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Trend Extraction From Economic Time Series With Missing Observations By Generalized Hodrick–Prescott Filters

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  • Yamada, Hiroshi

Abstract

The Hodrick–Prescott (HP) filter has been a popular method of trend extraction from economic time series. However, it is impractical without modification if some observations are not available. This paper improves the HP filter so that it can be applied in such situations. More precisely, this paper introduces two alternative generalized HP filters that are applicable for this purpose. We provide their properties and a way of specifying those smoothing parameters that are required for their application. In addition, we numerically examine their performance. Finally, based on our analysis, we recommend one of them for applied studies.

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  • Yamada, Hiroshi, 2022. "Trend Extraction From Economic Time Series With Missing Observations By Generalized Hodrick–Prescott Filters," Econometric Theory, Cambridge University Press, vol. 38(3), pages 419-453, June.
    • Handle: RePEc:cup:etheor:v:38:y:2022:i:3:p:419-453_1
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    Cited by:

    1. Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org, revised Apr 2024.
    2. Hiroshi Yamada, 2023. "Quantile regression version of Hodrick–Prescott filter," Empirical Economics, Springer, vol. 64(4), pages 1631-1645, April.

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