Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/150417 
Erscheinungsjahr: 
2016
Quellenangabe: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 7 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2016 [Pages:] 561-589
Verlag: 
The Econometric Society, New Haven, CT
Zusammenfassung: 
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 der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by-nc Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.