Isiaka Oloyede2025-04-152025-04-152022https://uilspace.unilorin.edu.ng/handle/123456789/15003Autocorrelation plays a 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 Markov Chain Monte Carlo simulation. Bias and Mean Squares Error criteria were used to evaluate the finite properties of the estimator. The following sample sizes: 25, 50, 100, and 250 were constructed and used. Thus 11,000 simulations with varying levels of Autocorrelated error were carried out. This is subjected to the level of convergence. Bias and 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 the large sample which obeys the law of large number and points to the fact that variance of error terms tend towards zero and distribution tends to be normal when the law of large number is applied. In line with the above assertion, the study had developed novel Bayesian Generalized Least Squares Autocorrelated Estimator (BGLSAE) that capture the presence of autocorrelation in the dataset. The study, therefore recommended that large samples should be obtained to make the inferences stable.enAutocorrelationBayesian InferenceGibbs SamplerMarkov Chain Monte Carlo MethodArticle