Minimum distance from independence estimation of nonseparable instrumental variables models

Alexander Torgovitsky

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)35-48
Number of pages14
JournalJournal of Econometrics
Volume199
Issue number1
DOIs
StatePublished - Jul 1 2017

Keywords

  • Instrumental variables
  • Minimum distance from independence
  • Nonseparable models
  • Quantile regression
  • Two-step estimation
  • Unobserved heterogeneity

ASJC Scopus subject areas

  • Economics and Econometrics
  • Applied Mathematics
  • History and Philosophy of Science

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