PyFixest 0.10.3

.github/workflows/ci-tests.yaml

@@ -14,7 +14,7 @@ jobs:
   test:
     runs-on: ${{ matrix.os }}
     strategy:
-      fail-fast: true
+      fail-fast: false
       matrix:
         os: ["ubuntu-latest"]
         python-version: ["3.8", "3.9", "3.10", "3.11"]

docs/news.md

@@ -1,8 +1,14 @@
 # News
 
+## 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`
 

pyfixest/demean.py

@@ -135,8 +135,7 @@ def demean_model(
 @nb.njit
 def _sad_converged(a, b, tol):
     for i in range(a.size):
-        tol -= np.abs(a[i] - b[i])
-        if tol < 0:
+        if np.abs(a[i] - b[i]) >= tol:
             return False
     return True
 

pyfixest/estimation.py

@@ -56,6 +56,7 @@ def feols(
         vcov (Union(str, dict[str, str])): A string or dictionary specifying the type of variance-covariance matrix to use for inference.
             If a string, it can be one of "iid", "hetero", "HC1", "HC2", "HC3".
             If a dictionary, it should have the format dict("CRV1":"clustervar") for CRV1 inference or dict(CRV3":"clustervar") for CRV3 inference.
+            For twoway clustering, combine the cluster variables via a "+", i.e. dict(".CRV1":"clustervar1+clustervar2").
 
         ssc (str): A ssc object specifying the small sample correction to use for inference. See the documentation for sscc() for more information.
 
@@ -184,6 +185,7 @@ def fepois(
         vcov (Union(str, dict[str, str])): A string or dictionary specifying the type of variance-covariance matrix to use for inference.
             If a string, it can be one of "iid", "hetero", "HC1", "HC2", "HC3".
             If a dictionary, it should have the format dict("CRV1":"clustervar") for CRV1 inference or dict(CRV3":"clustervar") for CRV3 inference.
+            For twoway clustering, combine the cluster variables via a "+", i.e. dict(".CRV1":"clustervar1+clustervar2").
 
         ssc (string): A ssc object specifying the small sample correction to use for inference. See the documentation for sscc() for more information.
 

pyfixest/feols.py

@@ -1041,7 +1041,7 @@ def _check_vcov_input(vcov, data):
             list(vcov.values())[0], str
         ), "vcov dict value must be a string"
         deparse_vcov = list(vcov.values())[0].split("+")
-        assert all(col in data.columns for col in deparse_vcov), "vcov dict value must be a column in the data"
+        assert all(col.replace(" ","") in data.columns for col in deparse_vcov), "vcov dict value must be a column in the data"
 
         assert (
             len(deparse_vcov) <= 2
@@ -1082,6 +1082,7 @@ def _deparse_vcov_input(vcov, has_fixef, is_iv):
         deparse_vcov = list(vcov.values())[0].split("+")
         if isinstance(deparse_vcov, str):
             deparse_vcov = [deparse_vcov]
+        deparse_vcov = [x.replace(" ", "") for x in deparse_vcov]
     elif isinstance(vcov, list):
         vcov_type_detail = vcov
     elif isinstance(vcov, str):

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pyfixest"
-version = "0.10.2"
+version = "0.10.3"
 
 
 description = "(Not so) experimental draft package for high dimensional fixed effect estimation. Supports OLS, IV and Poisson regression and a range of inference procedures."

tests/test_vs_fixest.py

@@ -465,8 +465,8 @@ def test_twoway_clustering():
     for cluster_adj in cluster_adj_options:
         for cluster_df in cluster_df_options:
 
-            fit1 = feols("Y ~ X1 + X2 ", data=data, vcov = {"CRV1":"f1+f2"}, ssc = ssc(cluster_adj = cluster_adj, cluster_df = cluster_df))
-            fit2 = feols("Y ~ X1 + X2 ", data=data, vcov = {"CRV3":"f1+f2"}, ssc = ssc(cluster_adj = cluster_adj, cluster_df = cluster_df))
+            fit1 = feols("Y ~ X1 + X2 ", data=data, vcov = {"CRV1":"f1 +f2"}, ssc = ssc(cluster_adj = cluster_adj, cluster_df = cluster_df))
+            fit2 = feols("Y ~ X1 + X2 ", data=data, vcov = {"CRV3":" f1+f2"}, ssc = ssc(cluster_adj = cluster_adj, cluster_df = cluster_df))
 
             feols_fit1 = fixest.feols(
                 ro.Formula("Y ~ X1 + X2"),