Identification of Counterfactuals in Dynamic Discrete Choice Models

Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non-identification of models does not necessarily imply the non-identification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non- additive changes in payoffs or changes to agents' choice sets. In doing so, we propose a general framework that allows the investigation of the identification of a broad class of counterfactuals (covering virtually any counterfactual encountered in applied work). To illustrate the results, we consider a firm entry/exit problem numerically, as well as an empirical model of agricultural land use. In each case, we provide examples of both identified and non-identified counterfactuals of interest.