Multi-step Estimation for Forecasting
AbstractThe authors delineate conditions which favor multistep, or dynamic, estimation for multistep forecasting. An analytical example shows how dynamic estimation may accommodate incorrectly specified models as the forecast lead alters, improving forecast performance for some misspecifications. However, in correctly specified models, reducing finite-sample biases does not justify dynamic estimation. In a Monte Carlo forecasting study for integrated processes, estimating a unit root in the presence of a neglected negative moving-average error may favor dynamic estimation, though other solutions exist to that scenario. A second Monte Carlo study obtains the estimator biases and explains those using asymptotic approximations. Copyright 1996 by Blackwell Publishing Ltd
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Bibliographic InfoArticle provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics & Statistics.
Volume (Year): 58 (1996)
Issue (Month): 4 (November)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0305-9049
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Other versions of this item:
- Clements, Michael P. & Hendry, David F., 1996. "Multi-Step Estimation for Forecasting," The Warwick Economics Research Paper Series (TWERPS) 447, University of Warwick, Department of Economics.
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