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Quantile Models with Endogeneity

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • V. Chernozhukov & C. Hansen, 2013. "Quantile Models with Endogeneity," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 57-81, May.
  • Handle: RePEc:anr:reveco:v:5:y:2013:p:57-81
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    3. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    4. Balestra, Simone & Backes-Gellner, Uschi, 2017. "Heterogeneous returns to education over the wage distribution: Who profits the most?," Labour Economics, Elsevier, vol. 44(C), pages 89-105.
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    9. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
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    11. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    12. Christina Christou & Ruthira Naraidoo & Rangan Gupta & Won Joong Kim, 2018. "Monetary Policy Reaction Functions of the TICKs: A Quantile Regression Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(15), pages 3552-3565, December.
    13. Simone Balestra & Uschi Backes-Gellner, 2014. "Heterogeneous effects of pupil-to-teacher ratio policies - A look at class size reduction and teacher aide," Economics of Education Working Paper Series 0102, University of Zurich, Department of Business Administration (IBW), revised Apr 2017.
    14. Apergis, Nicholas & Christou, Christina, 2015. "The behaviour of the bank lending channel when interest rates approach the zero lower bound: Evidence from quantile regressions," Economic Modelling, Elsevier, vol. 49(C), pages 296-307.
    15. David Powell, 2020. "Quantile Treatment Effects in the Presence of Covariates," The Review of Economics and Statistics, MIT Press, vol. 102(5), pages 994-1005, December.
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    18. Blaise Melly und Kaspar Wüthrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    19. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    20. Valizadeh, Pourya & Smith, Travis A., 2017. "How Did the American Recovery and Reinvestment Act (ARRA) Impact the Material Well-being of SNAP Participants? A Distributional Approach," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258496, Agricultural and Applied Economics Association.
    21. Paul. B. Kenfac Dongmezo & P. N. Mwita & I. R. Kamga Tchwaket, 2018. "Distributive and Quantile Treatment Effects: Imputation Based Estimators Approach," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 7(2), pages 1-3.
    22. Kaspar Wüthrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    23. Han, Jidong & Popkowski Leszczyc, Peter T.L. & Zhang, Zelin, 2021. "Empirical Analyses of Nonlinear Effects of Reserve Prices on Ending Prices in Online Auctions," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 86-102.

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    More about this item

    Keywords

    identification; treatment effects; structural models; instrumental variables;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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