180 |      0.000 |   2.034 |    2.378 |
+# | X1            |      0.358 |        0.068 |     5.254 |      0.000 |   0.224 |    0.491 |
+# ---
+# RMSE: 1.765  Adj. R2: 0.024  Adj. R2 Within: 0.024
+

Last, PyFixest also supports IV estimation via three part formula syntax:

+
fit_iv = feols("Y ~ 1 | f1 | X1 ~ Z1", data = data)
+fit_iv.summary()
+
+# ###
+#
+# Model:  IV
+# Dep. var.:  Y
+# Fixed effects:  f1
+# Inference:  CRV1
+# Observations:  997
+#
+# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+# | X1            |      0.479 |        0.096 |     4.979 |      0.000 |   0.282 |    0.676 |
+# ---
+
+ +
+ + +
+ + + + \ No newline at end of file diff --git a/readme.md b/readme.md index e75e2f8e..a8d8ec1b 100644 --- a/readme.md +++ b/readme.md @@ -5,7 +5,7 @@ ![PyPI - Downloads](https://img.shields.io/pypi/dm/pyfixest) [![image](https://codecov.io/gh/s3alfisc/pyfixest/branch/master/graph/badge.svg)](https://codecov.io/gh/s3alfisc/pyfixest) -This is a draft package (no longer highly experimental) for a Python clone of the excellent [fixest](https://github.com/lrberge/fixest) package. The package aims to mimic `fixest` syntax and functionality as closely as possible. Fixed effects are projected out via the [PyHDFE](https://github.com/jeffgortmaker/pyhdfe) package. For a quick introduction, see the [tutorial](https://s3alfisc.github.io/pyfixest/tutorial/). +`PyFixest` is a Python clone of the excellent [fixest](https://github.com/lrberge/fixest) package. The package aims to mimic `fixest` syntax and functionality as closely as Python allows. For a quick introduction, see the [tutorial](https://s3alfisc.github.io/pyfixest/tutorial/). ## Functionality @@ -16,8 +16,41 @@ At the moment, `PyFixest` supports - Multiple Estimation Syntax - Several Robust and Cluster Robust Variance-Covariance Types - Wild Cluster Bootstrap Inference (via [wildboottest](https://github.com/s3alfisc/wildboottest)) -- Support for estimators of the "new" Difference-in-Difference literature is work in progress. `PyFixest` currently provides an - experimental implementation of Gardner's Did2s estimtator (via the `pyfixest.experimental.did` module, only ATT estimation). +- Support for estimators of the "new" Difference-in-Difference literature is work in progress. + - Gardner's two-stage ("`Did2s`") estimator is available via the `pyfixest.experimental.did` module + +## News + +`PyFixest` 0.10.8 adds experimental support for Gardner's two stage "DID2s" estimator: + +```py +from pyfixest.experimental.did import did2s +from pyfixest.estimation import feols +from pyfixest.visualize import iplot +import pandas as pd +import numpy as np + +df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") + +fit = did2s( + df_het, + yname = "dep_var", + first_stage = "~ 0 | state + year", + second_stage = "~i(rel_year)", + treatment = "treat", + cluster = "state", + i_ref1 = [-1.0, np.inf], +) + +fit_twfe = feols( + "dep_var ~ i(rel_year) | state + year", + df_het, + i_ref1 = [-1.0, np.inf] +) + +iplot([fit, fit_twfe], coord_flip=False, figsize = (900, 400), title = "TWFE vs DID2S") +``` +![](./figures/event_study.svg) ## Installation