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

Abstract

This paper investigates conditional choice probability es timation 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-peri od dependence is obtainable in any dynamic structural model. This finite dependence prop erty also provides a convenient and computationally cheap representation of the optimality conditions for the continuous choice variables. I allow for a general form of discrete-valued unobserved heterogeneity in utilities, transition probabilities, an d 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. I apply our method to estimate a model of education and labor supply choices to investigate properties of t he distribution of returns to education, using data from the National Longitudinal Survey of Youth 1979.