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

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  • Bloechl, Andreas

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.

Suggested Citation

  • Bloechl, Andreas, 2014. "Reducing the Excess Variability of the Hodrick-Prescott Filter by Flexible Penalization," Discussion Papers in Economics 17940, University of Munich, Department of Economics.
  • Handle: RePEc:lmu:muenec:17940
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    File URL: https://epub.ub.uni-muenchen.de/17940/1/HP-filter%20with%20reduced%20excess%20variability.pdf
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    References listed on IDEAS

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    1. Danthine, Jean-Pierre & Girardin, Michel, 1989. "Business cycles in Switzerland : A comparative study," European Economic Review, Elsevier, vol. 33(1), pages 31-50, January.
    2. Flaig Gebhard, 2015. "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), De Gruyter, vol. 235(6), pages 518-538, December.
    3. Stamfort, Stefan, 2005. "Berechnung trendbereinigter Indikatoren für Deutschland mit Hilfe von Filterverfahren," Discussion Paper Series 1: Economic Studies 2005,19, Deutsche Bundesbank.
    4. Schlicht, Ekkehart, 2004. "Estimating the Smoothing Parameter in the So-Called Hodrick-Prescott Filter," Discussion Papers in Economics 304, University of Munich, Department of Economics.
    5. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
    6. McElroy, Tucker, 2008. "Matrix Formulas For Nonstationary Arima Signal Extraction," Econometric Theory, Cambridge University Press, vol. 24(04), pages 988-1009, August.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

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

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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