fan
(Package: semsfa) :
Pseudolikelihood estimator of the eqn{lambda
Pseudolikelihood estimator of the λ parameter
● Data Source:
CranContrib
● Keywords:
● Alias: fan
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semsfa
(Package: semsfa) :
Semiparametric Estimation of Stochastic Frontier Models
Semiparametric Estimation of Stochastic Frontier Models following the two step procedure proposed by Fan et al (1996) and further developed by Vidoli and Ferrara (2014). In the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions regards the functional form of the frontier and in the second step variance parameters are obtained by pseudolikelihood or method of moments estimators.
● Data Source:
CranContrib
● Keywords:
● Alias: semsfa
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plot.semsfa
(Package: semsfa) :
Default SEMSFA plotting
This function plots the semiparametric/nonparametric intermediate model object estimated in the first step of the algorithm and, if efficiencies.semsfa() is esecuted, individual point estimate of the efficiency.
● Data Source:
CranContrib
● Keywords:
● Alias: plot.semsfa
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efficiencies.semsfa
(Package: semsfa) :
Prediction of the individual efficiency score
This function calculates and returns efficiency estimates from semiparametric stochastic frontier models estimated with semsfa().
● Data Source:
CranContrib
● Keywords:
● Alias: efficiencies.semsfa
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Create and print summary results of a stochastic frontier model object returned by semsfa() with regard to the "CONDITIONAL EXPECTATION ESTIMATE" of the first step and to the "VARIANCE COMPONENTS ESTIMATE" of the compound error.
● Data Source:
CranContrib
● Keywords:
● Alias: summary.semsfa
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semsfa-package
(Package: semsfa) :
Semiparametric Stochastic Frontier Models
Semiparametric Estimation of Stochastic Frontier Models following the two step procedure originally proposed by Fan et al (1996) and further developed also by Vidoli and Ferrara (2014). In the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions regards the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood or method of moment estimators.
● Data Source:
CranContrib
● Keywords:
● Alias: semsfa-package
●
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providing 
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