Compara mètodes

	Disseny Bayesiana d'Estudis d'Esdeveniments	Diferències en Diferències Bayesianes
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	1990s–2010s	2015-2023
Autor original	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)
Tipus	Quasi-experimental / causal inference	Bayesian causal inference / panel regression
Font seminal	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.	Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529.
Àlies	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
Relacionats	5	5
Resum	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.