On the Cyclical Properties of Hamilton’s Regression Filter

21 Pages Posted: 17 Apr 2020 Last revised: 2 Mar 2021

Date Written: January 25, 2021

Abstract

Hamilton (2018) proposes a regression filter (Hamilton filter) as an alternative to the Hodrick-Prescott filter (HP filter). He argues that the Hamilton filter meets all of the objectives desired by users of the HP filter, while addressing its drawbacks. I show that there is a trade-of between these two goals, which has been overlooked. The Hamilton filter does indeed avoid spurious cycles, ad hoc filter settings and end-of-sample bias. However, this comes at a cost. The Hamilton filter may modify fluctuations by altering variances and inducing phase shifts. Furthermore, the exact form of modification can vary across time series.

Keywords: Business cycles, detrending, band pass filter, forecast errors, spurious cycles, phase shifts

JEL Classification: C10, E32, E58, G01

Suggested Citation

Schüler, Yves Stephan, On the Cyclical Properties of Hamilton’s Regression Filter (January 25, 2021). Available at SSRN: https://ssrn.com/abstract=3559776 or http://dx.doi.org/10.2139/ssrn.3559776

Yves Stephan Schüler (Contact Author)

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

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