Improving forecast accuracy by combining recursive and rolling forecasts

Todd Clark and Michael McCracken

No 2008-028, Working Papers from Federal Reserve Bank of St. Louis

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: Economic forecasting; Econometric models (search for similar items in EconPapers)
Date: 2008
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: IMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTS (2009)
Working Paper: Improving forecast accuracy by combining recursive and rolling forecasts (2004)
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