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Paneldata-avbruten tidsserieanalys×Panel Data Difference-in-Differences (Panel DiD / TWFE)×
ÄmnesområdeKausal inferensKausal inferens
FamiljRegression modelRegression model
Ursprungsår2000s–2010s1985–2004
UpphovspersonShadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)Ashenfelter & Card (1985); codified by Angrist & Pischke (2009); serial correlation critique by Bertrand, Duflo & Mullainathan (2004)
TypQuasi-experimental causal inferenceCausal inference / panel regression
UrsprungskällaLopez 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 ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Aliaspanel ITS, multi-unit ITS, panel ITSA, controlled interrupted time seriesTwo-Way Fixed Effects DiD, TWFE, Panel DiD, Panel Diff-in-Diff
Närliggande54
SammanfattningPanel 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.Panel Data Difference-in-Differences extends the classic two-period DiD design to settings with multiple units observed across many time periods. By absorbing unit-level fixed effects and time fixed effects simultaneously, it isolates the causal effect of a treatment or policy change while controlling for both time-invariant unit heterogeneity and common time shocks affecting all units.
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ScholarGateJämför metoder: Panel Data Interrupted Time Series · Panel Data Difference-in-Differences. Hämtad 2026-06-15 från https://scholargate.app/sv/compare