CCP Estimation of Dynamic Discrete/Continuous Choice Models with Generalized Finite Dependence and Correlated Unobserved Heterogeneity

This paper investigates conditional choice probability estimation of dynamic structural discrete and continuous choice models. I extend the concept of finite dependence in a way that accommodates non-stationary, irreducible transition probabilities. I show that under this new definition of finite dependence, one-period dependence is obtainable in any dynamic structural model with non-degenerate transition functions. This finite dependence property also provides a convenient and computationally cheap representation of the optimality conditions for the continuous choice variables. I allow for discrete-valued unobserved heterogeneity in utilities, transition probabilities, and production functions. The unobserved heterogeneity may be correlated with the observable state variables. I show the estimator is root-n--asymptotically normal. I develop a new and computationally cheap algorithm to compute the estimator, and analyse the finite sample properties of this estimator via Monte Carlo techniques. I apply the proposed method to estimate a model of education and labor supply choices to investigate properties of the distribution of returns to education, using data from the National Longitudinal Survey of Youth 1979