diff --git a/tests/test_did.py b/tests/test_did.py index 0b0e5fd7..2680d063 100644 --- a/tests/test_did.py +++ b/tests/test_did.py @@ -96,13 +96,13 @@ def test_did2s(): second_stage = "~i(rel_year)", treatment = "treat", cluster = "state", - i_ref1 = [-1.0], + i_ref1 = [-1.0, np.inf], ) fit_r = did2s.did2s( data = df_het, yname = "dep_var", first_stage = ro.Formula("~ 0 | state + year"), - second_stage = ro.Formula("~ i(rel_year, ref = c(-1))"), treatment = "treat", + second_stage = ro.Formula("~ i(rel_year, ref = c(-1, Inf))"), treatment = "treat", cluster_var = "state" ) @@ -113,10 +113,12 @@ def test_did2s(): fit.coef(), stats.coef(fit_r) ) np.testing.assert_allclose( - fit.se(), float(did2s_df[2]) + fit.se(), did2s_df[2].values.astype(float) ) - # test event study with covariate + df_het["X"] = np.random.normal(size = len(df_het)) + + # test event study with covariate in first stage fit = did2s_pyfixest( df_het, yname = "dep_var", @@ -127,22 +129,49 @@ def test_did2s(): i_ref1 = [-1.0, np.inf], ) - if False: - fit_r = did2s.did2s( - data = df_het, - yname = "dep_var", first_stage = ro.Formula("~ X | state + year"), - second_stage = ro.Formula("~ i(rel_year, ref = c(-1, Inf))"), treatment = "treat", - cluster_var = "state" - ) + fit_r = did2s.did2s( + data = df_het, + yname = "dep_var", first_stage = ro.Formula("~ X | state + year"), + second_stage = ro.Formula("~ i(rel_year, ref = c(-1, Inf))"), treatment = "treat", + cluster_var = "state" + ) - did2s_df = broom.tidy_fixest(fit_r, conf_int=ro.BoolVector([True])) - did2s_df = pd.DataFrame(did2s_df).T + did2s_df = broom.tidy_fixest(fit_r, conf_int=ro.BoolVector([True])) + did2s_df = pd.DataFrame(did2s_df).T - np.testing.assert_allclose( - fit.coef(), stats.coef(fit_r) - ) - np.testing.assert_allclose( - fit.se(), float(did2s_df[2]) - ) + np.testing.assert_allclose( + fit.coef(), stats.coef(fit_r) + ) + np.testing.assert_allclose( + fit.se(), did2s_df[2].values.astype(float) + ) + + # test event study with covariate in first stage and second stage + fit = did2s_pyfixest( + df_het, + yname = "dep_var", + first_stage = "~ X | state + year", + second_stage = "~ X + i(rel_year)", + treatment = "treat", + cluster = "state", + i_ref1 = [-1.0, np.inf], + ) + + fit_r = did2s.did2s( + data = df_het, + yname = "dep_var", first_stage = ro.Formula("~ X | state + year"), + second_stage = ro.Formula("~ X + i(rel_year, ref = c(-1, Inf))"), treatment = "treat", + cluster_var = "state" + ) + + did2s_df = broom.tidy_fixest(fit_r, conf_int=ro.BoolVector([True])) + did2s_df = pd.DataFrame(did2s_df).T + + np.testing.assert_allclose( + fit.coef(), stats.coef(fit_r) + ) + np.testing.assert_allclose( + fit.se(), did2s_df[2].values.astype(float) + ) From efb30ed9c0cf62f6e32499a9bdf9135054550108 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 5 Nov 2023 17:25:05 +0100 Subject: [PATCH 41/43] readme tweaks --- readme.html | 40 +++++++++++++++++++--------------------- readme.md | 2 +- 2 files changed, 20 insertions(+), 22 deletions(-) diff --git a/readme.html b/readme.html index 5c609e07..184ed2f3 100644 --- a/readme.html +++ b/readme.html @@ -117,7 +117,7 @@

