IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/255-ec-2022.html
   My bibliography  Save this paper

Instrumental Variable Quantile Regression For Clustered Data

Author

Listed:
  • Galina Besstremyannaya

    (National Research University Higher School of Economics)

  • Sergei Golovan

    (National Research University Higher School of Economics)

Abstract

The purpose of the paper is to enable inference in case of quantile regression with endogenous covariates and clustered data. We prove that the instrumental variable quantile regression estimator is consistent where there is correlation of errors within clusters. We derive an asymptotic distribution for the estimator, which may be used for inference for a given tau. As regards inference based on the entire instrumental variable quantile regression process, we prove that cluster-based bootstrapping of a statistic of a certain class offers a computationally tractable approach for implementing asymptotic tests. Our theoretical results concerning the asymptotic properties of the instrumental variable quantile regression estimator for clustered data are supported by simulation analysis. The empirical part of the paper applies the technique to estimation of the earning equations of US men and women where female labor supply is endogenous and subject to the shock of World War II

Suggested Citation

  • Galina Besstremyannaya & Sergei Golovan, 2022. "Instrumental Variable Quantile Regression For Clustered Data," HSE Working papers WP BRP 255/EC/2022, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:255/ec/2022
    as

    Download full text from publisher

    File URL: https://wp.hse.ru/data/2022/06/14/1854950053/255EC2022.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Katarína Borovičková & Robert Shimer, 2017. "High Wage Workers Work for High Wage Firms," NBER Working Papers 24074, National Bureau of Economic Research, Inc.
    2. Andrews, M.J. & Gill, L. & Schank, T. & Upward, R., 2012. "High wage workers match with high wage firms: Clear evidence of the effects of limited mobility bias," Economics Letters, Elsevier, vol. 117(3), pages 824-827.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Juhn, Chinhui & Kim, Dae Il, 1999. "The Effects of Rising Female Labor Supply on Male Wages," Journal of Labor Economics, University of Chicago Press, vol. 17(1), pages 23-48, January.
    5. Jörn Boehnke & Victor Gay, 2022. "The Missing Men: World War I and Female Labor Force Participation," Journal of Human Resources, University of Wisconsin Press, vol. 57(4), pages 1209-1241.
    6. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    7. Van Reenen, John, 2011. "Wage inequality, technology and trade: 21st century evidence," Labour Economics, Elsevier, vol. 18(6), pages 730-741.
    8. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    9. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    10. Parente Paulo M.D.C. & Santos Silva João M.C., 2016. "Quantile Regression with Clustered Data," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 1-15, January.
    11. Psacharopoulos, George & Tzannatos, Zafiris, 1989. "Female Labor Force Participation: An International Perspective," The World Bank Research Observer, World Bank, vol. 4(2), pages 187-201, July.
    12. Boehnke, Jörn & Gay, Victor, 2020. "The Missing Men: World War I and Female Labor Force Participation," TSE Working Papers 20-1064, Toulouse School of Economics (TSE).
    13. Cain, Glen G & Dooley, Martin D, 1976. "Estimation of a Model of Labor Supply, Fertility, and Wages of Married Women," Journal of Political Economy, University of Chicago Press, vol. 84(4), pages 179-199, August.
    14. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 318, University of California, Davis, Department of Economics.
    15. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, March.
    16. Jacob Mincer, 1962. "Labor Force Participation of Married Women: A Study of Labor Supply," NBER Chapters, in: Aspects of Labor Economics, pages 63-105, National Bureau of Economic Research, Inc.
    17. Daron Acemoglu & David H. Autor & David Lyle, 2004. "Women, War, and Wages: The Effect of Female Labor Supply on the Wage Structure at Midcentury," Journal of Political Economy, University of Chicago Press, vol. 112(3), pages 497-551, June.
    18. Andreas Hagemann, 2017. "Cluster-Robust Bootstrap Inference in Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 446-456, January.
    19. Heckman, James J, 1978. "A Partial Survey of Recent Research on the Labor Supply of Women," American Economic Review, American Economic Association, vol. 68(2), pages 200-207, May.
    20. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    21. Alberto Abadie, 1997. "Changes in Spanish labor income structure during the 1980's: a quantile regression aproach," Investigaciones Economicas, Fundación SEPI, vol. 21(2), pages 253-272, May.
    22. Rose, Evan K., 2018. "The Rise and Fall of Female Labor Force Participation During World War II in the United States," The Journal of Economic History, Cambridge University Press, vol. 78(3), pages 673-711, September.
    23. Raquel Fernández & Alessandra Fogli & Claudia Olivetti, 2004. "Mothers and Sons: Preference Formation and Female Labor Force Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(4), pages 1249-1299.
    24. 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.
    25. Moshe Buchinsky, 1998. "The dynamics of changes in the female wage distribution in the USA: a quantile regression approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 1-30.
    26. Hong H. & Chernozhukov V., 2002. "Three-Step Censored Quantile Regression and Extramarital Affairs," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 872-882, September.
    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. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    2. Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
    3. Yinchu Zhu, 2018. "Learning non-smooth models: instrumental variable quantile regressions and related problems," Papers 1805.06855, arXiv.org, revised Sep 2019.
    4. Chen, Songnian & Wang, Qian, 2023. "Quantile regression with censoring and sample selection," Journal of Econometrics, Elsevier, vol. 234(1), pages 205-226.
    5. 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.
    6. 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.
    7. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    8. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    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. Victor Gay, 2017. "The Legacy of the Missing Men: The Long-Run Impact of World War I on Female Labor Force Participation," 2017 Papers pga905, Job Market Papers.
    11. Jun, Sung Jae, 2009. "Local structural quantile effects in a model with a nonseparable control variable," Journal of Econometrics, Elsevier, vol. 151(1), pages 82-97, July.
    12. Machado, José A.F. & Santos Silva, J.M.C. & Wei, Kehai, 2016. "Quantiles, corners, and the extensive margin of trade," European Economic Review, Elsevier, vol. 89(C), pages 73-84.
    13. Huber, Martin & Melly, Blaise, 2011. "Quantile Regression in the Presence of Sample Selection," Economics Working Paper Series 1109, University of St. Gallen, School of Economics and Political Science.
    14. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    15. 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.
    16. Su, Liangjun & Hoshino, Tadao, 2016. "Sieve instrumental variable quantile regression estimation of functional coefficient models," Journal of Econometrics, Elsevier, vol. 191(1), pages 231-254.
    17. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    18. David Powell, 2022. "Quantile regression with nonadditive fixed effects," Empirical Economics, Springer, vol. 63(5), pages 2675-2691, November.
    19. Gonzalez, Felipe & Prem, Mounu & von Dessauer, Cristine, 2023. "Empowerment or Indoctrination? Women Centers Under Dictatorship," SocArXiv 64mf9, Center for Open Science.
    20. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.

    More about this item

    Keywords

    quantile regression; endogeneity; clustered data; instrumental variables;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    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:hig:wpaper:255/ec/2022. 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: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.html .

    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.