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Filtering Time Series with Penalized Splines

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Author Info

  • 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.

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Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 15 (2011)
Issue (Month): 2 (March)
Pages: 1-28

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Handle: RePEc:bpj:sndecm:v:15:y:2011:i:2:n:2

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Cited by:
  1. Göran Kauermann & Timo Teuber & Peter Flaschel, 2012. "Exploring US Business Cycles with Bivariate Loops Using Penalized Spline Regression," Computational Economics, Society for Computational Economics, vol. 39(4), pages 409-427, April.
  2. 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.
  3. Holst, Carsten & von Cramon-Taubadel, Stephan, 2011. "International Synchronisation of the Pork Cycle," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114532, European Association of Agricultural Economists.

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