Functionality

  • Multiple Estimation Syntax
  • Several Robust and Cluster Robust Variance-Covariance Types
  • Wild Cluster Bootstrap Inference (via wildboottest)
    • -
  • Support for estimators of the “new” Difference-in-Difference literature is work in progress. +
  • Difference-in-Difference Estimators:
    • Gardner’s two-stage (“Did2s”) estimator is available via the pyfixest.experimental.did module
    • @@ -133,26 +133,24 @@

    News

    import numpy as np df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") -df_het["X"] = np.random.normal(size = len(df_het))# -df_het["rel_year"] = df_het["rel_year"].astype("category") - -fit = did2s( - df_het, - yname = "dep_var", - first_stage = "~ 0 | state + year", - second_stage = "~i(rel_year)", - treatment = "treat", - cluster = "state", - i_ref1 = [-1.0, np.inf], -) - -fit_twfe = feols( - "dep_var ~ i(rel_year) | state + year", - df_het, - i_ref1 = [-1.0, np.inf] -) - -iplot([fit, fit_twfe], coord_flip=False, figsize = (900, 400), title = "TWFE vs DID2S") + +fit = did2s( + df_het, + yname = "dep_var", + first_stage = "~ 0 | state + year", + second_stage = "~i(rel_year)", + treatment = "treat", + cluster = "state", + i_ref1 = [-1.0, np.inf], +) + +fit_twfe = feols( + "dep_var ~ i(rel_year) | state + year", + df_het, + i_ref1 = [-1.0, np.inf] +) + +iplot([fit, fit_twfe], coord_flip=False, figsize = (900, 400), title = "TWFE vs DID2S")

    diff --git a/readme.md b/readme.md index a8d8ec1b..7bf32ffa 100644 --- a/readme.md +++ b/readme.md @@ -16,7 +16,7 @@ At the moment, `PyFixest` supports - Multiple Estimation Syntax - Several Robust and Cluster Robust Variance-Covariance Types - Wild Cluster Bootstrap Inference (via [wildboottest](https://github.com/s3alfisc/wildboottest)) -- Support for estimators of the "new" Difference-in-Difference literature is work in progress. +- Difference-in-Difference Estimators: - Gardner's two-stage ("`Did2s`") estimator is available via the `pyfixest.experimental.did` module ## News From 257aafa8dad3c5d5967b43181f8523985a727251 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 5 Nov 2023 17:42:24 +0100 Subject: [PATCH 42/43] update news --- .gitignore | 2 + docs/news.html | 436 +++++++++++++++++++++++++++++++++++++++++++++++++ docs/news.md | 40 +++++ 3 files changed, 478 insertions(+) create mode 100644 docs/news.html diff --git a/.gitignore b/.gitignore index 2afb1d14..977149ba 100644 --- a/.gitignore +++ b/.gitignore @@ -12,4 +12,6 @@ dist *.pyc __pycache__/ figures +docs/figures +docs/news_files readme_files \ No newline at end of file diff --git a/docs/news.html b/docs/news.html new file mode 100644 index 00000000..b696729a --- /dev/null +++ b/docs/news.html @@ -0,0 +1,436 @@ + + + + + + + + + +news + + + + + + + + + + + + + + + + + + + +
    + +
    + + + +
    +

    News

    +
    +

    PyFixest 0.10.8

    +
    +

    Breaking changes

    +

    Reference levels for the i() formula syntax can no longer be set within the formula, but need to be added via the i_ref1 function argument to either feols() and fepois().

    +
    +
    +

    New feature

    +

    A dids2() function is added, which implements the 2-stage difference-in-differences procedure à la Gardner and follows the syntax of @kylebutts did2s R package.

    +
    from pyfixest.experimental.did import did2s
+from pyfixest.estimation import feols
+from pyfixest.visualize import iplot
+import pandas as pd
+import numpy as np
+
+df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv")
+
+fit = did2s(
+    df_het,
+    yname = "dep_var",
+    first_stage = "~ 0 | state + year",
+    second_stage = "~i(rel_year)",
+    treatment = "treat",
+    cluster = "state",
+    i_ref1 = [-1.0, np.inf],
+)
+
+fit_twfe = feols(
+    "dep_var ~ i(rel_year) | state + year",
+    df_het,
+    i_ref1 = [-1.0, np.inf]
+)
+
+iplot([fit, fit_twfe], coord_flip=False, figsize = (900, 400), title = "TWFE vs DID2S")
    +

