Comparar métodos

	Diseño de Estudio de Eventos Bayesiano	Diferencia en Diferencias (Diff-in-Diff)
Campo	Inferencia causal	Econometría
Familia	Regression model	Regression model
Año de origen	1990s–2010s	1994
Autor original	Developed from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010s	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Quasi-experimental / causal inference	Causal inference / panel regression
Fuente 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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	Bayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BES	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados	5	5
Resumen	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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.