Regression analysis is one of the most popularly used statistical technique which only measures the direct effect of independent variables on dependent variable. Path analysis looks for both direct and indirect effects of independent variables and may overcome several hurdles allied with regression models. It utilizes one or more structural regression equations in the model which are used to estimate the unknown parameters. The aim of this work is to study the path analysis models when the endogenous (dependent) variable and exogenous (independent) variables are linked through the elliptical copulas. Using well-organized numerical schemes, we investigate the performance of path models when direct and indirect effects are estimated applying classical ordinary least squares and copula-based regression approaches in different scenarios. Finally, two real data applications are also presented to demonstrate the performance of path analysis using copula approach.

翻译：回归分析是最常用的统计技术之一，但其仅能衡量自变量对因变量的直接影响。路径分析则同时考察自变量的直接效应与间接效应，并可克服回归模型相关的若干障碍。该方法在模型中使用一个或多个结构回归方程来估计未知参数。本文旨在研究当内生（因）变量与外生（自）变量通过椭圆Copula连接时的路径分析模型。通过设计完善的数值模拟方案，我们在不同情境下比较了应用经典普通最小二乘法与基于Copula的回归方法估计直接效应和间接效应时路径模型的性能。最后，通过两个实际数据应用展示了采用Copula方法进行路径分析的效果。