방법 비교

	다기간 사건 연구 설계	동적 이중차분법 (Dynamic Difference-in-Differences)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	1993	2021
창시자	Jacobson, LaLonde & Sullivan (1993); seminal methodological treatment by Sun & Abraham (2021)	Callaway & Sant'Anna; Sun & Abraham
유형	Quasi-experimental causal inference	Causal inference / quasi-experimental
원전	Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings losses of displaced workers. American Economic Review, 83(4), 888-909.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
별칭	multi-period event study, dynamic event study, relative-time event study, leads-and-lags design	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
관련	3	4
요약	The multi-period event study design estimates causal treatment effects at each point in time relative to the treatment onset, using panel data with multiple pre- and post-treatment periods. By plotting the full path of treatment coefficients rather than a single average, it reveals how effects build up, fade, or remain stable over time — and allows formal tests of pre-treatment parallel trends across many periods simultaneously.	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.