IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2401.11422.html
   My bibliography  Save this paper

Local Identification in Instrumental Variable Multivariate Quantile Regression Models

Author

Listed:
  • Haruki Kono

Abstract

In the instrumental variable quantile regression (IVQR) model of Chernozhukov and Hansen (2005), a one-dimensional unobserved rank variable monotonically determines a single potential outcome. Even when multiple outcomes are simultaneously of interest, it is common to apply the IVQR model to each of them separately. This practice implicitly assumes that the rank variable of each regression model affects only the corresponding outcome and does not affect the other outcomes. In reality, however, it is often the case that all rank variables together determine the outcomes, which leads to a systematic correlation between the outcomes. To deal with this, we propose a nonlinear IV model that allows for multivariate unobserved heterogeneity, each of which is considered as a rank variable for an observed outcome. We show that the structural function of our model is locally identified under the assumption that the IV and the treatment variable are sufficiently positively correlated.

Suggested Citation

  • Haruki Kono, 2024. "Local Identification in Instrumental Variable Multivariate Quantile Regression Models," Papers 2401.11422, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2401.11422
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2401.11422
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard Blundell & Dennis Kristensen & Rosa Matzkin, 2017. "Individual counterfactuals with multidimensional unobserved heterogeneity," CeMMAP working papers 60/17, Institute for Fiscal Studies.
    2. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    2. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    3. 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.
    4. Dima, Bogdan & Dincă, Marius Sorin & Spulbăr, Cristi, 2014. "Financial nexus: Efficiency and soundness in banking and capital markets," Journal of International Money and Finance, Elsevier, vol. 47(C), pages 100-124.
    5. 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.
    6. Kang, Changhui, 2007. "Classroom peer effects and academic achievement: Quasi-randomization evidence from South Korea," Journal of Urban Economics, Elsevier, vol. 61(3), pages 458-495, May.
    7. Victor Chernozhukov & Iván Fernández‐Val & Whitney Newey & Sami Stouli & Francis Vella, 2020. "Semiparametric estimation of structural functions in nonseparable triangular models," Quantitative Economics, Econometric Society, vol. 11(2), pages 503-533, May.
    8. 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.
    9. Jun Zhang & Yuang He & Jing Zhang, 2022. "Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach," IJERPH, MDPI, vol. 19(2), pages 1-21, January.
    10. Xin Liu, 2024. "Averaging Estimation for Instrumental Variables Quantile Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(5), pages 1290-1312, October.
    11. Jia-Young Michael Fu & Joel L. Horowitz & Matthias Parey, 2015. "Testing exogeneity in nonparametric instrumental variables identified by conditional quantile restrictions," CeMMAP working papers 68/15, Institute for Fiscal Studies.
    12. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 123-128, August.
    13. Aguiar, Victor H. & Kashaev, Nail & Allen, Roy, 2023. "Prices, profits, proxies, and production," Journal of Econometrics, Elsevier, vol. 235(2), pages 666-693.
    14. Haoming Liu, 2014. "The quality–quantity trade-off: evidence from the relaxation of China’s one-child policy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 27(2), pages 565-602, April.
    15. 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.
    16. Imai, Susumu & Katayama, Hajime & Krishna, Kala, 2013. "A quantile-based test of protection for sale model," Journal of International Economics, Elsevier, vol. 91(1), pages 40-52.
    17. Choonsung Park, 2020. "Consumption, Reservation Wages, and Aggregate Labor Supply," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 37, pages 54-80, July.
    18. Panagiotidis, Theodore & Printzis, Panagiotis, 2021. "Investment and uncertainty: Are large firms different from small ones?," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 302-317.
    19. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    20. Hiroaki Kaido & Kaspar Wüthrich, 2021. "Decentralization estimators for instrumental variable quantile regression models," Quantitative Economics, Econometric Society, vol. 12(2), pages 443-475, May.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2401.11422. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.