Year: 2016
Type: article
Abstract: Regression Discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions. Nonetheless, most popular procedures assume i.i.d. data, which is unreasonable in many common applications. To relax this assumption, we derive the properties of traditional estimators in a setting that incorporates clustering at the level of the running variable, and propose an accompanying optimal-MSE bandwidth selection rule. Simulation results demonstrate that falsely assuming data are i.i.d. may lead to higher MSE due to inadequate bandwidth choice. We apply our procedure to analyze the impact of Low-Income Housing Tax Credits on neighborhood characteristics and low-income housing supply.
Authors: Otávio Bartalotti, Quentin Brummet
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2347026566