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Filtering Time Series with Penalized Splines

Author

Listed:
  • Kauermann Goeran

    (University Bielefeld)

  • Krivobokova Tatyana

    (University of Göttingen)

  • Semmler Willi

    (The New School)

Abstract

The decomposition and filtering of time series is an important issue in economics and econometrics and related fields. Even though there are numerous competing methods on the market, in applications one often meets one of the few favorites, like the Hodrick-Prescott filter or the bandpass filter.In this paper, we suggest to employ penalized splines fitting for detrending. The approach allows to take correlation of the residuals into account and provides a data driven setting of the smoothing parameter, none of which the classical filters allow. We show the simplicity of the penalized spline filter using the open source software R and demonstrate differences and features with numerous data examples.

Suggested Citation

  • Kauermann Goeran & Krivobokova Tatyana & Semmler Willi, 2011. "Filtering Time Series with Penalized Splines," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(2), pages 1-28, March.
    • Handle: RePEc:bpj:sndecm:v:15:y:2011:i:2:n:2
    DOI: 10.2202/1558-3708.1789
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Blöchl, Andreas, 2014. "Penalized Splines as Frequency Selective Filters - Reducing the Excess Variability at the Margins," Discussion Papers in Economics 20687, University of Munich, Department of Economics.
    3. Rosales, Francisco & von-Cramon, Stephan, 2015. "Analysis of Price Transmission using a Nonparametric Error Correction Model with Time-Varying Cointegration," 2015 Conference, August 9-14, 2015, Milan, Italy 230227, International Association of Agricultural Economists.
    4. Bloechl, Andreas, 2014. "Penalized Splines, Mixed Models and the Wiener-Kolmogorov Filter," Discussion Papers in Economics 21406, University of Munich, Department of Economics.
    5. Blöchl, Andreas, 2014. "Trend Estimation with Penalized Splines as Mixed Models for Series with Structural Breaks," Discussion Papers in Economics 18446, University of Munich, Department of Economics.
    6. Holst, Carsten & von Cramon-Taubadel, Stephan, 2012. "International Synchronisation of the Pork Cycle," Acta Oeconomica et Informatica, Faculty of Economics and Management, Slovak Agricultural University in Nitra (FEM SPU), vol. 15(1), pages 1-6, March.
    7. Göran Kauermann & Timo Teuber & Peter Flaschel, 2012. "Exploring US Business Cycles with Bivariate Loops Using Penalized Spline Regression," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 409-427, April.
    8. Anusha, "undated". "Evaluating reliability of some symmetric and asymmetric univariate filters," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2015-030, Indira Gandhi Institute of Development Research, Mumbai, India.
    9. Feng, Yuanhua & Härdle, Wolfgang Karl, 2020. "A data-driven P-spline smoother and the P-Spline-GARCH models," IRTG 1792 Discussion Papers 2020-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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