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
Many of our seminars and public events this year will continue as in person or as hybrid (online and in person) events. Please check our website listings and Twitter feed @STICERD_LSE for updates.
Unless otherwise specified, in-person seminars are open to the public.
Those unable to join the seminars in-person are welcome to participate via zoom if the event is hybrid.
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.
For further information please contact Lubala Chibwe, either by email: firstname.lastname@example.org.
Please use this link to subscribe or unsubscribe to STICERD Econometrics mailing list (emetrics).