Regression discontinuity design with covariates
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.
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- Rafael LALIVE, 2006.
"How do Extended Benefits affect Unemployment Duration? A Regression Discontinuity Approach,"
Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP)
06.06, Université de Lausanne, Faculté des HEC, DEEP.
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- Erich Battistin & Enrico Rettore, 2002. "Testing for programme effects in a regression discontinuity design with imperfect compliance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 39-57.
- Sandra E. Black, 1999.
"Do Better Schools Matter? Parental Valuation Of Elementary Education,"
The Quarterly Journal of Economics,
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- Patrick Puhani & Andrea Weber, 2007. "Does the early bird catch the worm?," Empirical Economics, Springer, vol. 32(2), pages 359-386, May.
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