Working Paper: NBER ID: w23429
Authors: James D. Hamilton
Abstract: 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.
Keywords: Hodrick-Prescott Filter; detrending; economic time series
JEL Codes: C22; E32; E47
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| HP filter (L63) | spurious dynamic relationships (C32) |
| filtered values at the end of the sample (C24) | different from those in the middle (Y60) |
| smoothing parameter of 1600 (C22) | inappropriate (K40) |
| regression on four most recent values (C29) | better alternative for detrending (C22) |