. 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. FOR. SCI. 46(3): 363–376.

中文翻译：

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预测未来林地面积：计量经济学方法比较

。未来林地面积的预测是森林政策分析的重要组成部分。在本文中，我们测试了计量土地利用模型准确预测森林面积的能力。我们为阿拉巴马州构建了一个面板数据集，其中包含 1964 年至 1992 年期间的县和时间序列观察。我们使用受限数据集（即早期数据）估计模型，并使用相关和独立的样本外值变量来构建模型预测准确性的精确测试。检查三个模型规范：普通最小二乘法、虚拟变量（固定效应）和误差分量（随机效应）。我们发现虚拟变量模型在县和州一级产生的预测比其他模型规范更准确。该结果与虚拟变量模型更完全地控制因变量的横截面变化的能力有关。这表明估计的模型参数更好地捕捉了森林面积和经济变量之间的时间关系。为了。 SCI。 46（3）：363-376。