Cristina Gualdani (Toulouse)
"Identification in Discrete Choice Models with Imperfect Information"
Abstract: We study identification of preferences in a class of single-agent, static, discrete choice models where decision makers may be imperfectly informed about the payoffs generated by the available alternatives. Decision makers rely on a common family of priors. They are allowed to update their priors by processing any possible signals, on which we remain agnostic. As we remain agnostic about signals, our methodology correctly recovers preferences under both perfect information and a range of models with information frictions. We leverage on the notion of one-player Bayes Correlated Equilibrium in Bergemann and Morris (2016) to provide a tractable characterisation of the identified set. We use our methodology and data on the 2017 UK general election to estimate a spatial model of voting under weak assumptions on the information that voters have about the returns to voting. Counterfactual exercises quantify the consequences of imperfect information in politics.
When: Monday 1st February 2021 - 1:00pm
Where: Zoom
Reading Group: Theory Workshop