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A New Framework for Estimation of Quantile Treatment Effects Nonseparable Disturbance in the Presence of Covariates

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  • David Powell
Abstract
This paper introduces a new framework for quantile estimation. Quantile regression techniques have proven to be extremely valuable in understanding the relationship between explanatory variables and the conditional distribution of the outcome variable. Quantile regression allows the effect of the explanatory variables to vary based on a nonseparable disturbance term, frequently interpreted as "unobserved proneness" for the outcome, and provides conditional quantile treatment effects. Researchers are typically interested in the impact of the treatment variables on the unconditional distribution of the outcome. Additional covariates may be necessary (or simply desirable) for identification but adding these variables alters the interpretation of the resulting estimates as some of the "unobserved proneness" becomes observed and the disturbance term is separated. The Generalized Quantile Regression (GQR) estimator provides unconditional quantile treatment effects - the impact of the treatment variables on the unconditional distribution of the outcome variables. The control variables are conditioned on for identification or variance reduction but without altering the interpretation of the estimates. This property parallels mean regression. An IV version (IV-GQR) is also introduced. The estimator is extremely straightforward to implement using standard statistical software. Quantile Regression and Instrumental Variables Quantile Regression are special cases of the introduced estimation technique, but the proposed technique provides additional flexibility in the estimation of quantile treatment effects.

Suggested Citation

  • David Powell, 2013. "A New Framework for Estimation of Quantile Treatment Effects Nonseparable Disturbance in the Presence of Covariates," Working Papers WR-824-1, RAND Corporation.
    • Handle: RePEc:ran:wpaper:wr-824-1
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Paul Contoyannis & Jinhu Li, 2017. "The dynamics of adolescent depression: an instrumental variable quantile regression with fixed effects approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 907-922, June.
    2. Jerry Hausman & Haoyang Liu & Ye Luo & Christopher Palmer, 2021. "Errors in the Dependent Variable of Quantile Regression Models," Econometrica, Econometric Society, vol. 89(2), pages 849-873, March.
    3. Astrid Krenz & Ana Abeliansky, 2015. "Democracy and Trade—Evidence along the Distribution of Trading Activity," EcoMod2015 8750, EcoMod.
    4. Yao, Dongmin & Xu, Yixuan & Zhang, Pengyuan, 2019. "How a disaster affects household saving: Evidence from China’s 2008 Wenchuan earthquake," Journal of Asian Economics, Elsevier, vol. 64(C), pages 1-1.
    5. Lethiwe Nzama & Thanda Sithole & Sezer Bozkus Kahyaoglu, 2022. "The Impact of Government Effectiveness on Trade and Financial Openness: The Generalized Quantile Panel Regression Approach," JRFM, MDPI, vol. 16(1), pages 1-20, December.
    6. Ashraf, Sania & P., Jithin & Slim, Skander & Najeeb, Roshen, 2023. "Global value chains and economic complexity index: Evidence from generalized panel quantile regression," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 347-365.
    7. David Powell & Dana P. Goldman, 2014. "Moral Hazard and Adverse Selection in Private Health Insurance," Working Papers WR-1032, RAND Corporation.
    8. Armstrong, Christopher S. & Blouin, Jennifer L. & Jagolinzer, Alan D. & Larcker, David F., 2015. "Corporate governance, incentives, and tax avoidance," Journal of Accounting and Economics, Elsevier, vol. 60(1), pages 1-17.

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    More about this item

    Keywords

    Quantile Treatment Effects; Quantile Regression; Nonseparable disturbance;
    All these keywords.

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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