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Trend Extraction From Time Series With Missing Observations

Author

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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. A practical problem arises, however, when some data points are missing. This note proposes a method for coping with this problem.

Suggested Citation

  • Schlicht, Ekkehart, 2007. "Trend Extraction From Time Series With Missing Observations," Discussion Papers in Economics 1927, University of Munich, Department of Economics.
    • Handle: RePEc:lmu:muenec:1927
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    File URL: https://epub.ub.uni-muenchen.de/1927/1/schlicht_missing_data-DP18.pdf
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    More about this item

    Keywords

    Trend extraction; missing observations; gaps; Hodrick-Prescott filter; Leser filter; spline; time-series; smoothing; interpolation.;
    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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