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Bayesianisches Ereignisstudiendesign×Analyse von unterbrochenen Zeitreihen (Interrupted Time Series, ITS)×
FachgebietKausale InferenzKausale Inferenz
FamilieRegression modelRegression model
Entstehungsjahr1990s–2010s2002
UrheberDeveloped from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010sWagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
TypQuasi-experimental / causal inferenceQuasi-experimental segmented regression
Wegweisende QuelleSorescu, A., Warren, N. L., & Ertekin, L. (2017). Event study methodology in the marketing literature: An overview. Journal of the Academy of Marketing Science, 45(2), 186-207. DOI ↗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 ↗
AliasnamenBayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BESITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Verwandt55
ZusammenfassungBayesian Event Study Design extends the classical event study framework by replacing frequentist significance testing with a full Bayesian inferential framework. It estimates how an event (policy change, announcement, shock) alters an outcome trajectory by learning a prior model from the estimation window and updating it with observed data, yielding posterior distributions over abnormal effects and cumulative causal impacts with full uncertainty quantification.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.
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ScholarGateMethoden vergleichen: Bayesian Event Study Design · Interrupted Time Series. Abgerufen am 2026-06-17 von https://scholargate.app/de/compare