Nonparametric Bounds on Treatment Effects with Imperfect Instruments
Kyunghoon Ban and
ISU General Staff Papers from Iowa State University, Department of Economics
This paper extends the identification results in Nevo and Rosen(2012) to nonparametric models. We derive nonparametric bounds on the averagetreatment effect when an imperfect instrument is available. As in Nevo andRosen (2012), we assume that the correlation between the imperfect instrumentand the unobserved latent variables has the same sign as the correlationbetween the endogenous variable and the latent variables. We show that themonotone treatment selection and monotone instrumental variable restrictions,introduced by Manski and Pepper (2000, 2009), jointly imply this assumption.We introduce the concept of comonotone instrumental variable, which alsosatisfies this assumption. Moreover, we show how the assumption that theimperfect instrument is less endogenous than the treatment variable can helptighten the bounds. We also use the monotone treatment response assumption toget tighter bounds. The identified set can be written in the form ofintersection bounds, which is more conducive to inference. We illustrate ourmethodology using the National Longitudinal Survey of Young Men data toestimate returns to schooling.
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://lib.dr.iastate.edu/cgi/viewcontent.cgi?art ... t=econ_workingpapers
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:isu:genstf:202010120700001113
Access Statistics for this paper
More papers in ISU General Staff Papers from Iowa State University, Department of Economics Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070. Contact information at EDIRC.
Bibliographic data for series maintained by Curtis Balmer ().