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Why You Should Never Use the Hodrick-Prescott Filter

2017; The MIT Press; Volume: 100; Issue: 5 Linguagem: Inglês

10.1162/rest_a_00706

1530-9142

James D. Hamilton,

Energy Load and Power Forecasting

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.