arXiv 9 Mar 2024 · Econometrics
arXiv:2403.05803 · PDF · DOI · OpenAlex · Extracted main text
Treatment effects in regression discontinuity designs (RDDs) are often estimated using local regression methods. \cite{Hahn:01} demonstrated that the identification of the average treatment effect at the cutoff in RDDs relies on the unconfoundedness assumption and that, without this assumption, only the local average treatment effect at the cutoff can be identified. In this paper, we propose a semiparametric framework tailored for identifying the average treatment effect in RDDs, eliminating the need for the unconfoundedness assumption. Our approach globally conceptualizes the identification as a partially linear modeling problem, with the coefficient of a specified polynomial function of propensity score in the linear component capturing the average treatment effect. This identification result underpins our semiparametric inference for RDDs, employing the $P$-spline method to approximate the nonparametric function and establishing a procedure for conducting inference within this framework. Through theoretical analysis, we demonstrate that our global approach achieves a faster convergence rate compared to the local method. Monte Carlo simulations further confirm that the proposed method consistently outperforms alternatives across various scenarios. Furthermore, applications to real-world datasets illustrate that our global approach can provide more reliable inference for practical problems.
appendix boundary found by appendix_command · 78% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hahn, J., P. Todd, and W. Van der Klaauw (2001) Identification and estimation of treatment effects with a regression-discontinuity design | 1.000 | 6 | 4 | 100% |
| 2 | Lee, D (2008) Randomized experiments from non-random selection in U.S. House elections | 1.000 | 5 | 3 | 100% |
| 3 | Calonico, S., M. D. Cattaneo, and R. Titiunik (2014) Robust nonparametric confidence intervals for regression discontinuity designs | 0.928 | 4 | 3 | 100% |
| 4 | Calonico, S., N. Jawadekar, K. Kezios, and A. Z. Al Hazzouri (2024) Regression discontinuity design studies: a guide for health researchers | 0.874 | 5 | 2 | 100% |
| 5 | Cattaneo, M., B. Frandsen, and R. Titiunik (2015) Randomization inference in the regression discontinuity design: An application to party advantages in the U.S. Senate | 0.737 | 3 | 2 | 100% |
| 6 | Imbens, G. and K. Kalyanaraman (2012) Optimal bandwidth choice for the regression discontinuity estimator | 0.737 | 3 | 2 | 100% |
| 7 | Ruppert, D (2002) Selecting the number of knots for penalized splines | 0.737 | 3 | 2 | 100% |
| 8 | Ruppert, D., M. P. Wand, and R. J. Carroll (2003) Semiparametric Regression | 0.737 | 3 | 2 | 100% |
| 9 | Fan, J. and I. Gijbels (1996) Local Polynomial Modelling and Its Applications | 0.644 | 2 | 2 | 100% |
| 10 | Gelman, A. and G. W. Imbens (2019) Why high-order polynomials should not be used in regression discontinuity designs | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 33 scored citations.