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Instrumental variables estimation of average treatment effects in econometrics and epidemiology

Author: Joshua David Angrist; National Bureau of Economic Research.
Publisher: Cambridge, Mass. : National Bureau of Economic Research, 1991.
Series: NBER technical working paper series, no. 115.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
The average effect of intervention or treatment is a parameter of interest in both epidemiology and econometrics. A key difference between applications in the two fields is that epidemiologic research is more likely to involve qualitative outcomes and nonlinear models. An example is the recent use of the Vietnam era draft lottery to construct estimates of the effect of Vietnam era military service on civilian  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Joshua David Angrist; National Bureau of Economic Research.
OCLC Number: 71823372
Notes: Title from http://www.nber.org/papers/t0115 viewed May 9, 2013.
"November 1991."
Description: 1 online resource (34 pages) : illustrations.
Series Title: NBER technical working paper series, no. 115.
Responsibility: Joshua D. Angrist.

Abstract:

The average effect of intervention or treatment is a parameter of interest in both epidemiology and econometrics. A key difference between applications in the two fields is that epidemiologic research is more likely to involve qualitative outcomes and nonlinear models. An example is the recent use of the Vietnam era draft lottery to construct estimates of the effect of Vietnam era military service on civilian mortality. In this paper. I present necessary and sufficient conditions for linear instrumental variables. techniques to consistently estimate average treatment effects in qualitative or other nonlinear models. Most latent index models commonly applied to qualitative outcomes in econometrics fail to satisfy these conditions, and monte carlo evidence on the bias of instrumental estimates of the average treatment effect in a bivariate probit model is presented. The evidence suggests that linear instrumental variables estimators perform nearly as well as the correctly specified maximum likelihood estimator. especially in large samples. Linear instrumental variables and the normal maximum likelihood estimator are also remarkably robust to non-normality.

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