    +
    +
    +
    +

    PyFixest 0.10.7

    +
      +
    • Adds basic support for event study estimation via two-way fixed effects and Gardner’s two-stage “Did2s” approach. This is a beta version and experimental. Further updates (i.e. proper event studies vs “only” ATTs) and a more flexible did2s front end will follow in future releases.
      • +
    +
    %load_ext autoreload
+%autoreload 2
+
+from pyfixest.experimental.did import event_study
+from pyfixest.summarize import etable
+import pandas as pd
+df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv")
+
+fit_twfe = event_study(
+    data = df_het,
+    yname = "dep_var",
+    idname= "state",
+    tname = "year",
+    gname = "g",
+    estimator = "twfe"
+)
+
+fit_did2s = event_study(
+    data = df_het,
+    yname = "dep_var",
+    idname= "state",
+    tname = "year",
+    gname = "g",
+    estimator = "did2s"
+)
+
+etable([fit_twfe, fit_did2s])
+# | Coefficient   | est1             | est2             |
+# |:--------------|:-----------------|:-----------------|
+# | ATT           | 2.135*** (0.044) | 2.152*** (0.048) |
+# Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001
    +
    +
    +

    PyFixest 0.10.6

    +
      +
    • Adds an etable() function that outputs markdown, latex or a pd.DataFrame.
      • +
    +
    +
    +

    PyFixest 0.10.5

    +
      +
    • Fixes a big in IV estimation that would trigger an error. See here for details. Thanks to @aeturrell for reporting!
      • +
    +
    +
    +

    PyFixest 0.10.4

    +
      +
    • Implements a custom function to drop singleton fixed effects.
      • +
    • Additional small performance improvements.
      • +
    +
    +
    +

    PyFixest 0.10.3

    +
      +
    • Allows for white space in the multiway clustering formula.
      • +
    • Adds documentation for multiway clustering.
      • +
    +
    +
    +

    PyFixest 0.10.2

    +
      +
    • Adds support for two-way clustering.
      • +
    • Adds support for CRV3 inference for Poisson regression.
      • +
    +
    +
    +

    PyFixest 0.10.1

    +
      +
    • Adapts the internal fixed effects demeaning criteron to match PyHDFE’s default.
      • +
    • Adds Styfen as coauthor.
      • +
    +
    +
    +

    PyFixest 0.10

    +
      +
    • Multiple performance improvements.
      • +
    • Most importantly, implements a custom demeaning algorithm in numba - thanks to Styfen Schaer (@styfenschaer), which leads to performance improvements of 5x or more:
      • +
    +
    %load_ext autoreload
+%autoreload 2
+
+import numpy as np
+import time
+import pyhdfe
+from pyfixest.demean import demean
+
+np.random.seed(1238)
+N = 10_000_000
+x = np.random.normal(0, 1, 10*N).reshape((N,10))
+f1 = np.random.choice(list(range(1000)), N).reshape((N,1))
+f2 = np.random.choice(list(range(1000)), N).reshape((N,1))
+
+flist = np.concatenate((f1, f2), axis = 1)
+weights = np.ones(N)
+
+algorithm = pyhdfe.create(flist)
+
+start_time = time.time()
+res_pyhdfe = algorithm.residualize(x)
+end_time = time.time()
+print(end_time - start_time)
+# 26.04527711868286
+
+
+start_time = time.time()
+res_pyfixest, success = demean(x, flist, weights, tol = 1e-10)
+# Calculate the execution time
+end_time = time.time()
+print(end_time - start_time)
+#4.334428071975708
+
+np.allclose(res_pyhdfe , res_pyfixest)
+# True
    +
    +
    +

    PyFixest 0.9.11

    +
      +
    • Bump required formulaic version to 0.6.5.
      • +
    • Stop copying the data frame in fixef().
      • +
    +
    +
    +

    PyFixest 0.9.10

    +
      +
    • Fixes a big in the wildboottest method (see #158).
      • +
    • Allows to run a wild bootstrap after fixed effect estimation.
      • +
    +
    +
    +

    PyFixest 0.9.9

    +
      +
    • Adds support for wildboottest for Python 3.11.
      • +
    +
    +
    +

    PyFixest 0.9.8

    +
      +
    • Fixes a couple more bugs in the predict() and fixef() methods.
      • +
    • The predict() argument data is renamed to newdata.
      • +
    +
    +
    +

    PyFixest 0.9.7

    +

    Fixes a bug in predict() produced when multicollinear variables are dropped.

