Why You Should Never Use the Hodrick-Prescott Filter

• 成果类型: Article
• 署名作者: Hamilton, James D.
• 署名单位: University of California System; University of California San Diego
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00706
• 发表日期: 2018-12
• 页码: 831-843
• 关键词: exchange-rate models; time-series; frequency; permanent; inference; fit

摘要

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.