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Why You Should Never Use the Hodrick-Prescott Filter
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.
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Cited by
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Metadata
- Authors
- Hamilton, J. D.
- Venue
- The Review of Economics and Statistics
- Publisher
- MIT Press
- Published
- 2018-12
- Volume
- 100
- Issue
- 5
- Pages
- 831-843
- ISSN
- 0034-6535 (ISSN-L)
- 1530-9142
- OpenAlex
- W2617957753
- Cited by
- 1,342
- References
- 0
- FWCI
- 149.10
- Type
- journal article
- Language
- en
- Open access
- closed
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