Small Sample Properties of Forecasts from Autoregressive Models Under Structural Breaks

47 Pages Posted: 28 Jun 2004

See all articles by M. Hashem Pesaran

M. Hashem Pesaran

University of Southern California - Department of Economics

Allan Timmermann

UCSD ; Centre for Economic Policy Research (CEPR)

Multiple version iconThere are 2 versions of this paper

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

Pesaran, M. Hashem and Timmermann, Allan, Small Sample Properties of Forecasts from Autoregressive Models Under Structural Breaks (June 2004). Available at SSRN: https://ssrn.com/abstract=560027

M. Hashem Pesaran

University of Southern California - Department of Economics ( email )

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Allan Timmermann (Contact Author)

UCSD ( email )

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Centre for Economic Policy Research (CEPR)

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