On Prediction Errors in Regression Models with Nonstationary Regressors

Ching-Kang Ing, Chor-Yiu Sin

IMS Lecture Notes–Monograph Series Time Series and Related Topics Vol. 52 (2006) 60–71

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In this article asymptotic expressions for the final prediction error (FPE) and the accumulated prediction error (APE) of the least squares predictor are obtained in regression models with nonstationary regressors. It is shown that the term of order 1/n in FPE and the term of order log n in APE share the same constant, where n is the sample size. Since the model includes the random walk model as a special case, these asymptotic expressions extend some of the results in Wei (1987) and Ing (2001). In addition, we also show that while the FPE of the least squares predictor is not affected by the contemporary correlation between the innovations in input and output variables, the mean squared error of the least squares estimate does vary with this correlation.