مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| طراحی مطالعه رویداد بیزی× | تفاوتهای بیزی در تفاوتها (Bayesian Difference-in-Differences)× | |
|---|---|---|
| حوزه | استنتاج علّی | استنتاج علّی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 1990s–2010s | 2015-2023 |
| پدیدآور≠ | Developed from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010s | Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series) |
| نوع≠ | Quasi-experimental / causal inference | Bayesian causal inference / panel regression |
| منبع بنیادین≠ | 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 ↗ | Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529. link ↗ |
| نامهای دیگر | Bayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BES | Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator |
| مرتبط | 5 | 5 |
| خلاصه≠ | 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. | Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available. |
| ScholarGateمجموعهداده ↗ |
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