CCP Estimation of Dynamic Discrete Choice Models with Unobserved Heterogeneity

Standard methods for solving dynamic discrete choice models involve calculating the value function either through backwards recursion (finite-time) or through the use of a fixed point algorithm (infinite-time). Conditional choice probability (CCP) estimators provide a computationally cheaper alternative but are perceived to be limited both by distributional assumptions and by being unable to incorporate unobserved heterogeneity via finite mixture distributions. We extend the classes of CCP estimators that need only a small number of CCP’s for estimation. We also show that not only can finite mixture distributions be used in conjunction with CCP estimation, but, because of the computational simplicity of the estimator, an individual’s location in unobserved space can transition over time. Finally, we show that when data is available on outcomes that are not the dynamic discrete choices themselves but are affected by the unobserved

state, the distribution of the unobserved heterogeneity can be estimated in a first stage while still accounting for dynamic selection. The second stage then operates as though the unobserved state is observed where the conditional probabilities of being in particular unobserved states are calculated in the first stage and used as weights in the second stage. Monte Carlo results suggest that the algorithms developed are computationally cheap with little loss in precision.

For more information, contact Petra Todd.