مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| مطالعه رویداد پانلی بیزی× | تفاوتهای بیزی در تفاوتها (Bayesian Difference-in-Differences)× | |
|---|---|---|
| حوزه | استنتاج علّی | استنتاج علّی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 2010s–2020s | 2015-2023 |
| پدیدآور≠ | Developed from panel event-study literature (Sun & Abraham 2021; Freyaldenhoven et al. 2021) combined with Bayesian estimation frameworks | Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series) |
| نوع≠ | Bayesian causal panel estimator | Bayesian causal inference / panel regression |
| منبع بنیادین≠ | Freyaldenhoven, S., Hansen, C., Shapiro, J. M., & Teso, E. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper No. 29170. National Bureau of Economic Research. link ↗ | Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529. link ↗ |
| نامهای دیگر | Bayesian event-study estimator, Bayesian dynamic DiD, Bayesian panel ES, Bayes event study | Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator |
| مرتبط≠ | 4 | 5 |
| خلاصه≠ | Bayesian Panel Event Study is a causal inference design that estimates dynamic treatment effects around a datable event using panel data, replacing classical frequentist estimation with Bayesian posterior inference. It produces period-by-period effect estimates with full probability distributions, enabling principled uncertainty quantification, regularization of noisy pre-trend coefficients, and probabilistic tests of parallel trends. | 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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