Dynamic regression discontinuity under treatment effect heterogeneity

• 成果类型: Article
• 署名作者: Hsu, Yu-Chin; Shen, Shu
• 署名单位: Academia Sinica - Taiwan; National Central University; National Chengchi University; National Taiwan University; University of California System; University of California Davis
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2150
• 发表日期: 2024
• 页码: 1035-1064
• 关键词: Long-term treatment effects; dynamic regression discontinuity; semiparametric; varying coefficient logit; C31

摘要

Regression discontinuity is a popular tool for analyzing economic policies or treatment interventions. This research extends the classic static RD model to a dynamic framework, where observations are eligible for repeated RD events and, therefore, treatments. Such dynamics often complicate the identification and estimation of long-term average treatment effects. Empirical papers with such designs have so far ignored the dynamics or adopted restrictive identifying assumptions. This paper presents identification strategies under various sets of weaker identifying assumptions and proposes associated estimation and inference methods. The proposed methods are applied to revisit the seminal study of Cellini, Ferreira, and Rothstein (2010) on long-term effects of California local school bonds.