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Quantile Models with Endogeneity

  • V. Chernozhukov

    (Department of Economics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142)

  • C. Hansen

    ()

    (The University of Chicago Booth School of Business, Chicago, Illinois 60637)

In this article, we review quantile models with endogeneity. We focus on models that achieve identification through the use of instrumental variables and discuss conditions under which partial and point identification are obtained. We discuss key conditions, which include monotonicity and full-rank-type conditions, in detail. In providing this review, we update the identification results of Chernozhukov & Hansen (2005). We illustrate the modeling assumptions through economically motivated examples. We also briefly review the literature on estimation and inference.

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File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080511-110952
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Article provided by Annual Reviews in its journal Annual Review of Economics.

Volume (Year): 5 (2013)
Issue (Month): 1 (05)
Pages: 57-81

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Handle: RePEc:anr:reveco:v:5:y:2013:p:57-81
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  1. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
  2. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
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  4. Victor Chernozhukov & Sokbae Lee & Adam Rosen, 2012. "Intersection bounds: estimation and inference," CeMMAP working papers CWP33/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. Marmer, Vadim & Sakata, Shinichi, 2011. "Instrumental Variables Estimation and Weak-Identification-Robust Inference Based on a Conditional Quantile Restriction," Microeconomics.ca working papers vadim_marmer-2011-26, Vancouver School of Economics, revised 28 Sep 2011.
  6. Markus Frölich & Blaise Melly, 2013. "Unconditional Quantile Treatment Effects Under Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 346-357, July.
  7. Lamarche, Carlos, 2011. "Measuring the incentives to learn in Colombia using new quantile regression approaches," Journal of Development Economics, Elsevier, vol. 96(2), pages 278-288, November.
  8. Galichon, Alfred & Henry, Marc, 2009. "A test of non-identifying restrictions and confidence regions for partially identified parameters," Journal of Econometrics, Elsevier, vol. 152(2), pages 186-196, October.
  9. Sakata, Shinichi, 2007. "Instrumental variable estimation based on conditional median restriction," Journal of Econometrics, Elsevier, vol. 141(2), pages 350-382, December.
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  16. Arie Beresteanu & Ilya Molchanov & Francesca Molinari, 2010. "Sharp identification regions in models with convex moment predictions," CeMMAP working papers CWP25/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  17. Hausman, Jerry A., 1977. "Errors in variables in simultaneous equation models," Journal of Econometrics, Elsevier, vol. 5(3), pages 389-401, May.
  18. David H. Autor & Susan N. Houseman & Sari Pekkala Kerr, 2012. "The Effect of Work First Job Placements on the Distribution of Earnings: An Instrumental Variable Quantile Regression Approach," NBER Working Papers 17972, National Bureau of Economic Research, Inc.
  19. Philip Kostov, 2009. "A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(1), pages 53-72.
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  24. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
  25. Honore, Bo E & Hu, Luojia, 2004. "On the Performance of Some Robust Instrumental Variables Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 30-39, January.
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