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Using Least Squares to Generate Forecasts in Regressions with Serial Correlation

Sergio Koreisha and Yue Fang
Decision Sciences Department
Lundquist College of Business
University of Oregon
Eugene, OR 97403 USA


Supplementary Materials. In this website you will find supplementary materials not included in the article which was published in the Journal of Time Series Analysis.  The tables here have been formatted as Adobe pdf files. If you do not have Adobe Acrobat Reader, you can downloaded it for free at this site


Abstract. The topic of serial correlation in regression models has attracted a great deal of research in the last fifty years.  Most of these studies have assumed that the structure of the error covariance matrix was known or could be consistently estimated from the data. In this article we describe a new procedure for generating forecasts for regression models with serial correlation based on ordinary least squares and on an approximate representation of the form of the autocorrelation.  We prove that the predictors from this specification are asymtotically efficient under some regularity conditions.  In addition we show that there is not much to be gained in trying to identify the correct form of the serial correlation since efficient forecasts can be generated using autoregressive approximations of the autocorrelation. A large simulation study is also used to compare the finite sample predictive efficiencies of this new estimator vis-`a-vis estimators based on ordinary least squares and generalized least squares.


Keywords: Autocorrelation; Autoregressive Moving Average; Least Squares; Model Identification; Prediction.

  • Data Sets Used in the Study
    • IBM Stock Prices (NY & London) Ibmnyln.xls – Source: DeLurgio, S. (1998) Forecasting Principles and Applications, Boston: Irwin/Mc-Graw Hill


    • Interest Rates, Aggregate Demand, and Liquid Assets Pindyck.xls– Source: Pindyck, R., and Rubinfeld, D. (1998) Econometric Models and Economic Forecasts, 4th ed., Boston: Irwin/McGraw-Hill.