Compară metode

	Design de Regresie Discontinuitate Multi-perioadă	Proiectarea Fuzzy Regression Discontinuity
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2010s–2020s	2001
Autorul original	Cattaneo, Idrobo & Titiunik (foundations); extended by multiple authors for repeated-period settings	Hahn, Todd & van der Klaauw
Tip	Quasi-experimental causal inference	Quasi-experimental causal inference
Sursa seminală	Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.	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.
Denumiri alternative	multi-wave RD, repeated RDD, dynamic RD, multi-cutoff RDD	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Înrudite	3	5
Rezumat	Multi-period Regression Discontinuity Design extends the classic RDD to settings where a cutoff-based treatment is applied in multiple waves, across repeated time periods, or with varying thresholds. By pooling or comparing period-specific discontinuity estimates, researchers gain statistical precision and can examine how causal effects evolve or persist over time.	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.