sfaR (version 1.0.0)

summary: Summary of results for stochastic frontier models

Description

Create and print summary results for stochastic frontier models returned by sfacross, sfalcmcross, or sfaselectioncross.

Usage

# S3 method for sfacross
summary(object, grad = FALSE, ci = FALSE, ...)
# S3 method for summary.sfacross
print(x, digits = max(3, getOption("digits") - 2), ...)
# S3 method for sfalcmcross
summary(object, grad = FALSE, ci = FALSE, ...)
# S3 method for summary.sfalcmcross
print(x, digits = max(3, getOption("digits") - 2), ...)
# S3 method for sfaselectioncross
summary(object, grad = FALSE, ci = FALSE, ...)
# S3 method for summary.sfaselectioncross
print(x, digits = max(3, getOption("digits") - 2), ...)

Value

The summary method returns a list of class 'summary.sfacross', 'summary.sfalcmcross', or

'summary.sfaselectioncross'

that contains the same elements as an object returned by sfacross, sfalcmcross, or sfaselectioncross with the following additional elements:

AIC

Akaike information criterion.

BIC

Bayesian information criterion.

HQIC

Hannan-Quinn information criterion.

sigmavSq

For object of class 'sfacross' or 'sfaselectioncross'. Variance of the two-sided error term (\(\sigma_v^2\)).

sigmauSq

For object of class 'sfacross' or 'sfaselectioncross'. Parametrization of the variance of the one-sided error term (\(\sigma_u^2\)).

Varu

For object of class 'sfacross' or 'sfaselectioncross'. Variance of the one-sided error term.

theta

For object of class 'sfacross' with 'udist = uniform'. \(\Theta\) value in the case the uniform distribution is defined as: \(u_i \in [0, \Theta]\).

Eu

For object of class 'sfacross' or 'sfaselectioncross'. Expected unconditional inefficiency (\(E[u]\)).

Expu

For object of class 'sfacross' or 'sfaselectioncross'. Expected unconditional efficiency (\(E[\exp(u)]\)).

olsRes

For object of class 'sfacross'. Matrix of OLS estimates, their standard errors, t-values, P-values, and when ci = TRUE their confidence intervals.

ols2StepRes

For object of class 'sfaselectioncross'. Matrix of OLS 2 step estimates, their standard errors, t-values, P-values, and when ci = TRUE their confidence intervals.

mlRes

Matrix of ML estimates, their standard errors, z-values, asymptotic P-values, and when grad = TRUE their gradient, ci = TRUE their confidence intervals.

chisq

For object of class 'sfacross'. Chi-square statistics of the difference between the stochastic frontier and the OLS.

df

Degree of freedom for the inefficiency model.

Arguments

object

An object of either class 'sfacross' returned by the function sfacross, or 'sfalcmcross' returned by the function sfalcmcross, or class 'sfaselectioncross' returned by the function sfaselectioncross.

grad

Logical. Default = FALSE. If TRUE, the gradient for the maximum likelihood (ML) estimates of the different parameters is returned.

ci

Logical. Default = FALSE. If TRUE, the 95% confidence interval for the different parameters (OLS or/and ML estimates) is returned.

...

Currently ignored.

x

An object of either class 'summary.sfacross', 'summary.sfalcmcross', or
'summary.sfaselectioncross'.

digits

Numeric. Number of digits displayed in values.

See Also

sfacross, for the stochastic frontier analysis model fitting function for cross-sectional or pooled data.

sfalcmcross, for the latent class stochastic frontier analysis model fitting function for cross-sectional or pooled data.

sfaselectioncross for sample selection in stochastic frontier model fitting function for cross-sectional or pooled data.

print for printing sfacross object.

coef for extracting coefficients of the estimation.

efficiencies for computing (in-)efficiency estimates.

fitted for extracting the fitted frontier values.

ic for extracting information criteria.

logLik for extracting log-likelihood value(s) of the estimation.

marginal for computing marginal effects of inefficiency drivers.

residuals for extracting residuals of the estimation.

vcov for computing the variance-covariance matrix of the coefficients.

bread for bread for sandwich estimator.

estfun for gradient extraction for each observation.

skewnessTest for implementing skewness test.

Examples

## Using data on fossil fuel fired steam electric power generation plants in the U.S.
# Translog SFA (cost function) truncated normal with scaling property
tl_u_ts <- sfacross(formula = log(tc/wf) ~ log(y) + I(1/2 * (log(y))^2) +
log(wl/wf) + log(wk/wf) + I(1/2 * (log(wl/wf))^2) + I(1/2 * (log(wk/wf))^2) +
I(log(wl/wf) * log(wk/wf)) + I(log(y) * log(wl/wf)) + I(log(y) * log(wk/wf)),
udist = 'tnormal', muhet = ~ regu, uhet = ~ regu, data = utility, S = -1,
scaling = TRUE, method = 'mla')
summary(tl_u_ts, grad = TRUE, ci = TRUE)