Efficient estimation in regression discontinuity designs via asymmetric kernels
AbstractEstimation of causal eects in regression discontinuity designs relies on a local Wald estimator whose components are estimated via local linear regressions centred at an specic point in the range of a treatment assignment variable. The asymptotic distribution of the estimator depends on the specic choice of kernel used in these nonparametric regressions, with some popular kernels causing a notable loss of effciency. This article presents the asymptotic distribution of the local Wald estimator when a gamma kernel is used in each local linear regression. The resulting statistics is easy to implement, consistent at the usual nonparametric rate, maintains its asymptotic normal distribution, but its bias and variance do not depend on kernel-related constants and, as a result, is becomes a more effcient method. The effciency gains are measured via a limited Monte Carlo experiment, and the new method is used in a substantive application.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 38164.
Date of creation: 24 Feb 2012
Date of revision:
Regression Discontinuity; Asymmetric Kernels; Local Linear Regression;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
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