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Bayesian Generalized Least Squares with Autocorrelated Error

Autocorrelation plays significant role in both time series and cross sectional data. More often than none it rendered the inference of parameter estimates invalid and those other statistics that use the parameters. This study investigates the asymptotic behaviour of generalised least squares with Autocorrelated errors cum Marcov Chain Monte Carlo simulation. Bias and Mean Squares Error criteria were used to evaluate finite properties of the estimator. The following sample sizes: 25, 50,100, and 250 were constructed and used. Thus 11,000 simulations with varying level of Autocorrelated error were carried out. This is subjected to the level of convergence. Bias and Minimum Mean Squares Error criteria revealed improving performance asymptotically regardless of the level of Autocorrelated error. The study observed that asymptotic consistency and efficiency were obtained at large sample which obey the law of large number and point to the fact that variance of error terms tend towards zero and distribution tends to normal when law of large number is applied. The study therefore recommended that large samples should be obtain to make the inferences stable