Econometric models of temperature impacts on GDP are increasingly used to inform global warming damage assessments. But theory does not prescribe estimable forms of this relationship. By estimating 800 plausible specifications of the temperature-GDP relationship, we demonstrate that a wide variety of models are statistically indistinguishable in their out-of-sample performance, including models that exclude any temperature effect. This full set of models, however, implies a wide range of climate change impacts by 2100, yielding considerable model uncertainty. The uncertainty is greatest for models that specify effects of temperature on GDP growth that accumulate over time; the 95% confidence interval that accounts for both sampling and model uncertainty across the best-performing models ranges from 84% GDP losses to 359% gains. Models of GDP levels effects yield a much narrower distribution of GDP impacts centered around 1–3% losses, consistent with damage functions of major integrated assessment models. Further, models that incorporate lagged temperature effects are indicative of impacts on GDP levels rather than GDP growth. We identify statistically significant marginal effects of temperature on poor country GDP and agricultural production, but not rich country GDP, non-agricultural production, or GDP growth.

温度对GDP的影响的计量经济学模型越来越多地用于全球变暖损害评估。但是理论并未规定这种关系的可估计形式。通过估计800种可能的温度与GDP关系的规范，我们证明了各种模型的样本外性能在统计上是无法区分的，包括排除任何温度影响的模型。然而，这套完整的模型意味着到2100年气候变化的影响范围很广，从而产生了很大的模型不确定性。对于指定温度对GDP增长的影响的模型，不确定性最大随着时间的推移而积累；表现最佳模型中样本和模型不确定性的95％置信区间介于GDP损失84％至收益359％之间。GDP水平效应模型产生的GDP分布范围要窄得多，损失集中在1-3％左右，这与主要综合评估模型的破坏函数一致。此外，包含滞后温度效应的模型表明了对GDP水平的影响，而不是对GDP增长的影响。我们发现温度对贫困国家的GDP和农业生产具有统计学意义的边际效应，但对富裕国家的GDP，非农业生产或GDP增长没有影响。