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STICERD Econometrics Seminar Series

Machine Learning for Dynamic Discrete Choice

Vira Semenova (MIT), joint with joint work with Victor Chernozhukov and Whitney Newey

Thursday 12 December 2019 14:00 - 15:30

Due to the onging coronavirus outbreak, many of our seminars and public events this year will continue as online seminars. Please check our website listings and Twitter feed @STICERD_LSE for updates.

About this event

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 firststage estimates into a moment inequality and solve for the structural parameter. The moment function is presented as the sum of two components, where the first one expresses the equilibrium assumption and the second one is a bias correction term that makes the sum insensitive (i.e., Neyman-orthogonal) to first-stage bias. The proposed estimator uniformly converges at the root-N rate and I use it to construct confidence regions. The results developed here can be used to incorporate high-dimensional state space into classic dynamic discrete choice models, for example, those considered in Rust (1987), Bajari et al. (2007), and Scott (2013).

STICERD Econometrics seminars are held on Thursdays in term time at 14.00-15.30, ONLINE, unless specified otherwise.

Seminar organisers: Professor Tai Otsu and Dr. Vassilis Hajivassiliou.

For further information please contact Lubala Chibwe, either by email:

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