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Instrumental variables quantile regression

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  • Di Liu

    (StataCorp LLC)

Abstract

When we want to study the effects of covariates on the different quantiles of the outcome, we use quantile regression. However, the traditional quantile regression is inconsistent when a covariate is endogenous. We introduce the Stata command ivqregress, which models the quantiles of the outcome and, at the same time, controls for problems that arise from endogeneity. We show how to use the suite of IV quantile regresison to estimate, visualize, and infer features of the outcome distribution.

Suggested Citation

  • Di Liu, 2024. "Instrumental variables quantile regression," Chinese Stata Conference 2023 07, Stata Users Group.
    • Handle: RePEc:boc:chin23:07
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    File URL: http://repec.org/chin2023/China23_Liu_IVQ.pdf
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    References listed on IDEAS

    as
    1. Kaplan, David M. & Sun, Yixiao, 2017. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
    2. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    3. Poterba, James M. & Venti, Steven F. & Wise, David A., 1995. "Do 401(k) contributions crowd out other personal saving?," Journal of Public Economics, Elsevier, vol. 58(1), pages 1-32, September.
    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.
    5. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    Full references (including those not matched with items on IDEAS)

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