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

  • Yong Bao (a1)
  • DOI: http://dx.doi.org/10.1017/S0266466607070338
  • Published online: 01 August 2007
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.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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Corresponding author
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
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Econometric Theory
  • ISSN: 0266-4666
  • EISSN: 1469-4360
  • URL: /core/journals/econometric-theory
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