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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, annual mortality or gross domestic product, along with a higher-frequency regressor, e.g. daily temperature. While applied researchers tend to consider multiple models to characterize the relationship between the outcome and the high-frequency regressor, to inform policy a choice between the damage functions implied by the different models has to be made. This paper formalizes the model selection problem in this empirical setting and provides conditions for the consistency of Monte Carlo Cross-validation and generalized information criteria. A simulation study illustrates the theoretical results and points to the relevance of the signal-to-noise ratio for the finite-sample behavior of the model selection criteria. Two empirical applications with starkly different signal-to-noise ratios illustrate the practical implications of the formal analysis on model selection criteria provided in this paper.
When the Stable Unit Treatment Value Assumption (SUTVA) is violated and there is interference among units, there is not a uniquely defined Average Treatment Effect (ATE), and alternative estimands may be of interest, among them average unit-level dif
We consider the problem of variable selection in high-dimensional settings with missing observations among the covariates. To address this relatively understudied problem, we propose a new synergistic procedure -- adaptive Bayesian SLOPE -- which eff
In employing spatial regression models for counts, we usually meet two issues. First, ignoring the inherent collinearity between covariates and the spatial effect would lead to causal inferences. Second, real count data usually reveal over or under-d
Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor
The joint modeling of mean and dispersion (JMMD) provides an efficient method to obtain useful models for the mean and dispersion, especially in problems of robust design experiments. However, in the literature on JMMD there are few works dedicated t