LINEAR-REGRESSION FORECASTING IN THE PRESENCE OF AR(1) DISTURBANCES

This paper is concerned with time-series forecasting based on the line ar regression model in the presence of AR(1) disturbances. The standar d approach is to estimate the AR(1) parameter, rho, and then construct forecasts assuming the estimated value is the true value. We introduc e a new approach which can be viewed as a weighted average of predicti ons assuming different values of rho. The weights are proportional to the marginal likelihood of rho. A Monte Carlo experiment was conducted to compare the new method with five more conventional predictors. Its results suggest that the new approach has a distinct edge over existi ng procedures.