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| 동적 퍼지 회귀 불연속 설계× | 회귀 불연속 설계의 패널 데이터 (Panel RDD)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2001-2010 | 1960 (original RDD); panel extension codified 2000s–2010s |
| 창시자≠ | Cellini, Ferreira & Rothstein (dynamic RDD, 2010); Hahn, Todd & Van der Klaauw (fuzzy RDD foundations, 2001) | Thistlethwaite & Campbell (1960); panel extension developed through Lee & Lemieux (2010) and related applied work |
| 유형≠ | Quasi-experimental causal inference | Causal inference / quasi-experimental |
| 원전≠ | Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ | Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355. DOI ↗ |
| 별칭 | Dynamic Fuzzy RDD, DFRD, Time-varying Fuzzy RD, Dynamic Fuzzy RD Design | Panel RD, Panel RDD, Longitudinal Regression Discontinuity, Fixed-Effects 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. | Panel data regression discontinuity design (Panel RDD) combines the sharp local identification of a regression discontinuity with the within-unit variation available in repeated-observation panel data. Units are observed across multiple periods, and treatment is assigned based on whether a running variable crosses a known threshold. By leveraging both the discontinuity and panel structure, researchers can control for unobserved unit-level heterogeneity while estimating a causal treatment effect near the threshold. |
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