Prediction in the Panel Data Model with Spatial Correlation

This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation. In particular, we consider a simple demand equation for cigarettes based on a panel of 46 states over the period 1963-1992. The spatial autocorrelation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. We derive the best linear unbiased predictor for the random error component model with spatial correlation and compare the performance of several predictors of the states demand for cigarettes for one year and five years ahead. The estimators whose predictions are compared include OLS, fixed effects ignoring spatial correlation, fixed effects with spatial correlation, random effects GLS estimator ignoring spatial correlation and random effects estimator accounting for the spatial correlation. Based on RMSE forecast performance, it is important to take into account spatial correlation and heterogeneity across the states.