02 agosto 2016

Nunca use o filtro Hodrick-Prescott


Here’s why. (1) The HP filter produces series with spurious dynamic relations that have no basis in the underlying data-generating process. (2) A one-sided version of the filter reduces but does not eliminate spurious predictability and moreover produces series that do not have the properties sought by most potential users of the HP filter. (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
Fonte: Why You Should Never Use the Hodrick-Prescott Filter∗ James D. Hamilton- Working Paper - 30 de julho 2016

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