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Asymptotic Mean‐Square Error of Predicting More than One‐Step Ahead Using the Regression Method

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  • R. J. Bhansali

Abstract

We consider the question of linear, least‐squares prediction of the future values of a discrete, stationary autoregressive process from a realization of past values. An expression is given for the asymptotic mean‐square error of predicting more than one‐step ahead when the autoregressive coefficients are estimated from the sample by solving the Yule‐Walker equations. Computer results are given to demonstrate the usefulness of the asymptotic result.

Suggested Citation

  • R. J. Bhansali, 1974. "Asymptotic Mean‐Square Error of Predicting More than One‐Step Ahead Using the Regression Method," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(1), pages 35-42, March.
    • Handle: RePEc:bla:jorssc:v:23:y:1974:i:1:p:35-42
    DOI: 10.2307/2347051
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    Cited by:

    1. Chan, W.S & Cheung, S.H & Wu, K.H, 2004. "Multiple forecasts with autoregressive time series models: case studies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(3), pages 421-430.
    2. Siu Hung Cheung & Ka Ho Wu & Wai Sum Chan, 1998. "Simultaneous prediction intervals for autoregressive-integrated moving-average models: A comparative study," Computational Statistics & Data Analysis, Elsevier, vol. 28(3), pages 297-306, September.

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