In: Journal of Business and Economic Statistics, 2019, vol. 37, no. 4, p. 736-748
This article proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one- dimensional nonparametric regression, irrespective of the...
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In: Journal of Applied Econometrics, 2020, p. 481-504
This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non-response bias, based on two instrumental variables. Using a discrete instrument for the treatment and an instrument with rich (in general continuous) support for non-response/attrition, we identify the average treatment effect on compliers as well as the...
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In: AStA Advances in Statistical Analysis, 2007, vol. 91, no. 3, p. 279-290
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In: Statistics and Computing, 2005, vol. 15, no. 3, p. 197-215
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(Working Papers SES ; 489)
This paper proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one-dimensional nonparametric regression, irrespective of the...
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(Working Papers SES ; 459)
This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non-response bias, using two instrumental variables. Making use of a discrete instrument for the treatment and a continuous instrument for nonresponse/attrition, we identify the average treatment effect on compliers as well as the total population and suggest...
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(Working Papers SES ; 454)
This paper investigates the fi nite sample performance of a comprehensive set of semi- and nonparametric estimators for treatment and policy evaluation. In contrast to previous simulation studies which mostly considered semiparametric approaches relying on parametric propensity score estimation, we also consider more fl exible approaches based on semi- or nonparametric propensity scores,...
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