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Reducing the Excess Variability of the Hodrick-Prescott Filter by Flexible Penalization

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  • Bloechl, Andreas
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    Abstract

    The Hodrick-Prescott filter is the probably most popular tool for trend estimation in economics. Compared to other frequently used methods like the Baxter-King filter it allows to estimate the trend for the most recent periods of a time series. However, the Hodrick- Prescott filter suffers from an increasing excess variability at the margins of the series inducing a too flexible trend function at the margins compared to the middle. This paper will tackle this problem using spectral analysis and a flexible penalization. It will show that the excess variability can be reduced immensely by a flexible penalization, while the gain function for the middle of the time series is used as a measure to determine the degree of the flexible penalization.

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    File URL: http://epub.ub.uni-muenchen.de/17940/1/HP-filter%20with%20reduced%20excess%20variability.pdf
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    Bibliographic Info

    Paper provided by University of Munich, Department of Economics in its series Discussion Papers in Economics with number 17940.

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    Date of creation: Jan 2014
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    Handle: RePEc:lmu:muenec:17940

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    Related research

    Keywords: Hodrick-Prescott filter; spectral analysis; trend estimation; gain function; flexible penalization;

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    1. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
    2. Danthine, Jean-Pierre & Girardin, Michel, 1989. "Business cycles in Switzerland : A comparative study," European Economic Review, Elsevier, vol. 33(1), pages 31-50, January.
    3. Marianne Baxter & Robert G. King, 1995. "Measuring Business Cycles Approximate Band-Pass Filters for Economic Time Series," NBER Working Papers 5022, National Bureau of Economic Research, Inc.
    4. Gebhard Flaig, 2012. "Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter," CESifo Working Paper Series 3816, CESifo Group Munich.
    5. McElroy, Tucker, 2008. "Matrix Formulas For Nonstationary Arima Signal Extraction," Econometric Theory, Cambridge University Press, vol. 24(04), pages 988-1009, August.
    6. Stamfort, Stefan, 2005. "Berechnung trendbereinigter Indikatoren für Deutschland mit Hilfe von Filterverfahren," Discussion Paper Series 1: Economic Studies 2005,19, Deutsche Bundesbank, Research Centre.
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