sfa (version 1.0-1)

sfa: Fitting stochastic frontier analysis models

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)