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Regression discontinuity inference with specification error

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Author Info

  • Lee, David S.
  • Card, David

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.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 142 (2008)
Issue (Month): 2 (February)
Pages: 655-674

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Handle: RePEc:eee:econom:v:142:y:2008:i:2:p:655-674

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Web page: http://www.elsevier.com/locate/jeconom

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  1. 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.
  2. David Card & Lara D. Shore-Sheppard, 2002. "Using Discontinuous Eligibility Rules to Identify the Effects of the Federal Medicaid Expansions on Low Income Children," NBER Working Papers 9058, National Bureau of Economic Research, Inc.
  3. 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 334-38, May.
  4. repec:fth:prinin:374 is not listed on IDEAS
  5. 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 149-70, February.
  6. 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.
  7. Lara Shore-Sheppard, 1996. "The Precision of Instrumental Variables Estimates With Grouped Data," Working Papers 753, Princeton University, Department of Economics, Industrial Relations Section..
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