CPR Working Paper Series No. 84
Prediction in the Panel Data Model
with Spatial Correlation: The Case of Liquor
Badi H. Baltagi and Dong Li
July 2006
Abstract:
This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965-1994. The spatial autocorrelation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states demand for liquor 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, estimators that take into account spatial correlation and heterogeneity across the states perform the best for one year ahead forecasts. However, for two to five years ahead forecasts, estimators that take into account the heterogeneity across the states yield the best forecasts.
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