Conditional Choice Probability Estimation of Dynamic Discrete Choice Models with 2-period Finite Dependence

This paper extends the work of Arcidiacono and Miller (2011, 2019) by introducing a novel characterization of finite dependence within dynamic discrete choice models, demonstrating that numerous models display 2-period finite dependence. We recast finite dependence as a problem of searching for weights sequentially, and introduce an computationally efficient method for determining these weights by utilizing the Kronecker product structure embedded in state transitions. With the estimated weight, we develop a computationally attractive Conditional Choice Probability estimator with 2-period finite dependence. The computational efficacy of our proposed estimator is demonstrated through Monte Carlo simulations.