Сравнение на методи

	Байесов панелен ефектен анализ	Динамичен метод „разлика в разликите“
Област	Причинно-следствено заключение	Причинно-следствено заключение
Семейство	Regression model	Regression model
Година на възникване	2010s–2020s	2021
Създател	Developed from panel event-study literature (Sun & Abraham 2021; Freyaldenhoven et al. 2021) combined with Bayesian estimation frameworks	Callaway & Sant'Anna; Sun & Abraham
Тип	Bayesian causal panel estimator	Causal inference / quasi-experimental
Основополагащ източник	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.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Други названия	Bayesian event-study estimator, Bayesian dynamic DiD, Bayesian panel ES, Bayes event study	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Свързани	4	4
Резюме	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.	Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time.