Estimating Semi-Parametric Panel Multinomial Choice Models Using Cyclic Monotonicity
提出一种基于循环单调性的半参数识别与估计方法，用于面板数据中带个体固定效应的多项选择模型，无需对随机效用冲击的分布施加形状限制。
This paper proposes a new semi‐parametric identification and estimation approach to multinomial choice models in a panel data setting with individual fixed effects. Our approach is based on cyclic monotonicity, which is a defining convex‐analytic feature of the random utility framework underlying multinomial choice models. From the cyclic monotonicity property, we derive identifying inequalities without requiring any shape restrictions for the distribution of the random utility shocks. These inequalities point identify model parameters under straightforward assumptions on the covariates. We propose a consistent estimator based on these inequalities.