IMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTS* |
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| Authors: | Todd E Clark Michael W McCracken |
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| Institution: | 1. Federal Reserve Bank of Kansas City;2. Federal Reserve Bank of St. Louis;3. We gratefully acknowledge the excellent research assistance of Taisuke Nakata and helpful comments from Ulrich Müller, Peter Summers, Ken West, Jonathan Wright, seminar participants at the University of Virginia, the Board of Governors and the Federal Reserve Bank of Kansas City, and participants at the following meetings: MEG, Canadian Economic Association, SNDE, MEC, 2004 NBER Summer Institute, NBER/NSF Time Series Conference, and the conference for young researchers on Forecasting in Time Series. The views expressed herein are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Kansas City, Federal Reserve Bank of St. Louis, or any of its staff. Please address correspondence to: Todd E. Clark, Economic Research Department, Federal Reserve Bank of Kansas City, 1 Memorial Drive, Kansas City, MO 64198. Phone: 816 881 2575. Fax: 816 881 2199. E‐mail: . |
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| Abstract: | 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. |
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