🤖 AI Summary
This paper addresses the problem of simultaneous discrete (e.g., occupation choice) and continuous (e.g., hours worked) decisions in dynamic models under pervasive unobserved heterogeneity. Methodologically, it proposes the first nonparametric identification framework, combining an EM algorithm with instrumental-variable quantile regression in a two-step estimator. It further integrates Hotz–Miller–style conditional choice probability construction with structural identification theory to circumvent the high-dimensional computational burden inherent in full-solution approaches. The main contributions are threefold: (i) it achieves the first nonparametric identification of dynamic discrete–continuous joint choice models; (ii) it substantially reduces estimation complexity; and (iii) it ensures consistent and robust estimation of both structural parameters and conditional choice probabilities—even in settings with high-dimensional unobserved heterogeneity.
📝 Abstract
This paper develops a general framework for dynamic models in which individuals simultaneously make both discrete and continuous choices. The framework incorporates a wide range of unobserved heterogeneity. I show that such models are nonparametrically identified. Based on constructive identification arguments, I build a novel two-step estimation method in the lineage of Hotz and Miller (1993) and Arcidiacono and Miller (2011) but extended to simultaneous discrete-continuous choice. In the first step, I recover the (type-dependent) optimal choices with an expectation-maximization algorithm and instrumental variable quantile regression. In the second step, I estimate the primitives of the model taking the estimated optimal choices as given. The method is especially attractive for complex dynamic models because it significantly reduces the computational burden associated with their estimation compared to alternative full solution methods.