Salīdzināt metodes

	Bayesian Event Study Design	Paneļa notikumu pētījums
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1990s–2010s	1990s–2020s (modern panel formulation)
Autors	Developed from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010s	Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation
Tips	Quasi-experimental / causal inference	Quasi-experimental / causal panel design
Pirmavots	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.	Freyaldenhoven, S., Hansen, C., Perez-Orive, J., & Shapiro, J. M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper 29170. National Bureau of Economic Research.
Citi nosaukumi	Bayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BES	event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model
Saistītās	5	4
Kopsavilkums	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.	A panel event study estimates the dynamic causal effect of a treatment or policy by regressing an outcome on a full set of relative-time indicators — one for each period before and after the event — while controlling for unit and time fixed effects. The resulting coefficient plot shows how the treated units diverged from untreated units at each point in calendar time relative to their treatment date, making both pre-treatment trend violations and post-treatment effect trajectories immediately visible.