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Inference with Dependent Data in Accounting and Finance Applications
Author(s) -
CONLEY TIMOTHY,
GONÇALVES SILVIA,
HANSEN CHRISTIAN
Publication year - 2018
Publication title -
journal of accounting research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.767
H-Index - 141
eISSN - 1475-679X
pISSN - 0021-8456
DOI - 10.1111/1475-679x.12219
Subject(s) - estimator , heteroscedasticity , inference , econometrics , computer science , context (archaeology) , autocorrelation , sample (material) , panel data , audit , specification , accounting , economics , statistics , mathematics , artificial intelligence , paleontology , chemistry , chromatography , biology
We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include the standard clustered and multiway clustered estimators, and discuss alternative sample‐splitting inference procedures, such as the Fama–Macbeth procedure, within this context. We outline pros and cons of the different procedures. We then illustrate the properties of the discussed procedures within a simulation experiment designed to mimic the type of firm‐level panel data that might be encountered in accounting and finance applications. Our conclusion, based on theoretical properties and simulation performance, is that sample‐splitting procedures with suitably chosen splits are the most likely to deliver robust inferential statements with approximately correct coverage properties in the types of large, heterogeneous panels many researchers are likely to face.