This paper establishes conditions for nonparametric identification of dynamic
optimization models in which agents make both discrete and continuous choices.
We consider identification of both the payoff function and the distribution of unobservables.
Models of this kind are prevalent in applied microeconomics and
many of the required conditions are standard assumptions currently used in empirical
work. We focus on conditions on the model that can be implied by economic
theory and assumptions about the data generating process that are likely
to be satisfied in a typical application. Our analysis is intended to highlight the
identifying power of each assumption individually, where possible, and our proofs
are constructive in nature.
Keywords. Nonparametric identification, Markov decision processes, dynamic
decision processes, discrete choice, continuous choice.
JEL classification. C14, C23, C25, C51.