Reducing the Excess Variability of the Hodrick-Prescott Filter by Flexible Penalization
AbstractThe Hodrick-Prescott filter is the probably most popular tool for trend estimation in economics. Compared to other frequently used methods like the Baxter-King filter it allows to estimate the trend for the most recent periods of a time series. However, the Hodrick- Prescott filter suffers from an increasing excess variability at the margins of the series inducing a too flexible trend function at the margins compared to the middle. This paper will tackle this problem using spectral analysis and a flexible penalization. It will show that the excess variability can be reduced immensely by a flexible penalization, while the gain function for the middle of the time series is used as a measure to determine the degree of the flexible penalization.
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Bibliographic InfoPaper provided by University of Munich, Department of Economics in its series Discussion Papers in Economics with number 17940.
Date of creation: Jan 2014
Date of revision:
Hodrick-Prescott filter; spectral analysis; trend estimation; gain function; flexible penalization;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-01-24 (All new papers)
- NEP-ECM-2014-01-24 (Econometrics)
- NEP-ETS-2014-01-24 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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5022, National Bureau of Economic Research, Inc.
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- Gebhard Flaig, 2012. "Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter," CESifo Working Paper Series 3816, CESifo Group Munich.
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