Title: Minimum Contrast Empirical Likelihood Manipulation Testing for Regression Discontinuity Design

Speaker: Lecturer Jun Ma, Renmin University of China

Time: May 19, 2017 13:30–14:45

Venue: Conference Room 106B, Zhonghui Building (College of Economics, JNU)

Abstract:

This paper investigates the asymptotic properties of a simple empirical-likelihood-based inference method for discontinuity in density. In a regression discontinuity design (RDD), the continuity of the density of the assignment variable at the threshold is considered as a “no-manipulation” behavioral assumption, which is a testable implication of an identifying condition for the local treatment effect (LATE). Our approach is based on the first-order conditions ob- tained from a minimum contrast (MC) problem and complements Otsu et al. (2013)’s method. Our inference procedure has three main advantages. Firstly, it requires only one tuning parameter; secondly, it does not require concentrating out any nuisance pa- rameter and therefore is very easily implementable; thirdly, its delicate second-order properties lead to a simple coverage-error-optimal bandwidth selection rule. We propose a data-driven CE-optimal bandwidth selector for use in practice. Results from Monte Carlo simulations are presented. Usefulness of our method is illustrated by empirical examples.