Compara mètodes

	Estudi d'esdeveniments amb panells bayesians	Diferències en Diferències Dinàmiques
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2010s–2020s	2021
Autor original	Developed from panel event-study literature (Sun & Abraham 2021; Freyaldenhoven et al. 2021) combined with Bayesian estimation frameworks	Callaway & Sant'Anna; Sun & Abraham
Tipus	Bayesian causal panel estimator	Causal inference / quasi-experimental
Font seminal	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.
Àlies	Bayesian event-study estimator, Bayesian dynamic DiD, Bayesian panel ES, Bayes event study	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Relacionats	4	4
Resum	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.