Salīdzināt metodes

	Daudzperiodu izplūdušā regresijas pārrāvuma dizains	Reģresijas atslēguma dizains panelī (Panel Data Regression Discontinuity Design)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2001 (fuzzy RD); multi-period extension ~2010s	1960 (original RDD); panel extension codified 2000s–2010s
Autors	Hahn, Todd & Van der Klaauw (foundational fuzzy RD, 2001); extended to multi-period settings by Cattaneo, Idrobo & Titiunik and subsequent applied literature	Thistlethwaite & Campbell (1960); panel extension developed through Lee & Lemieux (2010) and related applied work
Tips	Quasi-experimental causal inference	Causal inference / quasi-experimental
Pirmavots	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.	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.
Citi nosaukumi	multi-period fuzzy RDD, fuzzy RD with repeated assignment, multi-wave fuzzy RD, staggered fuzzy RDD	Panel RD, Panel RDD, Longitudinal Regression Discontinuity, Fixed-Effects RDD
Saistītās	4	5
Kopsavilkums	Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarantee it — and when this assignment process is observed across two or more time periods or cohorts, enabling pooled or period-specific causal estimates under repeated near-threshold comparisons.	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.