Salīdzināt metodes

	Dinamiskā notikumu pētījumu dizains	Dinamiskā "starpību starpībās" metode
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2021 (canonical treatment); practice since 1990s)	2021
Autors	Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics	Callaway & Sant'Anna; Sun & Abraham
Tips	Quasi-experimental / causal inference	Causal inference / quasi-experimental
Pirmavots	Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Citi nosaukumi	dynamic DiD, lead-lag event study, relative-time event study, event-time regression	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Saistītās	3	4
Kopsavilkums	The dynamic event study design extends the standard difference-in-differences framework by estimating treatment effects at each period before and after the event, rather than collapsing everything into a single post-treatment coefficient. By plotting lead and lag coefficients against relative event time, researchers can simultaneously test for pre-existing trends and trace how the causal effect evolves over multiple post-treatment periods.	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.