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Trend Extraction From Time Series With Structural Breaks and Missing Observations

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  • Schlicht, Ekkehart

Abstract

Trend extraction from time series is often performed by using the filter proposed by Leser (1961), also known as the Hodrick-Prescott filter. Practical problems arise, however, if the time series contains structural breaks (as produced by German unification for German time series, for instance), or if some data are missing. This note proposes a method for coping with these problems.

Suggested Citation

  • Schlicht, Ekkehart, 2008. "Trend Extraction From Time Series With Structural Breaks and Missing Observations," Discussion Papers in Economics 2127, University of Munich, Department of Economics.
    • Handle: RePEc:lmu:muenec:2127
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    File URL: https://epub.ub.uni-muenchen.de/2127/1/JJSS-Missing_Date_and_Breaks_DP.pdf
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    References listed on IDEAS

    as
    1. Schlicht, Ekkehart, 1981. "A Seasonal Adjustment Principle and a Seasonal Adjustment Method Derived From this Principle," Munich Reprints in Economics 3374, University of Munich, Department of Economics.
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    Citations

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    Cited by:

    1. Mand, Matthias, 2016. "On the Cyclicality of R&D Activities," VfS Annual Conference 2016 (Augsburg): Demographic Change 145472, Verein für Socialpolitik / German Economic Association.
    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. Daniel Buncic, 2020. "Econometric issues with Laubach and Williams' estimates of the natural rate of interest," Papers 2002.11583, arXiv.org, revised Aug 2020.
    4. Mand, Matthias, 2019. "On the cyclicality of R&D activities," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 38-58.

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    More about this item

    Keywords

    dummies; gaps; Hodrick-Prescott filter; interpolation; Leser filter; missing observations; smoothing; spline; structural breaks; time-series; trend; break point; break point location;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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