Machine Learning for Dynamic Discrete Choice

Understand

Dynamic discrete choice models often discretize the state vector and restrict its dimension in order to achieve valid inference.

• I propose a novel two-stage estimator for the set-identified structural parameter that incorporates a high-dimensional state space into the dynamic model of imperfect competition.
• In the first stage, I estimate the state variable's law of motion and the equilibrium policy function using machine learning tools.
• In the second stage, I plug the first-stage estimates into a moment inequality and solve for the structural parameter.