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Динамичен дизайн на регресионно прекъсване с размити граници×Размита регресионна прекъсната конструкция×
ОбластПричинно-следствено заключениеПричинно-следствено заключение
СемействоRegression modelRegression model
Година на възникване2001-20102001
СъздателCellini, Ferreira & Rothstein (dynamic RDD, 2010); Hahn, Todd & Van der Klaauw (fuzzy RDD foundations, 2001)Hahn, Todd & van der Klaauw
ТипQuasi-experimental causal inferenceQuasi-experimental causal inference
Основополагащ източникImbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635. DOI ↗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. DOI ↗
Други названияDynamic Fuzzy RDD, DFRD, Time-varying Fuzzy RD, Dynamic Fuzzy RD DesignFuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Свързани45
РезюмеDynamic 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.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.
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  2. 2 Източници
  3. PUBLISHED

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ScholarGateСравнение на методи: Dynamic Fuzzy Regression Discontinuity · Fuzzy Regression Discontinuity. Извлечено на 2026-06-19 от https://scholargate.app/bg/compare