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Working Paper 2008-028A

"Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts"
by Todd E. Clark, and Michael W. McCracken

This paper 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 tradeoff 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.

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Category > Applied Econometrics
Author > Michael W. McCracken
Research Papers and Publications: JEL Code > C53
Research Papers and Publications: JEL Code > C12
Research Papers and Publications: JEL Code > C52