Endogeneity in regression models is a key marketing research concern. The Gaussian copula approach offers an instrumental variable (IV)-free technique to mitigate endogeneity bias in regression models. Previous research revealed substantial finite sample bias when applying this method to regression models with an intercept. This is particularly problematic as models in marketing studies almost always require an intercept. To resolve this limitation, our research determines the bias’s sources, making several methodological advances in the process. First, we show that the cumulative distribution function estimation’s quality strongly affects the Gaussian copula approach’s performance. Second, we use this insight to develop an adjusted estimator that improves the Gaussian copula approach’s finite sample performance in regression models with (and without) an intercept. Third, as a broader contribution, we extend the framework for copula estimation to models with multiple endogenous variables on continuous scales and exogenous variables on discrete and continuous scales, and non-linearities such as interaction terms. Fourth, simulation studies confirm that the new adjusted estimator outperforms the established ones. Further simulations also underscore that our extended framework allows researchers to validly deal with multiple endogenous and exogenous regressors, and the interactions between them. Fifth, we demonstrate the adjusted estimator and the general framework’s systematic application, using an empirical marketing example with real-world data. These contributions enable researchers in marketing and other disciplines to effectively address endogeneity problems in their models by using the improved Gaussian copula approach.

回归模型中的内生性是一个关键的市场研究关注点。Gaussian copula 方法提供了一种无工具变量 （IV） 的技术来减轻回归模型中的内生性偏差。先前的研究表明，当将这种方法应用于具有截距的回归模型时，存在很大的有限样本偏差。这尤其成问题，因为市场营销研究中的模型几乎总是需要截距。为了解决这一限制，我们的研究确定了偏差的来源，并在此过程中取得了一些方法上的进步。首先，我们表明累积分布函数估计的质量强烈影响 Gaussian copula 方法的性能。其次，我们利用这一见解开发了一个调整后的估计器，以提高高斯 copula 方法在有（和没有）截距的回归模型中的有限样本性能。第三，作为更广泛的贡献，我们将 copula 估计的框架扩展到在连续尺度上具有多个内生变量、在离散和连续尺度上具有外生变量以及非线性（如交互项）的模型。第四，仿真研究证实，新的调整后的估计器优于现有的估计器。进一步的模拟还强调，我们的扩展框架允许研究人员有效地处理多个内生和外生回归变量，以及它们之间的相互作用。第五，我们使用具有真实数据的经验营销示例来演示调整后的估计器和一般框架的系统应用。 这些贡献使市场营销和其他学科的研究人员能够通过使用改进的 Gaussian copula 方法有效地解决其模型中的内生性问题。