.png){kind=icon}
.png){kind=icon}
Return
Instrumental values
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jeconom.2006.06.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper studies identification of partial differences of nonseparable structural functions. A model is defined which admits structural functions exhibiting a degree of monotonicity with respect to a latent variate. The model identifies partial differences when there are instrumental values of covariates over which the latent variate exhibits a local quantile invariance, and a local order condition holds. The result is useful when covariates exhibit discrete variation, as arises often in practice, and when restricting latent variates and covariates to be statistically independent is unpalatable. The results are illustrated with data from the returns-to-schooling study of Angrist and Krueger [1991. Does compulsory schooling attendance affect schooling and earnings? Quarterly Journal of Economics 106, 979-1014]. (C) 2006 Elsevier B.V. All rights reserved.
Keywords:
identification
nonseparable models
nonparametric identification
endogeneity
instrumental variables
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.2K
Citations:
3.0W
Organization
No organization information available