Small Sample Properties of Forecasts from Autoregressive Models Under Structural Breaks
42 Pages Posted: 21 Aug 2003
There are 2 versions of this paper
Small Sample Properties of Forecasts from Autoregressive Models Under Structural Breaks
Small Sample Properties of Forecasts from Autoregressive Models Under Structural Breaks
Date Written: July 2003
Abstract
Autoregressive models are used routinely in forecasting and often lead to better performance than more complicated models. However, empirical evidence is also suggesting that the autoregressive representations of many macroeconomic and financial time series are likely to be subject to structural breaks. This paper develops a theoretical framework for the analysis of small-sample properties of forecasts from general autoregressive models under a structural break. Our approach is quite general and allows for unit roots both pre- and post-break. We derive finite-sample results for the mean squared forecast error of one-step-ahead forecasts, both conditionally and unconditionally and present numerical results for different types of break specifications. Implications of breaks for the determination of the optimal window size are also discussed.
Keywords: Small Sample Properties of Forecasts, RMSFE, Structural Breaks, Autoregression
JEL Classification: C22, C53
Suggested Citation: Suggested Citation
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