Salīdzināt metodes

	Pasākumu pētījums ar paneļiem izglītības pētniecībā	Paneļa notikumu pētījums
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1993 (general method); 2000s (education applications)	1990s–2020s (modern panel formulation)
Autors	Jacobson, LaLonde & Sullivan (1993); widely adopted in education economics from 2000s onward	Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation
Tips	Causal inference / panel regression	Quasi-experimental / causal panel design
Pirmavots	Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings Losses of Displaced Workers. American Economic Review, 83(4), 685-709.	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.
Citi nosaukumi	education event study, panel event-study design, education policy event study, school event study	event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model
Saistītās	4	4
Kopsavilkums	The panel event study is a causal-inference design that tracks outcomes for a panel of educational units — students, teachers, schools, or districts — across relative time periods around a well-defined event such as a policy change, school reform, or staffing transition. By estimating period-by-period treatment effects, it reveals not only whether an intervention mattered but also when effects appeared and how long they persisted, making it especially valued in education economics.	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.