Estimation and prediction in the random effects model with AR(pp) remainder disturbances

This paper considers the problem of estimation and forecasting in a panel data model with random individual effects and AR(pp) remainder disturbances. It utilizes a simple exact   transformation for the AR(pp) time series process derived by Baltagi and Li (1994) and obtains the generalized least squares estimator for this panel model as a least squares regression. This exact transformation is also used in conjunction with Goldberger’s (1962) result to derive an analytic expression for the best linear unbiased predictor. The performance of this predictor is investigated using Monte Carlo experiments and illustrated using an empirical example.