Regression discontinuity design with covariates
Series
VWA Discussion Papers
Type
discussion paper
Date Issued
2007-09-04
Author(s)
Froelich, Markus
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 constructed 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.
Language
English
Keywords
Treatment effect
causal effect
complier
LATE
nonparametric regression
endogeneity
HSG Classification
contribution to scientific community
Refereed
No
Publisher place
St.Gallen
Number
2007-32
Start page
27
Subject(s)
Division(s)
Eprints ID
39601
File(s)![Thumbnail Image]()
Name
DP-32-FrölichGanz.pdf
Size
239.88 KB
Format
Adobe PDF
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