论文标题
多元分布随机边界模型
Multivariate Distributional Stochastic Frontier Models
论文作者
论文摘要
随机前沿（SF）分析的主要目的是将估计的误差项对噪声和效率低下的反卷积。假设具有参数生产函数（例如Cobb-Douglas，TransLog等）可能会导致虚假效率估计值。为了克服这一限制假设，可以利用P-Spline建模生产函数。这种强大而灵活的工具的应用实现了广泛的生产功能的建模。此外，一个人可以允许组合误差分布的参数以功能形式取决于协变量。然后，可以将SF模型施放到用于位置，比例和形状（GAMLSS）的广义添加剂模型的框架中。此外，决策单元（DMU）通常会产生多个输出。它通过操作多个子DMU来做到这一点，每个sub-dmus都采用生产过程来产生单个输出。因此，子-DMU的生产过程通常不是独立的。因此，效率低下也可以依赖。在本文中，引入了分布随机前沿模型（DSFM）。组合误差项的多元分布是使用Copula建模的。结果，提出的模型是Lai and Huang（2013）看似无关的随机前沿回归模型的概括。
The primary objective of Stochastic Frontier (SF) Analysis is the deconvolution of the estimated composed error terms into noise and inefficiency. Assuming a parametric production function (e.g. Cobb-Douglas, Translog, etc.), might lead to false inefficiency estimates. To overcome this limiting assumption, the production function can be modelled utilizing P-splines. Application of this powerful and flexible tool enables modelling of a wide range of production functions. Additionally, one can allow the parameters of the composed error distribution to depend on covariates in a functional form. The SF model can then be cast into the framework of a Generalized Additive Model for Location, Scale and Shape (GAMLSS). Furthermore, a decision-making unit (DMU) typically produces multiple outputs. It does this by operating several sub-DMUs, which each employ a production process to produce a single output. Therefore, the production processes of the sub-DMUs are typically not independent. Consequently, the inefficiencies may be expected to be dependent, too. In this paper, the Distributional Stochastic Frontier Model (DSFM) is introduced. The multivariate distribution of the composed error term is modeled using a copula. As a result, the presented model is a generalization of the model for seemingly unrelated stochastic frontier regressions by Lai and Huang (2013).
