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Байесов дизайн на събитийно проучване×Анализ на прекъснати времеви редове (ITS)×
ОбластПричинно-следствено заключениеПричинно-следствено заключение
СемействоRegression modelRegression model
Година на възникване1990s–2010s2002
СъздателDeveloped 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)
ТипQuasi-experimental / causal inferenceQuasi-experimental segmented regression
Основополагащ източникSorescu, 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 ↗
Други названияBayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BESITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Свързани55
РезюмеBayesian 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.
ScholarGateНабор от данни
  1. v1
  2. 2 Източници
  3. PUBLISHED
  1. v1
  2. 2 Източници
  3. PUBLISHED

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ScholarGateСравнение на методи: Bayesian Event Study Design · Interrupted Time Series. Извлечено на 2026-06-18 от https://scholargate.app/bg/compare