Econometric Theory



NOTES AND PROBLEMS

FINITE-SAMPLE PROPERTIES OF FORECASTS FROM THE STATIONARY FIRST-ORDER AUTOREGRESSIVE MODEL UNDER A GENERAL ERROR DISTRIBUTION


Yong  Bao  a1 c1
a1 University of Texas at San Antonio

Article author query
bao y   [Google Scholar] 
 

Abstract

We study the properties of the multi-period-ahead least-squares forecast for the stationary AR(1) model under a general error distribution. We find that the forecast is unbiased up to O(T−1), where T is the in-sample size, regardless of the error distribution and that the mean squared forecast error, up to O(T−3/2), is robust against nonnormality. a


Correspondence:
c1 Address correspondence to Yong Bao, Department of Economics, University of Texas at San Antonio, San Antonio, TX 78249, USA; e-mail: yong.bao@utsa.edu


Footnotes

a The author is grateful to the co-editor Paolo Paruolo and two anonymous referees for helpful comments. The author is solely responsible for any remaining errors.



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