sfa: Fitting stochastic frontier analysis models

In sfa: Stochastic Frontier Analysis

Description

sfa is used to fit stochastic frontier analysis models.

Usage

sfa(formula, data = NULL, intercept = TRUE, fun = "hnormal",
pars = NULL, par_mu = NULL, form = "cost", method = "BFGS", ...)

Arguments

formula	an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	a data frame.
intercept	logical. If true the model includes intercept.
fun	specifies the distribution for the inefficency term u as half-normal ("hnormal"), exponential ("exp"), or truncated-normal ("tnormal").
pars	initial values for the parameters to be estimated.
par_mu	value for mu in the normal-/truncated-normal case. If mu is known.
form	specifies the form of the frontier model as "cost" or "production".
method	the method to be used. See optim for more details.
...	ignored.

Value

sfa returns an object of class sfa:

y	response
x	covariables
X	design matrix
coef	coefficients
sigmau2	sigmau2
sigmav2	sigmav2
mu	mu of the truncated-normal distribution (Only if fun = tnormal)
par_mu	NULL if mu is not estimated
logLik	value of the log likelihood function
maxlik	log likelihood function
fun	distribution of the inefficiency term u
sc	specifies the form of the frontier model (-1 = cost, 1 = production)
hess	a symmetric matrix giving an estimate of the Hessian at the solution found (See optim)
ols	the linear model for the LR-test

Examples

set.seed(225)
daten <- dgp(n = 100, b = c(1, 2), sc = -1)
test <- sfa(y ~ x, data = daten)

Example output

There were 18 warnings (use warnings() to see them)

sfa documentation built on May 2, 2019, 4:38 a.m.