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Trend Extraction From Time Series With Missing Observations

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Author Info
Schlicht, Ekkehart

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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.

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File URL: http://epub.ub.uni-muenchen.de/1927/1/schlicht_missing_data-DP18.pdf
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Publisher Info
Paper provided by University of Munich, Department of Economics in its series Discussion Papers in Economics with number 1927.

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Date of creation: May 2007
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Handle: RePEc:lmu:muenec:1927

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

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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This page was last updated on 2009-10-30.

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