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不完全信息离散选择模型中的识别AI 解读

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. Leveraging the notion of one-player Bayes Correlated Equilibrium by Bergemann and Morris (2016), we provide a tractable characterisation 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…

婚姻市场匹配模型中的部分识别AI 解读

We study partial identification of the preference parameters in the one-to-one matching model with perfectly transferable utilities. We do so without imposing parametric distributional assumptions on the unobserved heterogeneity and with data on one large market. We provide a tractable characterization of the identified set under various classes of nonparametric distributional assumptions on the unobserved heterogeneity. We reexamine some of the relevant questions in the…

网络形成的计量模型：企业董事交叉任职应用AI 解读

We study identification of the players’ preferences in a network formation game featuring complete information, nonreciprocal links, and a spillover effect. We decompose the network formation game into local games such that the network formation game is in equilibrium if and only if each local game is in equilibrium. This decomposition helps us prove equilibrium existence, reduce the number of moment inequalities characterising the identified set, and simplify the…