copulaSFM: Copula-Based Simultaneous Stochastic Frontier Models
Provides estimation procedures for copula-based stochastic frontier
models for cross-sectional data. The package implements maximum likelihood
estimation of stochastic frontier models allowing flexible dependence
structures between inefficiency and noise terms through various copula
families (e.g., Gaussian and Student-t). It enables estimation of technical
efficiency scores, log-likelihood values, and information criteria (AIC and
BIC). The implemented framework builds upon stochastic frontier analysis
introduced by Aigner, Lovell and Schmidt (1977) <doi:10.1016/0304-4076(77)90052-5>
and the copula theory described in Joe (2014, ISBN:9781466583221).
Empirical applications of copula-based stochastic frontier models can be
found in Wiboonpongse et al. (2015) <doi:10.1016/j.ijar.2015.06.001> and
Maneejuk et al. (2017, ISBN:9783319562176).
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