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Quantile models with endogeneity

  • Victor Chernozhukov

    ()

    (Institute for Fiscal Studies and MIT)

  • Christian Hansen

    (Institute for Fiscal Studies and Chicago GSB)

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 and Hansen (2005). We illustrate the modelling assumptions through economically motivated examples. We also briefly review the literature on estimation and inference.

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File URL: http://www.cemmap.ac.uk/wps/cwp251313.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP25/13.

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Date of creation: Jun 2013
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Handle: RePEc:ifs:cemmap:25/13
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  1. Markus Frölich & Blaise Melly, 2007. "Unconditional quantile treatment effects under endogeneity," CeMMAP working papers CWP32/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Alberto Abadie, 1997. "Changes in Spanish labor income structure during the 1980's: a quantile regression aproach," Investigaciones Economicas, Fundación SEPI, vol. 21(2), pages 253-272, May.
  3. Andrew Chesher & Adam Rosen & Konrad Smolinski, 2011. "An instrumental variable model of multiple discrete choice," CeMMAP working papers CWP39/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Sokbae Lee, 2004. "Endogeneity in Quantile Regression Models: A Control Function Approach," Econometric Society 2004 North American Summer Meetings 521, Econometric Society.
  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. 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.
  7. Hausman, Jerry A., 1977. "Errors in variables in simultaneous equation models," Journal of Econometrics, Elsevier, vol. 5(3), pages 389-401, May.
  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. 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.
  10. 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.
  11. Arie Beresteanu & Ilya Molchanov & Francesca Molinari, 2011. "Sharp Identification Regions in Models With Convex Moment Predictions," Econometrica, Econometric Society, vol. 79(6), pages 1785-1821, November.
  12. Joel L. Horowitz & Sokbae Lee, 2007. "Nonparametric Instrumental Variables Estimation of a Quantile Regression Model," Econometrica, Econometric Society, vol. 75(4), pages 1191-1208, 07.
  13. Ivar Ekeland & Alfred Galichon & Marc Henry, 2010. "Optimal transportation and the falsifiability of incompletely specified economic models," Economic Theory, Springer, vol. 42(2), pages 355-374, February.
  14. Ozkan Eren, 2009. "Does Membership Pay off for Covered Workers? A Distributional Analysis of the Free Rider Problem," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 62(3), pages 367-380, April.
  15. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
  16. Sakata, Shinichi, 2007. "Instrumental variable estimation based on conditional median restriction," Journal of Econometrics, Elsevier, vol. 141(2), pages 350-382, December.
  17. 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.
  18. Alex Maynard & Jiaping Qiu, 2009. "Public insurance and private savings: who is affected and by how much?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 282-308, 03.
  19. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
  20. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
  21. 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.
  22. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 487-535, October.
  23. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, 09.
  24. 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.
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