Abstract
In this paper, we examine the small-sample properties of feasible GLS estimators and tests of individual regression coefficients. Bayesian and non-Bayesian estimators for an autocorrelation coefficient (denoted by ϱ) are examined by Monte Carlo experiments. It is shown that when the value of ϱ is large, the Bayesian estimator for ϱ performs better than non-Bayesian estimators for ϱ. When the value of ϱ is large and the sample size is 20, none of the feasible GLS estimators performs well in hypothesis testing. However, when the sample size is 40, the feasible Bayesian GLS estimator performs much better that the feasible non-Bayesian GLS estimators in hypothesis testing.
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