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Semiparametric Estimation of Instrumental Variable Models for Causal Effects

42 Pages Posted: 16 Sep 2000 Last revised: 19 Sep 2010

Alberto Abadie

Harvard University - Harvard Kennedy School (HKS); National Bureau of Economic Research (NBER)

Date Written: September 2000

Abstract

This article introduces a new class of instrumental variable (IV) estimators of causal treatment effects for linear and nonlinear models with covariates. The rationale for focusing on nonlinear models is to improve the approximation to the causal response function of interest. For example, if the dependent variable is binary or limited, or if the effect of the treatment varies with covariates, a nonlinear model is likely to be appropriate. However, identification is not attained through functional form restrictions. This paper shows how to estimate a well-defined approximation to a nonlinear causal response function of unknown functional form using simple parametric models. As an important special case, I introduce a linear model that provides the best linear approximation to an underlying causal relation. It is shown that Two Stage Least Squares (2SLS) does not always have this property and some possible interpretations of 2SLS coefficients are brie y studied. The ideas and estimators in this paper are illustrated using instrumental variables to estimate the effects of 401(k) retirement programs on savings.

Suggested Citation

Abadie, Alberto, Semiparametric Estimation of Instrumental Variable Models for Causal Effects (September 2000). NBER Working Paper No. t0260. Available at SSRN: https://ssrn.com/abstract=242120

Alberto Abadie (Contact Author)

Harvard University - Harvard Kennedy School (HKS) ( email )

79 John F. Kennedy Street
Cambridge, MA 02138
United States
617-496-4547 (Phone)
617-495-2575 (Fax)

National Bureau of Economic Research (NBER)

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Cambridge, MA 02138
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