Estimating effects of staggered intervention with count and binary outcomes: a simulation study

Anil Yadav, John McHale, Jason Harold and Stephen O'Neill
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Anil Yadav: Central Bank of Ireland
Jason Harold: University of Galway
Stephen O'Neill: London School of Hygiene & Tropical Medicine

No 4/RT/24, Research Technical Papers from Central Bank of Ireland

Abstract: Difference-in-Differences and Event-study methods with staggered intervention may provide biased estimates when these approaches are implemented using a two-way fixed effect (TWFE) estimator in the presence of heterogeneous effects. Recent literature proposed alternative estimators that are unbiased, however to date, attention has primarily focused on linear outcome models. This study addresses this gap by extending five of these alternative estimators to count and binary outcomes and assessing their accuracy against the TWFE estimator in Monte Carlo simulations. While unbiased for linear models, some of the estimators yield biased estimates for nonlinear outcomes. An application revisits the statistical association between citations and star coauthorship.

Keywords: Nonlinear difference-in-differences and Event-study; Staggered intervention; Count and Binary outcomes; Treatment effect heterogeneity. (search for similar items in EconPapers)
JEL-codes: C13 C18 C22 C23 C35 (search for similar items in EconPapers)
Date: 2024-07
New Economics Papers: this item is included in nep-ecm
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