Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Mitmeperioodiline hägusa regressioonkatkestuse disain× | Paneeldata regressioonkatkestusdisain× | |
|---|---|---|
| Valdkond | Põhjuslik järeldamine | Põhjuslik järeldamine |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 2001 (fuzzy RD); multi-period extension ~2010s | 1960 (original RDD); panel extension codified 2000s–2010s |
| Looja≠ | 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 |
| Tüüp≠ | Quasi-experimental causal inference | Causal inference / quasi-experimental |
| Algallikas≠ | 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 ↗ | Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355. DOI ↗ |
| Rööpnimetused | 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 |
| Seotud≠ | 4 | 5 |
| Kokkuvõte≠ | 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. |
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