Regression forecasts when disturbances are autocorrelated

A Monte Carlo simulation is used to study the quality of forecasts obtained from regression models with various degrees of autocorrelation present in the disturbances. The methods used to estimate the model parameters include least squares, full maximum likelihood, Prais-Winsten, Cochrane-Orcutt and Bayesian estimation. Results indicate that the Cochrane-Orcutt method should be avoided. The full maximum likelihood, Prais-Winsten and Bayesian methods are relatively more efficient than least squares when the degree of autocorrelation is high (greater than or equal to 0.5) and show little efficiency loss when the degree is low. These results hold for both trended and untrended independent variables.