Dummy endogenous variables in weakly separable models

• 成果类型: Article
• 署名作者: Vytlacil, Edward; Yildiz, Nese
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/j.1468-0262.2007.00767.x
• 发表日期: 2007
• 页码: 757-779
• 关键词: LATENT INDEX MODELS; binary choice; identification; estimators

摘要

In this paper, we consider the nonparametric identification and estimation of the average effect of a dummy endogenous regressor in models where the regressors are weakly but not additively separable from the error term. The model is not required to be strictly increasing in the error term, and the class of models considered includes limited dependent variable models such as discrete choice models. Conditions are established conditions under which it is possible to identify the average effect of the dummy endogenous regressor in a weakly separable model without relying on parametric functional form or distributional assumptions and without the use of large support conditions.