Identification in discrete choice models with imperfect information

• Tác giả: Cristina Gualdani, Shruti Sinha
• Ngôn ngữ: eng
• Ký hiệu phân loại: 003.56 Decision theory
• Thông tin xuất bản: 2019
• Bộ sưu tập: Metadata
• ID: 163588

Tóm tắt

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.