Estimation and inference in regression discontinuity designs with asymmetric kernels

Fé, Eduardo (2014) Estimation and inference in regression discontinuity designs with asymmetric kernels. Journal of Applied Statistics, 41 (11). pp. 2406-2417. ISSN 0266-4763

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Official URL: https://doi.org/10.1080/02664763.2014.910638

Abstract

We study the behaviour of the Wald estimator of causal effects in regression discontinuity design when local linear regression (LLR) methods are combined with an asymmetric gamma kernel. We show that the resulting statistic is no more complex to implement than existing methods, remains consistent at the usual non-parametric rate, and maintains an asymptotic normal distribution but, crucially, has bias and variance that do not depend on kernel-related constants. As a result, the new estimator is more efficient and yields more reliable inference. A limited Monte Carlo experiment is used to illustrate the efficiency gains. As a by product of the main discussion, we extend previous published work by establishing the asymptotic normality of the LLR estimator with a gamma kernel. Finally, the new method is used in a substantive application.

Creators(s):	Fé, Eduardo ORCID: https://orcid.org/0000-0001-7693-9143;
Item type:	Article
ID code:	53601
Keywords:	asymmetric kernels, local linear regression, regression discontinuity, Statistics, Economic Theory, Statistics and Probability, Statistics, Probability and Uncertainty
Subjects:	Social Sciences > Statistics Social Sciences > Economic Theory
Department:	Strathclyde Business School > Economics
Depositing user:	Pure Administrator
Date deposited:	06 Jul 2015 13:34
Last modified:	25 May 2020 04:53
Related URLs:	Scopus publication
URI:	https://strathprints.strath.ac.uk/id/eprint/53601
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