Design-Robust Event-Study Estimation under Staggered Adoption Diagnostics, Sensitivity, and Orthogonalisation

Abstract

This paper develops a design-first econometric framework for event-study and difference-in-differences estimands under staggered adoption with heterogeneous effects, emphasising (i) exact probability limits for conventional two-way fixed effects event-study regressions, (ii) computable design diagnostics that quantify contamination and negative-weight risk, and (iii) sensitivity-robust inference that remains uniformly valid under restricted violations of parallel trends. The approach is accompanied by orthogonal score constructions that reduce bias from high-dimensional nuisance estimation when conditioning on covariates. Theoretical results and Monte Carlo experiments jointly deliver a self-contained methodology paper suitable for finance and econometrics applications where timing variation is intrinsic to policy, regulation, and market-structure changes.

71 pages, 9 figures, 9 tables. arXiv submission: full theoretical development; Monte Carlo evidence (Section 8); replicable empirical application to staggered state banking deregulation (Section 9) comparing TWFE event-studies to heterogeneity-robust estimators with diagnostics (weights, pre-trends, placebo) and calibrated sensitivity analysis over (B,Γ,Δ(\mathcal{R}))