Regression discontinuity design with covariates

Markus Frölich ()

No CWP27/07, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract:

In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension of X. It thus extends the analysis of Hahn, Todd and van der Klaauw (2001) and Porter (2003), who examined identification and estimation without covariates, requiring assumptions that may often be too strong in applications. In many applications, individuals to the left and right of the threshold differ in observed characteristics. Houses may be Cconstructed in different ways across school attendance district boundaries. Firms may differ around a threshold that implies certain legal changes, etc. Accounting for these differences in covariates is important to reduce bias. In addition, accounting for covariates may also reduces variance. Finally, estimation of quantile treatment effects (QTE) is also considered.

JEL-codes: C13 C14 C21 (search for similar items in EconPapers)
Date: 2007-11-22
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Citations: View citations in EconPapers (23)

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http://cemmap.ifs.org.uk/wps/cwp2707.pdf (application/pdf)

Related works:
Working Paper: Regression Discontinuity Design with Covariates (2007)
Working Paper: Regression discontinuity design with covariates (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:ifs:cemmap:27/07

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