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

TL;DR

This paper introduces a new, efficient method for estimating dynamic discrete choice models with 2-period finite dependence, improving computational feasibility and accuracy.

Contribution

It characterizes finite dependence as a sequential weights search problem and develops a novel estimator leveraging Kronecker product structure.

Findings

The proposed estimator is computationally efficient.

Monte Carlo simulations confirm estimator accuracy.

Finite dependence can be effectively characterized and utilized.

Abstract

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 sequentially searching for weights and introduce a computationally efficient method for determining these weights by utilizing the Kronecker product structure embedded in state transitions. With the estimated weights, 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.