Stochastic frontier models with dependent error components

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, t...

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Bibliographic Details
Published in:The econometrics journal 2008-01, Vol.11 (1), p.172-192
Main Author: Smith, Murray D.
Format: Article
Language:English
Subjects:
Copula
Copula functions
Cost efficiency
Cumulative distribution functions
Dependence
Distribution
Econometrics
Economic models
Error
Estimation bias
Estimators
Industrial efficiency
Probability theory
Productivity
Random variables
Sklar's theorem
Spearman's Sρ
Statistical models
Stochastic frontier model
Stochastic models
Studies
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Summary: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.
ISSN:1368-4221
1368-423X
DOI:10.1111/j.1368-423X.2007.00228.x