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| Robust Interrupted Time Series Analysis× | Diferenču starpībām (Diff-in-Diff)× | |
|---|---|---|
| Nozare≠ | Cēloņsakarību secināšana | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2010s | 1994 |
| Autors≠ | Bernal, Cummins & Gasparrini; Linden (robust extensions) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Tips≠ | Quasi-experimental segmented regression with robust inference | Causal inference / panel regression |
| Pirmavots≠ | Bernal, J. L., 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 ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Citi nosaukumi≠ | robust ITS, outlier-robust ITS, robust segmented regression, robust ITSA | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedasticity-consistent standard errors. It is widely used in health services research and public-health evaluation when the time series contains influential observations, non-constant variance, or mild autocorrelation. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
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