Endogeneity in panel data regressions: methodological guidance for corporate finance researchers

Lucas Barros,Daniel Bergmann,Alexandre Da Silveira,F Henrique Castro

doi:10.7819/rbgn.v22i0.4059

Abstract

Purpose –To describe the use of specific lags (and/or temporal differences) of the original regressors as instrumental variables in a succinct and practical way, showing, by means of a theoretical discussion illustrated by an original simulation exercise, how combining these with adequate modeling of firm and time fixed effects can address not only the dynamic endogeneity problem, but also those derived from the presence of omitted variables, measurement errors, and simultaneity between dependent and independent variables. Design/methodology/approach – Monte Carlo simulation Findings – The traditional OLS, RE, and FE estimators may be inconsistent in the presence of endogeneity problems that are quite plausible in the context of corporate finance. On the other hand, the estimation methods for panel data based on GMM that use assumptions of sequential exogeneity of the regressors present alternatives that are capable of effectively overcoming all the problems listed (provided these assumptions are valid) even if the researcher does not have good instrumental variables that are external to the model Originality/value –The paper discusses and illustrates a greater number of endogeneity problems, showing how they are addressed by different estimators for panel data, using less technical and more accessible language for researchers not yet initiated in the intricacies of estimating dynamic models for panel data.

Highlights

A large proportion of empirical studies in corporate finance use panel data, observing N firms over T time periods
The main objective of this study is to describe this estimation strategy in a succinct and practical way, showing, by means of the theoretical discussion illustrated by an original simulation exercise, how combining it with adequate modeling of firm and time fixed effects can address the dynamic endogeneity problem, and those derived from the presence of omitted variables, measurement errors, and simultaneity between dependent and independent variables
As this laboratorial analysis synthesizes key and important aspects of the data typically used in empirical studies in corporate finance, it can offer a methodological guide for researchers in the area, on one hand highlighting some of the biggest concerns they should pay attention to and, on the other hand, offering possible solutions

Summary

Introduction

A large proportion of empirical studies in corporate finance use panel data, observing N firms over T time periods (typically, with a much lower T than N ). Other areas of investigation analyze the various factors that can influence the market value, financial performance, or operational performance of firms These factors can include the firm’s capital structure, its corporate governance structure, and the characteristics of its managers, among others (e.g., Bertrand & Schoar, 2003; Himmelberg, Hubard, & Palia, 1999). Of all the assumptions needed for a regression analysis to yield appropriate inferences regarding causal relationships between variables, the most important is the assumption of exogeneity of the regressors. This is the hardest to verify and the most implausible when data collected from firms are used. The endogeneity problem in the context of corporate finance normally derives from the existence of omitted variables, measurement errors of the variables included in the model, and/or simultaneity between the dependent and independent variables

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Conclusion