    +
    +
    +

    PyFixest 0.9.6

    +

    Improved Collinearity handling. See #145

    +
    +
    +

    PyFixest 0.9.5

    +
      +
    • Moves plotting from matplotlin to lets-plot.
      • +
    • Fixes a few minor bugs in plotting and the fixef() method.
      • +
    +
    +
    +

    PyFixest 0.9.1

    +
    +

    Breaking API changes

    +

    It is no longer required to initiate an object of type Fixest prior to running feols or fepois. Instead, you can now simply use feols() and fepois() as functions, just as in fixest. Both function can be found in an estimation module and need to obtain a pd.DataFrame as a function argument:

    +
    from pyfixest.estimation import fixest, fepois
+from pyfixest.utils import get_data
+
+data = get_data()
+fit = feols("Y ~ X1 | f1", data = data, vcov = "iid")
    +

    Calling feols() will return an instance of class Feols, while calling fepois() will return an instance of class Fepois. Multiple estimation syntax will return an instance of class FixestMulti.

    +

    Post processing works as before via .summary(), .tidy() and other methods.

    +
    +
    +

    New Features

    +

    A summary function allows to compare multiple models:

    +
    from pyfixest.summarize import summary
+fit2 = feols("Y ~ X1 + X2| f1", data = data, vcov = "iid")
+summary([fit, fit2])
    +

    Visualization is possible via custom methods (.iplot() & .coefplot()), but a new module allows to visualize a list of Feols and/or Fepois instances:

    +
    from pyfixest.visualize import coefplot, iplot
+coefplot([fit, fit2])
    +

    The documentation has been improved (though there is still room for progress), and the code has been cleaned up a bit (also lots of room for improvements).

    +
    +
    +
    + +
    + + +
    + + + + \ No newline at end of file diff --git a/docs/news.md b/docs/news.md index 0195ea38..7135a756 100644 --- a/docs/news.md +++ b/docs/news.md @@ -1,5 +1,45 @@ # News +## PyFixest `0.10.8` + +### Breaking changes +Reference levels for the `i()` formula syntax can no longer be set within the formula, but need to be added via the `i_ref1` function argument to either `feols()` and `fepois()`. + +### New feature + +A `dids2()` function is added, which implements the 2-stage difference-in-differences procedure à la Gardner and follows the syntax of @kylebutts [did2s](https://github.com/kylebutts/did2s) R package. + +```py +from pyfixest.experimental.did import did2s +from pyfixest.estimation import feols +from pyfixest.visualize import iplot +import pandas as pd +import numpy as np + +df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") + +fit = did2s( + df_het, + yname = "dep_var", + first_stage = "~ 0 | state + year", + second_stage = "~i(rel_year)", + treatment = "treat", + cluster = "state", + i_ref1 = [-1.0, np.inf], +) + +fit_twfe = feols( + "dep_var ~ i(rel_year) | state + year", + df_het, + i_ref1 = [-1.0, np.inf] +) + +iplot([fit, fit_twfe], coord_flip=False, figsize = (900, 400), title = "TWFE vs DID2S") +``` +![](figures/event_study.svg) + + + ## PyFixest `0.10.7` - Adds basic support for event study estimation via two-way fixed effects and Gardner's two-stage "Did2s" approach. From 3d89638c9a628734b5f55cd38d0ea28b92379363 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sun, 5 Nov 2023 17:53:25 +0100 Subject: [PATCH 43/43] update readme --- docs/news.md | 2 +- readme.md | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/news.md b/docs/news.md index 7135a756..d9ccb14b 100644 --- a/docs/news.md +++ b/docs/news.md @@ -16,7 +16,7 @@ from pyfixest.visualize import iplot import pandas as pd import numpy as np -df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") +df_het = pd.read_csv("https://raw.githubusercontent.com/s3alfisc/pyfixest/master/pyfixest/experimental/data/df_het.csv") fit = did2s( df_het, diff --git a/readme.md b/readme.md index 7bf32ffa..b4037d81 100644 --- a/readme.md +++ b/readme.md @@ -30,7 +30,8 @@ from pyfixest.visualize import iplot import pandas as pd import numpy as np -df_het = pd.read_csv("pyfixest/experimental/data/df_het.csv") +# download csv from this repo +df_het = pd.read_csv("https://raw.githubusercontent.com/s3alfisc/pyfixest/master/pyfixest/experimental/data/df_het.csv") fit = did2s( df_het,