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Identification of quantile regression models with endogeneity

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  • Wang, Qian

Abstract

The instrumental variables approach is a standard practice to address endogeneity, while it is often challenging to obtain valid and strong instruments in many empirical studies. This paper considers a linear simultaneous triangular system of quantile regressions in the absence of instruments. We establish identification results for this model by leveraging copula of the latent error terms to characterize their dependence structure.

Suggested Citation

  • Wang, Qian, 2025. "Identification of quantile regression models with endogeneity," Economics Letters, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:ecolet:v:254:y:2025:i:c:s0165176525002368
    DOI: 10.1016/j.econlet.2025.112399
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    References listed on IDEAS

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    1. Blundell, Richard & Powell, James L., 2007. "Censored regression quantiles with endogenous regressors," Journal of Econometrics, Elsevier, vol. 141(1), pages 65-83, November.
    2. Juan Carlos Escanciano & David Jacho‐Chávez & Arthur Lewbel, 2016. "Identification and estimation of semiparametric two‐step models," Quantitative Economics, Econometric Society, vol. 7(2), pages 561-589, July.
    3. Ruosha Li & Limin Peng, 2015. "Quantile regression adjusting for dependent censoring from semicompeting risks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 107-130, January.
    4. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, Enero-Abr.
    6. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    7. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    8. Han, Sukjin & Vytlacil, Edward J., 2017. "Identification in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Econometrics, Elsevier, vol. 199(1), pages 63-73.
    9. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    10. 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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    More about this item

    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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