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

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.

摘要 研究目的：以简洁实用的方式阐释原始回归元的特定滞后项（及/或时间差）作为工具变量的应用路径；并通过原创性模拟实验辅助理论探讨，说明将此类滞后项与企业固定效应、时间固定效应的合理建模相结合，不仅能够解决动态内生性问题，还可处理由遗漏变量、测量误差以及因变量与自变量间双向因果关系所引发的各类内生性问题。
设计/研究方法：蒙特卡洛（Monte Carlo）模拟实验
研究发现：传统普通最小二乘法（Ordinary Least Squares，OLS）、随机效应（Random Effects，RE）与固定效应（Fixed Effects，FE）估计量，在面临公司金融场景中颇为常见的内生性问题时，可能出现估计结果不一致的情况。另一方面，基于广义矩估计（Generalized Method of Moments，GMM）的面板数据估计方法，若采用回归元序列外生性假设，则可提供可有效克服上述所有问题的替代方案（前提为该假设成立），即便研究者无法获取模型外部的优质工具变量。
研究创新性与价值：本文采用相较于专业技术语言更通俗易懂的表述，面向尚未掌握面板数据动态模型估计复杂细节的研究者，讨论并演示了更多类型的内生性问题，同时阐明了不同面板数据估计量对这些问题的解决路径。