Порівняння методів

	Просторовий дизайн дослідження подій	Панельне дослідження подій
Галузь	Причинно-наслідковий висновок	Причинно-наслідковий висновок
Родина	Regression model	Regression model
Рік появи	2000s–2010s	1990s–2020s (modern panel formulation)
Автор методу	Developed across applied spatial economics literature; canonical applications in Autor, Dorn & Hanson (2013) and related regional economics studies	Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation
Тип	Quasi-experimental causal inference with spatial structure	Quasi-experimental / causal panel design
Основоположне джерело	Autor, D. H., Dorn, D., & Hanson, G. H. (2013). The China Syndrome: Local Labor Market Effects of Import Competition in the United States. American Economic Review, 103(6), 2121-2168.	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.
Інші назви	spatial event study, geographic event study, spatial dynamic DiD, place-based event study	event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model
Пов'язані	5	4
Підсумок	Spatial event study design estimates the dynamic causal effects of a geographically concentrated shock or policy by plotting how outcomes in affected locations evolve relative to unaffected locations across time periods, while explicitly accounting for spatial spillovers and autocorrelation across geographic units. It is widely used in regional and urban economics to evaluate place-based policies, trade shocks, and local labour market interventions.	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.