Skip to main content
    • Aa
    • Aa


  • Yong Bao (a1)

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.

Corresponding author
Address correspondence to Yong Bao, Department of Economics, University of Texas at San Antonio, San Antonio, TX 78249, USA; e-mail:
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

Davisson, L.D. (1965) The prediction error of stationary Gaussian time series of unknown covariance. IEEE Transactions on Information Theory 11, 527532.

Dufour, J.-M. (1984) Unbiasedness of predictions from estimated autoregression when the true order is unknown. Econometrica 52, 209215.

Dufour, J.-M. (1985) Unbiasedness of predictions from estimator vector autoregressions. Econometric Theory 1, 387402.

Hoque, A., J.R. Magnus, & B. Pesaran (1988) The exact multi-period mean-squared forecast error for the first-order autoregressive model. Journal of Econometrics 39, 327346.

Fuller, W.A. & D.P. Hasza (1980) Predictors for the first-order autoregressive process. Journal of Econometrics 13, 139157.

Nagar, A.L. (1959) The bias and moment matrix of the general k-class estimators of the parameters in simultaneous equations. Econometrica 27, 575595.

Phillips, P.C.B. (1979) The sampling distribution of forecasts from a first-order autoregression. Journal of Econometrics 9, 241261.

Yamamoto, T. (1976) Asymptotic mean square prediction error for an autoregression model. Applied Statistics 25, 123127.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Econometric Theory
  • ISSN: 0266-4666
  • EISSN: 1469-4360
  • URL: /core/journals/econometric-theory
Please enter your name
Please enter a valid email address
Who would you like to send this to? *


Full text views

Total number of HTML views: 1
Total number of PDF views: 12 *
Loading metrics...

Abstract views

Total abstract views: 49 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 24th June 2017. This data will be updated every 24 hours.