Predicting Future Forestland Area: A Comparison of Econometric Approaches

Predictions of future forestland area are an important component of forest policy analyses. In this article, we test the ability of econometric land use models to accurately forecast forest area. We construct a panel data set for Alabama consisting of county and time-series observation for the period 1964 to 1992. We estimate models using restricted data sets-namely, data from early periods-and use out-of-sample values of dependent and independent variables to construct precise tests of the model's forecasting accuracy. Three model specifications are examined: ordinary least squares, dummy variables (fixed effects), and error components (random effects). We find that the dummy variables model produces more accurate forecasts at the county and state level than the other model specifications. This result is related to the ability of the dummy variables model to more completely control for cross-sectional variation in the dependent variables. This suggests that the estimated model parameters better capture the temporal relationship between forest area and economic variables.

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