Stochastic frontier models with dependent error components

Abstract

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 118 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.