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Faculty of Economics

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Pesaran, M.H. and Pick, A.

Forecast combination across estimation windows

Journal of Business and Economic Statistics

Vol. 29(2) pp. 307-318 (2011)

Abstract: In this article we consider combining forecasts generated from the same model but over different estimation windows. We develop theoretical results for random walks with breaks in the drift and volatility and for a linear regression model with a break in the slope parameter. Averaging forecasts over different estimation windows leads to a lower bias and root mean square forecast error (RMSFE) compared with forecasts based on a single estimation window for all but the smallest breaks. An application to weekly returns on 20 equity index futures shows that averaging forecasts over estimation windows leads to a smaller RMSFE than some competing methods.

JEL Codes: G17

Author links: M. Hashem Pesaran  

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