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Second-order risk comparison of SLSE with GLSE and MLE in a regression with serial correlation

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  • Toyooka, Yasuyuki

Abstract

First, the second-order bias of the estimator of the autoregressive parameter based on the ordinary least squares residuals in a linear model with serial correlation is given. Second, the second-order expansion of the risk matrix of a generalized least squares estimator with the above estimated parameter is obtained. This expansion is the same as that based on a suitable estimator of the autoregressive parameter independent of the sample. Third, it is shown that the risk matrix of the generalized least squares estimator is asymptotically equivalent to that of the maximum likelihood estimator up to the second order. Last, a sufficient condition is given for the term due to the estimation of the autoregressive parameter in this expansion to vanish under Grenander's condition for the explanatory variates.

Suggested Citation

  • Toyooka, Yasuyuki, 1985. "Second-order risk comparison of SLSE with GLSE and MLE in a regression with serial correlation," Journal of Multivariate Analysis, Elsevier, vol. 17(2), pages 107-126, October.
    • Handle: RePEc:eee:jmvana:v:17:y:1985:i:2:p:107-126
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    Cited by:

    1. Tamaki, Kenichiro, 2007. "Second order optimality for estimators in time series regression models," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 638-659, March.

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