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Instrumental-variables quantile regression

Author

Listed:
  • Di Liu

    (StataCorp)

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. I introduce the Stata command ivqregress, which models the quantiles of the outcome and simultaneously controls for problems that arise from endogeneity. I show how to use the suite of instrumental-variables quantile regression commands to estimate, visualize, and infer features of the outcome distribution.

Suggested Citation

  • Di Liu, 2024. "Instrumental-variables quantile regression," French Stata Users' Group Meetings 2024 07, Stata Users Group.
    • Handle: RePEc:boc:fsug24:07
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    File URL: http://repec.org/frsug2024/France24_Liu.pdf
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    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.
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