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| 동적 퍼지 회귀 불연속 설계× | 퍼지 회귀 불연속 설계× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2001-2010 | 2001 |
| 창시자≠ | Cellini, Ferreira & Rothstein (dynamic RDD, 2010); Hahn, Todd & Van der Klaauw (fuzzy RDD foundations, 2001) | Hahn, Todd & van der Klaauw |
| 유형 | Quasi-experimental causal inference | Quasi-experimental causal inference |
| 원전≠ | Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ | Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209. DOI ↗ |
| 별칭 | Dynamic Fuzzy RDD, DFRD, Time-varying Fuzzy RD, Dynamic Fuzzy RD Design | Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD |
| 관련≠ | 4 | 5 |
| 요약≠ | Dynamic Fuzzy Regression Discontinuity Design extends the standard fuzzy RDD to a panel or multi-period setting, allowing researchers to estimate how the causal effect of a probabilistic threshold-based treatment evolves over time. By combining an IV-based fuzzy first stage with time-indexed outcomes, it traces treatment effects across multiple post-treatment periods, not just at a single cross-sectional snapshot. | Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold. |
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