Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Регресійний розрив із дискретними даними панелі (Panel RDD)× | Перервний часовий ряд для панельних даних× | |
|---|---|---|
| Галузь | Причинно-наслідковий висновок | Причинно-наслідковий висновок |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1960 (original RDD); panel extension codified 2000s–2010s | 2000s–2010s |
| Автор методу≠ | Thistlethwaite & Campbell (1960); panel extension developed through Lee & Lemieux (2010) and related applied work | Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial) |
| Тип≠ | Causal inference / quasi-experimental | Quasi-experimental causal inference |
| Основоположне джерело≠ | Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355. DOI ↗ | Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ |
| Інші назви | Panel RD, Panel RDD, Longitudinal Regression Discontinuity, Fixed-Effects RDD | panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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. | Panel Data Interrupted Time Series (panel ITS) is a quasi-experimental method that estimates the causal effect of an intervention using repeated observations from multiple units over time. By exploiting variation across both units and time periods, it provides stronger causal identification than single-unit ITS, detecting changes in the level and slope of the outcome trajectory immediately following a clearly dated intervention. |
| ScholarGateНабір даних ↗ |
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