Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Designul de Discontinuitate prin Regresie Fuzzy pentru Date Panel× | Designul discontinuității regresiei pentru date de panel× | |
|---|---|---|
| Domeniu | Inferență cauzală | Inferență cauzală |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2001 (fuzzy RDD); panel extension circa 2011 | 1960 (original RDD); panel extension codified 2000s–2010s |
| Autorul original≠ | Hahn, Todd & Van der Klaauw; extended to panel settings by Papay, Willett & Murnane and others | Thistlethwaite & Campbell (1960); panel extension developed through Lee & Lemieux (2010) and related applied work |
| Tip≠ | Quasi-experimental causal inference | Causal inference / quasi-experimental |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | Panel Fuzzy RDD, Panel FRD, Fuzzy RD with Panel Data, Panel Fuzzy RD | Panel RD, Panel RDD, Longitudinal Regression Discontinuity, Fixed-Effects RDD |
| Înrudite | 5 | 5 |
| Rezumat≠ | Panel Data Fuzzy Regression Discontinuity Design (Panel FRD) extends the fuzzy RDD framework to settings where multiple observations per unit are available over time. It exploits a probabilistic — rather than deterministic — threshold-crossing rule to identify a local average treatment effect (LATE) while controlling for unit-level and time-level fixed effects, sharpening identification in repeated-measures contexts. | 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. |
| ScholarGateSet de date ↗ |
|
|