copSFM: Copula based Stochastic frontier Model
In woraphonyamaka/CopSFM: Copula based Simultanous stochastic frontier

Description Usage Arguments Details Value Author(s) References Examples

View source: R/copulaSFM.R

In the standard stochastic frontier model, the two-sided error term V and the one-sided technical inefficiency error term W are assumed to be independent. In this paper, we relax this assumption by modeling the dependence between V and W using copulas. Nine copula families are considered and their parameters are estimated using maximum simulated likelihood.

1	copSQM(Y,X,family=1,RHO=0.5,LB=-0.99,UB=0.99)

`Y`	vector of dependent variable
`X`	matrix of independent variable
`family`	Copula function eg. Gaussain=1, Student-t=2 (see, Vinecopula package)
`RHO`	The initail value of the copula parameter
`LB`	The lower bound of the copula parameter
`UB`	The upper bound of the copula parameter

herefore, the above copula families and relevant rotated copula can potentially capture the appropriate dependence between two random variables. Other popular copula families, such as Gaussain, Student,t Clayton, Gumbel etc.

`result`	The result contain the estimated parameters, standard errors, t-stat, and p-value
`AIC`	Akaiki Information Criteria
`BIC`	Bayesian Information Criteria
`Loglikelihood`	Maximum Log-likelihood function

Woraphon Yamaka

Wiboonpongse, A., Liu, J., Sriboonchitta, S., & Denoeux, T. (2015). Modeling dependence between error components of the stochastic frontier model using copula: application to intercrop coffee production in Northern Thailand. International Journal of Approximate Reasoning, 65, 34-44.

Maneejuk, P., Yamaka, W., & Sriboonchitta, S. (2017). Analysis of global competitiveness using copula-based stochastic frontier kink model. In Robustness in Econometrics (pp. 543-559). Springer, Cham.

## Required packages
library(truncnorm)
library(mvtnorm)
library("VineCopula")
library("frontier")

#example simulation data
data=sfa.simu(nob=200, alpha=c(1,2,0.5),sigV=1,sigU=0.5,family=1,rho=0.5)

# Select familty  copula upper and lower bouubd ( look at CDVine package)
# family=1   # 1 is Gaussian, 2 is Student-t, 3 is Clayton and so on....

#Gaussian (-.99, .99)
#Student t (-.99, .99)
#Clayton (0.1, Inf)
model=copSFM(Y=data$Y,X=data$X,family=1,RHO=0.5,LB=-0.99,UB=0.99)