IDEAS home Printed from https://ideas.repec.org/p/bos/wpaper/wp2015-009.html
   My bibliography  Save this paper

Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs

Author

Listed:
  • Zhongjun Qu

    () (Boston University)

  • Jungmo Yoon

    () (Hanyang University)

Abstract

This paper builds upon conditional quantile processes to develop methods for conducting uni- form inference on quantile treatment effects under sharp regression discontinuity (RD) designs. It begins by developing Score and Wald type tests for a range of hypotheses that are related to treatment significance, homogeneity and unambiguity. It gives conditions under which the asymptotic distributions of these tests are unaffected by the biases from the nonparametric es- timation without requiring under-smoothing. Further, for situations where the conditions can be stringent, the paper develops a procedure that explicitly accounts for the effects of the bi- ases while paying special attention to their estimation uncertainty. The paper also provides a procedure for constructing uniform confidence bands for the quantile treatment effects. As an empirical application, we apply the methods to study the e§ects of cash-on-hand on unemploy- ment durations. The results reveal pronounced treatment heterogeneity and also point to the importance of considering the long-term unemployed.

Suggested Citation

  • Zhongjun Qu & Jungmo Yoon, 2015. "Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs," Boston University - Department of Economics - Working Papers Series wp2015-009, Boston University - Department of Economics.
  • Handle: RePEc:bos:wpaper:wp2015-009
    as

    Download full text from publisher

    File URL: http://people.bu.edu/qu/RD/RD.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    2. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    3. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    4. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    5. David Card & Raj Chetty & Andrea Weber, 2007. "Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market," The Quarterly Journal of Economics, Oxford University Press, vol. 122(4), pages 1511-1560.
    6. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 487-535.
    7. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
    8. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    9. Lee, David S. & Card, David, 2008. "Regression discontinuity inference with specification error," Journal of Econometrics, Elsevier, vol. 142(2), pages 655-674, February.
    10. Richard B. Freeman, 1980. "Unionism and the Dispersion of Wages," ILR Review, Cornell University, ILR School, vol. 34(1), pages 3-23, October.
    11. Jens Ludwig & Douglas L. Miller, 2007. "Does Head Start Improve Children's Life Chances? Evidence from a Regression Discontinuity Design," The Quarterly Journal of Economics, Oxford University Press, vol. 122(1), pages 159-208.
    12. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    13. Guido Imbens & Karthik Kalyanaraman, 2012. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 933-959.
    14. Dong, Yingying, 2010. "Jumpy or Kinky? Regression Discontinuity without the Discontinuity," MPRA Paper 25461, University Library of Munich, Germany.
    15. David S. Lee & Enrico Moretti & Matthew J. Butler, 2004. "Do Voters Affect or Elect Policies? Evidence from the U. S. House," The Quarterly Journal of Economics, Oxford University Press, vol. 119(3), pages 807-859.
    16. Robert J. LaLonde, 1995. "The Promise of Public Sector-Sponsored Training Programs," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 149-168, Spring.
    17. Qu, Zhongjun & Yoon, Jungmo, 2015. "Nonparametric estimation and inference on conditional quantile processes," Journal of Econometrics, Elsevier, vol. 185(1), pages 1-19.
    18. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
    19. John DiNardo & David S. Lee, 2004. "Economic Impacts of New Unionization on Private Sector Employers: 1984–2001," The Quarterly Journal of Economics, Oxford University Press, vol. 119(4), pages 1383-1441.
    20. repec:spo:wpecon:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    heterogeneity; quantile regression; regression discontinuity; treatment effect; unemployment duration;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    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:bos:wpaper:wp2015-009. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Program Coordinator). General contact details of provider: http://edirc.repec.org/data/decbuus.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.