Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. agricultural yield, along with a higher-frequency regressor, e.g. daily temperature. Applied researchers then face a problem of selecting a model to characterize the nonlinear relationship between the outcome and the high-frequency regressor to make a policy recommendation based on the model-implied damage function. We show that existing model selection criteria are only suitable for the policy objective if one of the models under consideration nests the true model. If all models are seen as imperfect approximations of the true nonlinear relationship, the model that performs well in the historical climate conditions is not guaranteed to perform well at the projected climate. We therefore propose a new criterion, the proximity-weighted mean squared error (PWMSE) that directly targets precision of the damage function at the projected future climate. To make this criterion feasible, we assign higher weights to historical years that can serve as “weather analogs” to the projected future climate when evaluating competing models using the PWMSE. We show that our approach selects the best approximate regression model that has the smallest weighted squared error of predicted impacts for a projected future climate. A simulation study and an application revisiting the impact of climate change on agricultural production illustrate the empirical relevance of our theoretical analysis.

崔潇濛，现任暨南大学经济与社会研究院副教授，于2018年获得加州大学戴维斯分校农业与资源经济学博士学位。研究领域为环境与资源经济学、农业与发展经济学、应用计量经济学。研究成果发表于Journal of Econometrics，Journal of the Association of Environmental and Resource Economists，Journal of Environmental Economics and Management，American Journal of Agricultural Economics等国际权威期刊。主持国家自然科学基金面上项目、青年科学基金项目。