sfa: Fitting stochastic frontier analysis models

Description Usage Arguments Value Examples

View source: R/sfa.R

Description

sfa is used to fit stochastic frontier analysis models.

Usage

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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

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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 29, 2017, 5:51 p.m.