The boosted HP filter is more general than you might think

Ziwei Mei, Zhentao Shi and Peter Phillips
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Ziwei Mei: The Chinese University of Hong Kong
Zhentao Shi: The Chinese University of Hong Kong

No 2348, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: The global financial crisis and Covid recession have renewed discussion concerning trend-cycle discovery in macroeconomic data, and boosting has recently upgraded the popular HP filter to a modern machine learning device suited to data-rich and rapid computational environments. This paper sheds light on its versatility in trend-cycle determination, explaining in a simple manner both HP filter smoothing and the consistency delivered by boosting for general trend detection. Applied to a universe of time series in FRED databases, boosting outperforms other methods in timely capturing downturns at crises and recoveries that follow. With its wide applicability the boosted HP filter is a useful automated machine learning addition to the macroeconometric toolkit.

Pages: 46 pages
Date: 2022-09
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (4)

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Working Paper: The boosted HP filter is more general than you might think (2024)
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