Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter

Gebhard Flaig

No 3816, CESifo Working Paper Series from CESifo

Abstract: The HP filter is the most popular filter for extracting the trend and cycle components from an observed time series. Many researchers consider the smoothing parameter ë = 1600 as something like an universal constant. It is well known that the HP filter is an optimal filter under some restrictive assumptions, especially that the “cycle” is white noise. In this paper we show that one gets a good approximation of the optimal Wiener-Kolmogorov filter for autocorrelated cycle components by using the HP filter with a much higher smoothing parameter than commonly used. In addition, a new method - based on the properties of the differences of the estimated trend - is proposed for the selection of the smoothing parameter.

Keywords: Hodrick-Prescott filter; Wiener-Kolmogorov filter; smoothing parameter; trends; cycles (search for similar items in EconPapers)
JEL-codes: C22 C52 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.cesifo.org/DocDL/cesifo1_wp3816.pdf (application/pdf)

Related works:
Journal Article: Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_3816

Access Statistics for this paper

More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().