Addressing Endogeneity Without Instrumental Variables: An Evaluation of the Gaussian Copula Approach for Management Research
评估了高斯Copula方法作为工具变量回归的替代方案，用于纠正管理研究中的内生性问题，通过模拟揭示了其假设违反时的性能局限，并为学者提供了实用指南。
The availability and quality of instrumental variables (IV) are frequent concerns in empirical management research when trying to overcome endogeneity problems. For endogeneity that does not arise from sample selection, management scholars have recently started to apply the Gaussian Copula (GC) approach as an alternative to IV regression. Although the GC approach has various promising features, its limitations and usefulness in a management context are still not fully understood. We discuss the GC approach as a flexible, instrument-free approach to correct for endogeneity and examine its suitability for applied management research. We use simulations to explore the limitations and practical usefulness of the GC approach relative to ordinary least squares (OLS), IV regression, and a Higher Moments (HM) estimator by simulating the impact of different degrees of violation of the key underlying assumptions of the GC approach. We show that the GC approach can recover the true parameters remarkably well if all of its assumptions are met but that its absolute and relative performance in terms of parameter recovery and estimation precision can deteriorate quickly if these assumptions are violated. This is of particular concern as some of these assumptions are not testable and violations of them are likely in many empirical management contexts. Based on our results, we provide a series of recommendations and practical guidelines for scholars who consider using the GC approach when dealing with endogeneity.