Exploiting results from the literature on non-parametric identification, we make three methodological contributions to the empirical literature estimating the matching function, commonly used to map unemployment and vacancies into hires. First, we show how to non-parametrically identify the...

Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non-identification of models does not necessarily imply the nonidentification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non-additive changes in...

We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures. In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and...