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Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Bayesovský návrh štúdie udalostí× | Analýza prerušovaných časových radov (ITS)× | |
|---|---|---|
| Odbor | Kauzálna inferencia | Kauzálna inferencia |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1990s–2010s | 2002 |
| Tvorca≠ | Developed from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010s | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Typ≠ | Quasi-experimental / causal inference | Quasi-experimental segmented regression |
| Pôvodný zdroj≠ | Sorescu, 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 ↗ |
| Ďalšie názvy≠ | Bayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BES | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Príbuzné | 5 | 5 |
| Zhrnutie≠ | Bayesian 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. |
| ScholarGateDátová sada ↗ |
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