This paper revisits the issue of endogeneity in accounting and finance research by updating the framework initially proposed by Gippel et al . (2015), focusing on recent trends in the use of difference‐in‐differences (DiD) and natural experiments. We examine new requirements for rigorous endogeneity control, especially the structural estimation methods that extend beyond traditional ordinary least squares (OLS), two‐stage least squares (2SLS), instrumental variables (IV), and system GMM (generalized method of moments) approaches. These expanded expectations surrounding DiD assumptions and the need for structural estimation models allow for more robust causal inference and enable researchers to explore hypothetical scenarios. Additionally, we discuss the challenges of announcements with unresolved uncertainty and outline methods to separate news effects from value effects, particularly in cases involving options data. This paper serves as a practical guide for researchers to meet heightened standards in the finance literature and underlines the importance of theoretical modelling and multiple treatments in contemporary causal analysis.
Gippel et al. (Mon,) studied this question.
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