 IMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTSTodd Clark and Michael McCracken International Economic Review, 2009, vol. 50, issue 2, 363-395 Abstract: 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. Copyright © (2009) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association No claim to original US government works . Date: 2009 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ier:iecrev:v:50:y:2009:i:2:p:363-395 Ordering information: This journal article can be ordered from Access Statistics for this article International Economic Review is currently edited by Harold L. Cole More articles in International Economic Review from Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association 160 McNeil Building, 3718 Locust Walk, Philadelphia, PA 19104-6297. Contact information at EDIRC. |
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