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Home | Events Archive | Identification and inference in discretechoice models with imperfect information

Identification and inference in discretechoice models with imperfect information

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    March 06, 2020
    16:00 - 17:15

We study identification and inference of preference parameters in a single-agent, static, discretechoice model where the decision maker may face attentional limits precluding her to exhaustivelyacquire information about the payoffs of the available alternatives. Instead of explicitly modelling theinformation constraints, which can be susceptible to misspecification, we leverage on the notion ofone-player Bayesian Correlated Equilibrium in Bergemann and Morris (2016) to provide a tractablecharacterisation of the sharp identified set and discuss inference under minimal assumptions on theamount of information processed by the decision maker. Simulations reveal that the obtained boundson the preference parameters can be tight in several settings of empirical interest. Joint with Shruti Sinha

Keywords: Discrete choice model, Bayesian Persuasion, Bayes Correlated Equilibrium, IncompleteInformation, Partial Identification, Moment Inequalities

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