Small Sample Properties of Forecasts from Autoregressive Models Under Structural Breaks
47 Pages Posted: 28 Jun 2004
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Small Sample Properties of Forecasts from Autoregressive Models Under Structural Breaks
Date Written: June 2004
Abstract
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 means 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.
Keywords: Autoregression, MSFE, rolling window estimator, small sample properties of forecasts and structural breaks
JEL Classification: C22, C53
Suggested Citation: Suggested Citation
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