Improving forecast accuracy by combining recursive and rolling forecasts

Todd Clark and Michael McCracken

No RWP 04-10, Research Working Paper from Federal Reserve Bank of Kansas City

Abstract: This paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining recursive and rolling forecasts when linear predictive models are subject to structural change. We first provide 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. From that, we derive pointwise optimal, time-varying and data-dependent observation windows and combining weights designed to minimize mean square forecast error. We then proceed to consider other methods of forecast combination, including Bayesian methods that shrink the rolling forecast to the recursive and Bayesian model averaging. Monte Carlo experiments and several empirical examples indicate that although the recursive scheme is often difficult to beat, when gains can be obtained, some form of shrinkage 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: Forecasting (search for similar items in EconPapers)
Date: 2004
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
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Citations: View citations in EconPapers (21)

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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 (2008)
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