Minimum Distance from Independence Estimation of Nonseparable Instrumental Variables Models

45 Pages Posted: 14 Apr 2016

Date Written: April 9, 2016

Abstract

I develop a semiparametric minimum distance from independence estimator for a nonseparable instrumental variables model. An independence condition identifies the model for many types of discrete and continuous instruments. The estimator is taken as the parameter value that most closely satisfies this independence condition. Implementing the estimator requires a quantile regression of the endogenous variables on the instrument, so the procedure is two-step, with a finite or infinite-dimensional nuisance parameter in the first step. I prove consistency and establish asymptotic normality for a parametric, but flexibly nonlinear outcome equation. The consistency of the nonparametric bootstrap is also shown. I illustrate the use of the estimator by estimating the returns to schooling using data from the 1979 National Longitudinal Survey.

Keywords: Nonseparable models, instrumental variables, quantile regression, two-step estimation, minimum distance from independence, unobserved heterogeneity

JEL Classification: C14, C20, C51

Suggested Citation

Torgovitsky, Alexander, Minimum Distance from Independence Estimation of Nonseparable Instrumental Variables Models (April 9, 2016). Available at SSRN: https://ssrn.com/abstract=2761683 or http://dx.doi.org/10.2139/ssrn.2761683

Alexander Torgovitsky (Contact Author)

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

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