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

Full text not available in this repository.Request a copy from the Strathclyde author

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.

ORCID iDs

Fé, Eduardo ORCID logoORCID: https://orcid.org/0000-0001-7693-9143;
  • Item type:Article
    ID code:53601
    Dates:
    Date
    Event
    2 November 2014
    Published
    25 April 2014
    Published Online
    29 March 2014
    Accepted
    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:19 Feb 2021 03:57
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/53601
CORE (COnnecting REpositories)