Comparer des méthodes

	Regression Discontinuity Design pour Données de Panel	Régression par discontinuité floue
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1960 (original RDD); panel extension codified 2000s–2010s	2001
Auteur d'origine	Thistlethwaite & Campbell (1960); panel extension developed through Lee & Lemieux (2010) and related applied work	Hahn, Todd & van der Klaauw
Type	Causal inference / quasi-experimental	Quasi-experimental causal inference
Source fondatrice	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.	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.
Alias	Panel RD, Panel RDD, Longitudinal Regression Discontinuity, Fixed-Effects RDD	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Apparentées	5	5
Résumé	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.	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.