Small Sample Properties of Forecasts from Autoregressive Models Under Structural Breaks
M. Hashem Pesaran
University of Southern California; Cambridge University - Faculty of Economics; CESifo (Center for Economic Studies and Ifo Institute for Economic Research); Institute for the Study of Labor (IZA)
Allan G. Timmermann
University of California, San Diego (UCSD) - Department of Economics; Centre for Economic Policy Research (CEPR)
CESifo Working Paper Series No. 990
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.
Number of Pages in PDF File: 42
Keywords: Small Sample Properties of Forecasts, RMSFE, Structural Breaks, Autoregression
JEL Classification: C22, C53working papers series
Date posted: August 21, 2003
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