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Analyse von unterbrochenen Zeitreihen (Interrupted Time Series, ITS)×Differenz-in-Differenzen (DiD)×
FachgebietKausale InferenzÖkonometrie
FamilieRegression modelRegression model
Entstehungsjahr20021994
UrheberWagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TypQuasi-experimental segmented regressionCausal inference / panel regression
Wegweisende QuelleBernal, 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
AliasnamenITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizidiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Verwandt55
ZusammenfassungInterrupted 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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ScholarGateMethoden vergleichen: Interrupted Time Series · Difference-in-Differences. Abgerufen am 2026-06-15 von https://scholargate.app/de/compare