Title: Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models
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Abstract: We propose a tractable semiparametric estimation method for dynamic discrete choice models. 
These models describe optimal decision making by economic agents in dynamic settings where the decision 
in the current time period affects the current utility of the agent, the evolution of state variables, and, thus, 
the future utilities. The models are especially useful for counterfactual policy analysis. The distribution of 
additive utility shocks in the proposed framework is modeled by location-scale mixtures of extreme value 
distributions with varying numbers of mixture components. Our approach exploits the analytical tractability 
of extreme value distributions in the multinomial choice settings and the flexibility of the location-scale mixtures.
We implement the Bayesian approach to inference using Hamiltonian Monte Carlo and an approximately optimal reversible 
jump algorithm. In our simulation experiments, we show that the standard parametric model can deliver misleading results, 
especially about counterfactuals. Our semiparametric approach delivers reliable inference in these settings. 
We develop theoretical results on approximations by location-scale mixtures in an appropriate distance and
posterior concentration of the set identified utility parameters and the distribution of shocks in the model. 
This talk is based on joint work with Kenichi Shimizu.