summary: Summary of results for classic or latent class stochastic...

In sfaR: Stochastic Frontier Analysis using R

Summary of results for classic or latent class stochastic models

Description

Create and print summary results for classic or latent class stochastic models returned by sfacross and lcmcross.

Usage

## S3 method for class 'sfacross'
summary(object, grad = FALSE, ci = FALSE, ...)

## S3 method for class 'summary.sfacross'
print(x, digits = max(3, getOption("digits") - 2), ...)

## S3 method for class 'lcmcross'
summary(object, grad = FALSE, ci = FALSE, ...)

## S3 method for class 'summary.lcmcross'
print(x, digits = max(3, getOption("digits") - 2), ...)

Arguments

object	An object of either class 'sfacross', returned by the function sfacross, or class 'lcmcross', returned by the function lcmcross.
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 and ML estimates) is returned.
...	Currently ignored.
x	An object of either class 'summary.sfacross' or 'summary.lcmcross'.
digits	Numeric. Number of digits displayed in values.

Value

The summary method returns a list of class 'summary.sfacross' or 'summary.lcmcross' that contains the same elements as an object returned by sfacross or lcmcross with the following additional elements:

AIC	Akaike information criterion.
BIC	Bayesian information criterion.
HQIC	Hannan-Quinn information criterion.
sigmavSq	For object of class 'sfacross'. Variance of the two-sided error term (σ_v^2).
sigmauSq	For object of class 'sfacross'. Parametrization of the variance of the one-sided error term (σ_u^2).
Varu	For object of class 'sfacross'. Variance of the one-sided error term.
THETA	For object of class 'sfacross' with "udist = uniform". Θ value in the case the uniform distribution is defined as: u_i \in [0, Θ].
Eu	For object of class 'sfacross'. Expected unconditional inefficiency.
Expu	For object of class 'sfacross'. Expected unconditional efficiency.
olsRes	For object of class 'sfacross'. Matrix of OLS 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.

See Also

sfacross, for the stochastic frontier analysis model fitting function.

lcmcross, for the latent class stochastic frontier analysis model fitting function.

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.

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)

sfaR documentation built on May 3, 2022, 3 p.m.