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Improved forecasting of autoregressive series by weighted least squares approximate REML estimation

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  • Deo, Rohit S.

Abstract

Restricted maximum likelihood (REML) estimation has recently been shown to provide less biased estimates in autoregressive series. A simple weighted least squares approximate REML procedure has been developed that is particularly useful for vector autoregressive processes. Here, we compare the forecasts of such processes using both the standard ordinary least squares (OLS) estimates and the new approximate REML estimates. Forecasts based on the approximate REML estimates are found to provide a significant improvement over those obtained using the standard OLS estimates.

Suggested Citation

  • Deo, Rohit S., 2012. "Improved forecasting of autoregressive series by weighted least squares approximate REML estimation," International Journal of Forecasting, Elsevier, vol. 28(1), pages 39-43.
    • Handle: RePEc:eee:intfor:v:28:y:2012:i:1:p:39-43
    DOI: 10.1016/j.ijforecast.2011.02.014
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    References listed on IDEAS

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    1. Cressie, N. & Lahiri, S. N., 1993. "The Asymptotic Distribution of REML Estimators," Journal of Multivariate Analysis, Elsevier, vol. 45(2), pages 217-233, May.
    2. Willa W. Chen & Rohit S. Deo, 2010. "Weighted least squares approximate restricted likelihood estimation for vector autoregressive processes," Biometrika, Biometrika Trust, vol. 97(1), pages 231-237.
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    Cited by:

    1. Peter C.B. Phillips & Ye Chen, "undated". "Restricted Likelihood Ratio Tests in Predictive Regression," Cowles Foundation Discussion Papers 1968, Cowles Foundation for Research in Economics, Yale University.

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