Why You Should Never Use the Hodrick-Prescott Filter

James D. Hamilton
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James D. Hamilton: University of California, San Diego

The Review of Economics and Statistics, 2018, vol. 100, issue 5, 831-843

Abstract: Abstract Here’s why. (a) The Hodrick-Prescott (HP) filter introduces spurious dynamic relations that have no basis in the underlying data-generating process. (b) Filtered values at the end of the sample are very different from those in the middle and are also characterized by spurious dynamics. (c) A statistical formalization of the problem typically produces values for the smoothing parameter vastly at odds with common practice. (d) There is a better alternative. A regression of the variable at date t on the four most recent values as of date t - h achieves all the objectives sought by users of the HP filter with none of its drawbacks.

Date: 2018
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