Identification in discrete choice models with imperfect information
Abstract
We study identification of preferences in static single-agent discrete choice models where decision makers may be imperfectly informed about the state of the world. We leverage the notion of one-player Bayes Correlated Equilibrium by Bergemann and Morris (2016) to provide a tractable characterization of the sharp identified set. We develop a procedure to practically construct the sharp identified set following a sieve approach, and provide sharp bounds on counterfactual outcomes of interest. We use our methodology and data on the 2017 UK general election to estimate a spatial voting model under weak assumptions on agents' information about the returns to voting. Counterfactual exercises quantify the consequences of imperfect information on the well-being of voters and parties.
Cite
@article{arxiv.1911.04529,
title = {Identification in discrete choice models with imperfect information},
author = {Cristina Gualdani and Shruti Sinha},
journal= {arXiv preprint arXiv:1911.04529},
year = {2023}
}