IDEAS home Printed from https://ideas.repec.org/p/nbr/nberte/0322.html
   My bibliography  Save this paper

Regression Discontinuity Inference with Specification Error

Author

Listed:
  • David S. Lee
  • David Card

Abstract

A regression discontinuity (RD) research design is appropriate for program evaluation problems in which treatment status (or the probability of treatment) depends on whether an observed covariate exceeds a fixed threshold. In many applications the treatment-determining covariate is discrete. This makes it impossible to compare outcomes for observations "just above" and "just below" the treatment threshold, and requires the researcher to choose a functional form for the relationship between the treatment variable and the outcomes of interest. We propose a simple econometric procedure to account for uncertainty in the choice of functional form for RD designs with discrete support. In particular, we model deviations of the true regression function from a given approximating function -- the specification errors -- as random. Conventional standard errors ignore the group structure induced by specification errors and tend to overstate the precision of the estimated program impacts. The proposed inference procedure that allows for specification error also has a natural interpretation within a Bayesian framework.

Suggested Citation

  • David S. Lee & David Card, 2006. "Regression Discontinuity Inference with Specification Error," NBER Technical Working Papers 0322, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberte:0322
    Note: TWP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/t0322.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lara Shore-Sheppard, 1996. "The Precision of Instrumental Variables Estimates With Grouped Data," Working Papers 753, Princeton University, Department of Economics, Industrial Relations Section..
    2. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366 Elsevier.
    3. David Card & Lara D. Shore-Sheppard, 2004. "Using Discontinuous Eligibility Rules to Identify the Effects of the Federal Medicaid Expansions on Low-Income Children," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 752-766, August.
    4. White, Halbert, 1980. "Using Least Squares to Approximate Unknown Regression Functions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(1), pages 149-170, February.
    5. repec:fth:prinin:374 is not listed on IDEAS
    6. 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.
    7. Thomas J. Kane, 2003. "A Quasi-Experimental Estimate of the Impact of Financial Aid on College-Going," NBER Working Papers 9703, National Bureau of Economic Research, Inc.
    8. Lara D. Shore-Sheppard, 1996. "The Precision of Instrumental Variables Estimates With Grouped Data," Working Papers 753, Princeton University, Department of Economics, Industrial Relations Section..
    9. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
    10. Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-338, May.
    Full references (including those not matched with items on IDEAS)

    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • J0 - Labor and Demographic Economics - - General

    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:nbr:nberte:0322. 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: (). General contact details of provider: http://edirc.repec.org/data/nberrus.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.