Regression Discontinuity Design with Covariates
Universität Mannheim, Chair of Econometrics; Institute for the Study of Labor (IZA); University of St. Gallen - Swiss Institute for International Economics and Applied Economic Research
University of St.Gallen, Department of Economics, Discussion Paper No. 2007-32
IZA Discussion Paper No. 3024
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.
Number of Pages in PDF File: 25
Keywords: Treatment effect, causal effect, complier, LATE, nonparametric regression, endogeneity
JEL Classification: C13, C14, C21working papers series
Date posted: September 6, 2007
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