Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/150417
Authors: 
Escanciano, Juan Carlos
Jacho-Chávez, David
Lewbel, Arthur
Year of Publication: 
2016
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 7 [Year:] 2016 [Issue:] 2 [Pages:] 561-589
Abstract: 
Let H<sub>0</sub>(X) be a function that can be nonparametrically estimated. Suppose E [Y&7CX]=F<sub>0</sub>[X⊤β<sub>0</sub>, H<sub>0</sub>(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 β<sub>0</sub> and unknown function F<sub>0</sub> 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.
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Article

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