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Diseño de Regresión Discontinua (RDD)×Variables Instrumentales mediante Mínimos Cuadrados en Dos Etapas (IV/2SLS)×
CampoInferencia causalInferencia causal
FamiliaRegression modelRegression model
Año de origen20082009
Autor originalImbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
TipoQuasi-experimental causal designInstrumental-variables regression
Fuente seminalImbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
AliasRDD, regression discontinuity design, sharp RDD, fuzzy RDDinstrumental variables, IV estimation, 2SLS, instrumental variable regression
Relacionados55
ResumenRegression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009).
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ScholarGateComparar métodos: Regression Discontinuity · Two-Stage Least Squares (2SLS). Recuperado el 2026-06-18 de https://scholargate.app/es/compare