Comparer des méthodes

	Séries chronologiques interrompues multi-périodes	Étude d'événement sur données de panel
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000s-2015	1990s–2020s (modern panel formulation)
Auteur d'origine	Extended from segmented regression / ITS tradition; multi-break formalization developed across epidemiology and health policy literature (2000s-2010s)	Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation
Type	Quasi-experimental time series regression	Quasi-experimental / causal panel design
Source fondatrice	Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350, h2750.	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.
Alias	multi-period ITS, multiple-interruption ITS, segmented time series with multiple breakpoints, MITS	event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model
Apparentées	5	4
Résumé	Multi-period Interrupted Time Series (MITS) extends the classic ITS framework to settings where two or more interventions occur at known time points within the same series. By fitting a segmented regression with multiple breakpoints, MITS estimates the level change and slope change attributable to each intervention while controlling for the underlying secular trend and for the effects of earlier interruptions.	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.