Comparar métodos

	Diseño de Discontinuidad por Regresión con Datos de Panel	Diseño difuso de discontinuidad de regresión
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	1960 (original RDD); panel extension codified 2000s–2010s	2001
Autor original	Thistlethwaite & Campbell (1960); panel extension developed through Lee & Lemieux (2010) and related applied work	Hahn, Todd & van der Klaauw
Tipo	Causal inference / quasi-experimental	Quasi-experimental causal inference
Fuente seminal	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
Relacionados	5	5
Resumen	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.