Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter
Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), 2015, vol. 235, issue 6, 518-538
The HP filter is the most popular filter for extracting the unobserved trend and cycle components from a time series. Many researchers consider the smoothing parameter λ = 1600 as something like a 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 we can get 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)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://www.degruyter.com/view/j/jbnst.2015.235.is ... -0602.xml?format=INT (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:jns:jbstat:v:235:y:2015:i:6:p:518-538
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik) is currently edited by Peter Winker
More articles in Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik) from De Gruyter
Bibliographic data for series maintained by Peter Golla ().