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

TL;DR

This paper introduces a comprehensive econometric framework for event-study analysis under staggered adoption, providing exact limits, diagnostics, and robust inference methods to improve accuracy and reliability in policy and finance research.

Contribution

It offers a novel design-first approach with diagnostics and orthogonalization techniques, addressing biases and contamination in staggered adoption event studies.

Findings

Provides exact probability limits for two-way fixed effects regressions.

Develops computable diagnostics for contamination and negative weights.

Demonstrates robustness of inference under violations of parallel trends.

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.