ScholarGate
Msaidizi

Linganisha mbinu

Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.

Muundo wa kusoma kwa matukio ya Bayesian×Uchanganuzi wa Mfululizo wa Wakati Uliokatizwa (ITS)×
NyanjaUhitimisho wa KisababishiUhitimisho wa Kisababishi
FamiliaRegression modelRegression model
Mwaka wa asili1990s–2010s2002
MwanzilishiDeveloped 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)
AinaQuasi-experimental / causal inferenceQuasi-experimental segmented regression
Chanzo asiliaSorescu, 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 ↗
Majina mbadalaBayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BESITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Zinazohusiana55
MuhtasariBayesian 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.
ScholarGateSeti ya data
  1. v1
  2. 2 Vyanzo
  3. PUBLISHED
  1. v1
  2. 2 Vyanzo
  3. PUBLISHED

Nenda kwenye utafutaji Pakua slaidi

ScholarGateLinganisha mbinu: Bayesian Event Study Design · Interrupted Time Series. Imepatikana 2026-06-18 kutoka https://scholargate.app/sw/compare