Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts
FRB of Kansas City Working Paper No. RWP 04-10
FRB of St. Louis Working Paper No. 2008-028A
53 Pages Posted: 8 Nov 2004
Date Written: August 2008
Abstract
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.
Keywords: Structural breaks, forecasting, model averaging
JEL Classification: C53, C12, C52
Suggested Citation: Suggested Citation
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