Why You Should Never Use the Hodrick-Prescott Filter
Here's why. (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 λ 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.
I thank Daniel Leff for outstanding research assistance on this project and Frank Diebold, Robert King, James Morley, and anonymous referees for helpful comments on an earlier draft of this paper. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.
James D. Hamilton, 2018. "Why You Should Never Use the Hodrick-Prescott Filter," The Review of Economics and Statistics, vol 100(5), pages 831-843.