IMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTS

This article presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias--variance trade-off faced when choosing between either the recursive and rolling sch...

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Published inInternational economic review (Philadelphia) Vol. 50; no. 2; pp. 363 - 395
Main Authors Clark, Todd E., McCracken, Michael W.
Format Journal Article
LanguageEnglish
Published Malden, USA Blackwell Publishing Inc 01.05.2009
Blackwell Publishing on behalf of the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association
Blackwell Publishing Ltd
Subjects
Accuracy
Analytical forecasting
Bias
Coefficients
Economic forecasting
Economic forecasting models
Economic forecasts
Error
Errors
Estimate reliability
Estimation
Forecasting models
Forecasting techniques
Linear models
Monte Carlo simulation
Point estimators
Point forecasts
Statistical forecasts
Statistical variance
Structural change
Studies
Time series forecasting
Trade-off
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Summary:This article presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias--variance trade-off faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width.
Bibliography:ark:/67375/WNG-7ND648LF-H
ArticleID:IERE533
Manuscript received April 2006; revised September 2007.
istex:67319053CDF130A80F3BBEDA6E5CE6635CF60D91
We gratefully acknowledge the excellent research assistance of Taisuke Nakata and helpful comments from Ulrich Müller, Peter Summers, Ken West, Jonathan Wright, seminar participants at the University of Virginia, the Board of Governors and the Federal Reserve Bank of Kansas City, and participants at the following meetings: MEG, Canadian Economic Association, SNDE, MEC, 2004 NBER Summer Institute, NBER/NSF Time Series Conference, and the conference for young researchers on Forecasting in Time Series. The views expressed herein are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Kansas City, Federal Reserve Bank of St. Louis, or any of its staff. Please address correspondence to: Todd E. Clark, Economic Research Department, Federal Reserve Bank of Kansas City, 1 Memorial Drive, Kansas City, MO 64198. Phone: 816 881 2575. Fax: 816 881 2199. E‐mail
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todd.e.clark@kc.frb.org
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ISSN:0020-6598
1468-2354
DOI:10.1111/j.1468-2354.2009.00533.x