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Multi-period Event Study Design×Difference-in-Differences (DiD)×
VakgebiedCausale inferentieEconometrie
FamilieRegression modelRegression model
Jaar van ontstaan19931994
GrondleggerJacobson, LaLonde & Sullivan (1993); seminal methodological treatment by Sun & Abraham (2021)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TypeQuasi-experimental causal inferenceCausal inference / panel regression
Oorspronkelijke bronJacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings losses of displaced workers. American Economic Review, 83(4), 888-909. link ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Aliassenmulti-period event study, dynamic event study, relative-time event study, leads-and-lags designdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Verwant35
SamenvattingThe multi-period event study design estimates causal treatment effects at each point in time relative to the treatment onset, using panel data with multiple pre- and post-treatment periods. By plotting the full path of treatment coefficients rather than a single average, it reveals how effects build up, fade, or remain stable over time — and allows formal tests of pre-treatment parallel trends across many periods simultaneously.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.
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