Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter
No 3816, CESifo Working Paper Series from CESifo Group Munich
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)
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Journal Article: Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter (2015)
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