Multivariate Distributional Stochastic Frontier Models

TL;DR

This paper introduces a flexible multivariate stochastic frontier model that uses copulas to capture dependencies among multiple outputs and allows for covariate-dependent error distribution parameters, enhancing efficiency estimation accuracy.

Contribution

It develops a novel distributional stochastic frontier model incorporating copulas and P-splines within a GAMLSS framework for multivariate outputs with dependent inefficiencies.

Findings

Model captures dependence among sub-DMUs' inefficiencies.

Flexible modeling of production functions with P-splines.

Generalizes previous seemingly unrelated stochastic frontier models.

Abstract

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).