Panel data in accounting and finance: theory and application

The use of models that involve longitudinal data in accounting and finance is common. However, there is often a lack of proper care regarding the criteria for adopting one model over another as well as an insufficiently detailed discussion of the possible estimators to be studied in each situation. This article presents, in conceptual and applied form, the main panel data estimators that can be used in these areas of knowledge and discusses the definition of the most consistent model to be adopted in function of the data characteristics. The models covered for short panels are the POLS with clustered robust standard errors, with between estimator, fixed effects, fixed effects with clustered robust standard errors, random effects and random effects with clustered robust standard errors. In turn, for long panels, the models discussed are fixed effects, random effects, fixed effects with AR(1) error terms, random effects with AR(1) error terms, POLS with AR(1) errors and pooled FGLS with AR(1) errors. The models are also applied to a real case, based on data from Compustat Global. At the end, the main routines for applying each of the models in Stata are presented.