In the working paper titled "Why You Should Never Use the Hodrick-Prescott Filter", James D. Hamilton proposes a new alternative to economic time series filtering. The neverhpfilter package provides functions and data for reproducing his solution. Hamilton (2017) doi:10.3386/w23429
Hamilton's abstract offers an excellent introduction:
(1) The HP filter produces series with spurious dynamic relations that have no basis in the underlying data-generating process. (2) Filtered values at the end of the sample are very different from those in the middle, and are also characterized by spurious dynamics. (3) A statistical formalization of the problem typically produces values for the smoothing parameter vastly at odds with common practice, e.g., a value for
$\lambda$ far below 1600 for quarterly data. (4) There's a better alternative. A regression of the variable at date$t + h$ on the four most recent values as of date$t$ offers a robust approach to detrending that achieves all the objectives sought by users of the HP filter with none of its drawbacks.
Install from CRAN on R version >= 3.5.0.
install.packages("neverhpfilter")
Or install from the Github master branch on R version >= 3.5.0.
devtools::install_github("JustinMShea/neverhpfilter")
Load the package
library(neverhpfilter)
The package consists of 2 estimation functions, 12 economic xts objects, an xts object containing Robert Shiller's U.S. Stock Markets and CAPE Ratio data from 1871 to Present, and a data.frame containing the original filter estimates found on table 2 of Hamilton (2017) doi:10.3386/w23429
Documentation for each can be found here:
Finally, a vignette recreating the estimates of the original work can be found in Reproducing Hamilton.