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TREND EXTRACTION FROM ECONOMIC TIME SERIES WITH MISSING OBSERVATIONS BY GENERALIZED HODRICK–PRESCOTT FILTERS

Published online by Cambridge University Press:  11 June 2021

Hiroshi Yamada
Hiroshi Yamada*
Affiliation:
Hiroshima University
*
Address correspondence to Hiroshi Yamada, School of Informatics and Data Science, Hiroshima University, 1-2-1 Kagamiyama, Higashi-Hiroshima 739-8525, Japan; e-mail: yamada@hiroshima-u.ac.jp.
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Abstract

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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.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 38 , Issue 3 , June 2022 , pp. 419 - 453
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

The author thanks Jin Seo Cho, Kazuhiko Hayakawa, three anonymous referees, and the editor, Peter C. B. Phillips, for their valuable suggestions and comments on an earlier version of the paper. The usual caveat applies. The Japan Society for the Promotion of Science supported this work through KAKENHI Grant Numbers 16H03606 and 20H01484.

References

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