Salīdzināt metodes

	Dinamiskā notikumu pētījumu dizains	Diferenču starpībām (Diff-in-Diff)
Nozare	Cēloņsakarību secināšana	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2021 (canonical treatment); practice since 1990s)	1994
Autors	Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tips	Quasi-experimental / causal inference	Causal inference / panel regression
Pirmavots	Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Citi nosaukumi	dynamic DiD, lead-lag event study, relative-time event study, event-time regression	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Saistītās	3	5
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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.