This paper develops a general framework for models, static or dynamic, in which agents
simultaneously make both discrete and continuous choices. I show that such models are nonparametrically
identified. Based on the constructive identification arguments, I build a novel
two-step estimation method in the lineage of Hotz and Miller (1993) but extended to discrete
and continuous choice models. The method is especially attractive for complex dynamic models
because it significantly reduces the computational burden associated with their estimation. To
illustrate my new method, I estimate a dynamic model of female labor supply and consumption
