Estimating Semi-Parametric Panel Multinomial Choice Models Using Cyclic Monotonicity

• 成果类型: Article
• 署名作者: Shi, Xiaoxia; Shum, Matthew; Song, Wei
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; California Institute of Technology; Xiamen University; Xiamen University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA14115
• 发表日期: 2018
• 页码: 737-761
• 关键词: RANK CORRELATION ESTIMATOR; instrumental variables; UNIFORM-CONVERGENCE; REGRESSION-MODEL; linear-models; identification; inference; MARKETS

摘要

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.