期刊
JOURNAL OF ECONOMETRICS
卷 129, 期 1-2, 页码 183-217出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2004.09.007
关键词
small sample properties of forecasts; MSFE; structural breaks; autoregression; rolling window estimator
This paper develops a theoretical framework for the analysis of small-sample properties of forecasts from general autoregressive models under structural breaks. Finite-sample results for the mean squared forecast error of one-step ahead forecasts are derived, both conditionally and unconditionally, and numerical results for different types of break specifications are presented. It is established that forecast errors are unconditionally unbiased even in the presence of breaks in the autoregressive coefficients and/or error variances so long as the unconditional mean of the process remains unchanged. Insights from the theoretical analysis are demonstrated in Monte Carlo simulations and on a range of macroeconomic time series from G7 countries. The results are used to draw practical recommendations for the choice of estimation window when forecasting from autoregressive models subject to breaks. (c) 2004 Elsevier B.V. All rights reserved.
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