Salīdzināt metodes

	Telpiskās notikumu izpētes dizains	Dinamiskā "starpību starpībās" metode
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2000s–2010s	2021
Autors	Developed across applied spatial economics literature; canonical applications in Autor, Dorn & Hanson (2013) and related regional economics studies	Callaway & Sant'Anna; Sun & Abraham
Tips	Quasi-experimental causal inference with spatial structure	Causal inference / quasi-experimental
Pirmavots	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.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Citi nosaukumi	spatial event study, geographic event study, spatial dynamic DiD, place-based event study	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Saistītās	5	4
Kopsavilkums	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.	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.