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Generalized Least Squares with an Estimated Autocovariance Matrix
Takeshi Amemiya
Abstract
The paper proves the asymptotic normality of a generalized least squares estimator utilizing estimated autocovariances of the residual in a regression equation having a residual following a mixed autoregressive, moving-average process. It also proves the asymptotic normality of the best linear unbiased estimator and shows that the two asymptotic distributions are the same.
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