Methoden vergleichen

	Dynamisches Fuzzy Regression Discontinuity Design	Paneldaten-Regressions-Diskontinuitäts-Design
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2001-2010	1960 (original RDD); panel extension codified 2000s–2010s
Urheber	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
Typ	Quasi-experimental causal inference	Causal inference / quasi-experimental
Wegweisende Quelle	Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.
Aliasnamen	Dynamic Fuzzy RDD, DFRD, Time-varying Fuzzy RD, Dynamic Fuzzy RD Design	Panel RD, Panel RDD, Longitudinal Regression Discontinuity, Fixed-Effects RDD
Verwandt	4	5
Zusammenfassung	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.