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 work. Hamilton (2017)
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.