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Unconditional Quantile Treatment Effects Under Endogeneity

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  • Markus Frölich
  • Blaise Melly

Abstract

This article develops estimators for unconditional quantile treatment effects when the treatment selection is endogenous. We use an instrumental variable (IV) to solve for the endogeneity of the binary treatment variable. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. We propose a weighting estimator that is extremely simple to implement. This estimator is root n consistent, asymptotically normally distributed, and its variance attains the semiparametric efficiency bound. We also show that including covariates in the estimation is not only necessary for consistency when the IV is itself confounded but also for efficiency when the instrument is valid unconditionally. An application of the suggested methods to the effects of fertility on the family income distribution illustrates their usefulness. Supplementary materials for this article are available online.

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File URL: http://hdl.handle.net/10.1080/07350015.2013.803869
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.

Volume (Year): 31 (2013)
Issue (Month): 3 (July)
Pages: 346-357

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Handle: RePEc:taf:jnlbes:v:31:y:2013:i:3:p:346-357

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  1. Andrew Chesher, 2003. "Nonparametric identification under discrete variation," CeMMAP working papers CWP19/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Markus Froelich, 2002. "Nonparametric IV estimation of local average treatment effects with covariates," University of St. Gallen Department of Economics working paper series 2002 2002-19, Department of Economics, University of St. Gallen.
  3. Markus Frölich, 2006. "A Note on Parametric and Nonparametric Regression in the Presence of Endogenous Control Variables," University of St. Gallen Department of Economics working paper series 2006 2006-11, Department of Economics, University of St. Gallen.
  4. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
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