Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter
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.
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- George E. P. Box & Steven Hilimer & George C. Tiao, 1978.
"Analysis and Modeling of Seasonal Time Series,"
in: Seasonal Analysis of Economic Time Series, pages 309-344
National Bureau of Economic Research, Inc.
- Cogley, Timothy & Nason, James M., 1995.
"Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research,"
Journal of Economic Dynamics and Control,
Elsevier, vol. 19(1-2), pages 253-278.
- Timothy Cogley & James M. Nason, 1993. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series: implications for business cycle research," Working Papers in Applied Economic Theory 93-01, Federal Reserve Bank of San Francisco.
- King, R.G. & Rebelo, S.T., 1989.
"Low Frequency Filtering And Real Business Cycles,"
RCER Working Papers
205, University of Rochester - Center for Economic Research (RCER).
- McElroy, Tucker, 2008. "Matrix Formulas For Nonstationary Arima Signal Extraction," Econometric Theory, Cambridge University Press, vol. 24(04), pages 988-1009, August.
- Harvey, Andrew C. & Delle Monache, Davide, 2009. "Computing the mean square error of unobserved components extracted by misspecified time series models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 283-295, February.
- Gomez, Victor, 1999. "Three Equivalent Methods for Filtering Finite Nonstationary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 109-16, January.
- Watson, Mark W., 1986. "Univariate detrending methods with stochastic trends," Journal of Monetary Economics, Elsevier, vol. 18(1), pages 49-75, July.
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