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Regressão Descontínua Fuzzy para Avaliação de Políticas×Diferenças em Diferenças (DiD)×
ÁreaInferência causalEconometria
FamíliaRegression modelRegression model
Ano de origem20011994
Autor originalHahn, Todd & Van der KlaauwCard & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TipoQuasi-experimental / local IV estimatorCausal inference / panel regression
Fonte seminalHahn, 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. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Outros nomesFuzzy RDD, Fuzzy RD, Fuzzy Regression Discontinuity, Imperfect Compliance RDDdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados55
ResumoFuzzy Regression Discontinuity Design (Fuzzy RDD) estimates the causal effect of a policy when eligibility is determined by crossing a threshold on a continuous score, but actual take-up or compliance is imperfect. Developed formally by Hahn, Todd, and Van der Klaauw (2001), it uses the threshold as an instrumental variable to recover a Local Average Treatment Effect (LATE) among compliers near the cutoff.Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.
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ScholarGateComparar métodos: Policy Evaluation Fuzzy Regression Discontinuity · Difference-in-Differences. Recuperado em 2026-06-18 de https://scholargate.app/pt/compare