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| 시계열 단절 분석 (Interrupted Time Series, ITS)× | 이중차분법 (Diff-in-Diff)× | |
|---|---|---|
| 분야≠ | 인과추론 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2002 | 1994 |
| 창시자≠ | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| 유형≠ | Quasi-experimental segmented regression | Causal inference / panel regression |
| 원전≠ | 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 |
| 별칭 | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| 관련 | 5 | 5 |
| 요약≠ | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. | 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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