Identification and estimation of semiparametric two-step models

• 成果类型: Article
• 署名作者: Escanciano, Juan Carlos; Jacho-Chavez, David; Lewbel, Arthur
• 署名单位: Indiana University System; Indiana University Bloomington; Emory University; Boston College
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE328
• 发表日期: 2016
• 页码: 561-589
• 关键词: Identification by functional form; double index models; two-step estimators; semiparametric regression; control function estimators; sample selection models; empirical process theory; limited dependent variables; migration

摘要

Let H-0(X) be a function that can be nonparametrically estimated. Suppose E[Y vertical bar X] = F-0[X-inverted perpendicular beta(0), H-0(X)]. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector beta(0) and unknown function F-0 are generally point identified without exclusion restrictions or instruments, in contrast to the usual assumption that identification without instruments requires fully specified functional forms. We propose an estimator with asymptotic properties allowing for data dependent bandwidths and random trimming. A Monte Carlo experiment and an empirical application to migration decisions are also included.