SFM.within: Within Transformation of a Stochastic Frontier Model

In clemenshaerder/fepsfrontieR: Estimation of fixed-effect panel stochastic frontier models by MLE

Description

Performs a within transformation to a Stochastic Frontier Model. By within-transformation, the sample mean of each panel is subtracted from every observation in the panel. The transformation thus removes the time-invariant individual effect from the model

Usage

SFM.within(par = c(sigma_u = NULL, sigma_v = NULL, beta = c(), delta =
  c()), xv, y, z, N = NULL, Time = NULL, cumTime, mu = 0,
  optim = F, K = NULL, R = NULL, seqN = 1:N)

Arguments

par	is a vector of regression coefficients & variance parameters. 1st parameter: sigma_u, 2nd parameter: sigma_v, followed by K beta & R delta coefficients
xv	is a n*t x k matrix (explantatory variables)
y	is a n*t x 1 vector (response)
z	is a n*t x r matrix (inefficency determinants)
N	is a integer (n - panels)
Time	is a integer (observations per panel)
cumTime	ia a vector of the cumulated times of the Time vector. It serves as an index for computation.
mu	is a number (mean of the truncated normal distribution of the inefficency)
optim	is a boolean (set F to obtain a list of model variables. T to obtain the -sum of log.likelihood)
K	is an integer (# of xv variables)
R	is an integer (# of z variables)
seqN	is a sequence from 1 to N

Value

If optim = T the log.likelihood is returned of all panels. If optim = F the model fit is returned including all important model variables.

clemenshaerder/fepsfrontieR documentation built on May 22, 2019, 3:43 p.m.