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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Bibliographic Details
Published in:International economic review (Philadelphia) 2009-05, Vol.50 (2), p.363-395
Main Authors: Clark, Todd E., McCracken, Michael W.
Format: Article
Language:English
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.
ISSN:0020-6598
1468-2354
DOI:10.1111/j.1468-2354.2009.00533.x