In the productivity modelling literature, the disturbances U (representing technical inefficiency) and V (representing noise) of the composite error W = V -U of the stochastic frontier model are assumed to be independent random variables. By employing the copula approach to statistical modelling, the joint behaviour of U and V can be parametrized thereby allowing the data the opportunity to determine the adequacy of the independence assumption. In this context, three examples of the copula approach are given: the first is algebraic (the Logistic-Exponential stochastic frontier model with margins bound by the Farlie-Gumbel-Morgenstern copula), the second uses a cross-section of cost data sampled from the US electrical power industry and the third constructs a model for panel data that is then used to conduct a Monte Carlo exercise in which estimator bias is examined when the dependence structure is incorrectly ignored.
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机译：在生产率建模文献中，随机边界模型的复合误差W = V -U的扰动U（代表技术效率低下）和V（代表噪声）被假定为独立随机变量。通过使用copula方法进行统计建模，可以对U和V的联合行为进行参数化，从而使数据有机会确定独立性假设的充分性。在这种情况下，给出了copula方法的三个示例：第一个是代数的（边际受Farlie-Gumbel-Morgenstern copula约束的Logistic-指数随机边界模型），第二个使用从美国电力行业，第三个模型构建了面板数据模型，然后用于进行蒙特卡洛练习，在该模型中，当不正确地忽略依赖结构时，将检查估计偏差。
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