sfaR-package | R Documentation |
The sfaR package provides a set of tools (maximum likelihood - ML and maximum simulated likelihood - MSL) for various specifications of stochastic frontier analysis (SFA).
Three categories of functions are available: sfacross
,
sfalcmcross
, sfaselectioncross
,
which estimate different types of frontiers and offer eleven alternative
optimization algorithms (i.e., "bfgs", "bhhh", "nr", "nm", "cg", "sann",
"ucminf", "mla", "sr1", "sparse", "nlminb").
sfacross
estimates the basic stochastic
frontier analysis (SFA) for cross-sectional or pooled data and allows for
ten different distributions for the one-sided error term. These distributions
include the exponential, the gamma, the generalized exponential,
the half normal, the lognormal, the truncated normal, the truncated skewed
Laplace, the Rayleigh, the uniform, and the Weibull distributions.
In the case of the gamma, lognormal, and Weibull distributions, maximum
simulated likelihood (MSL) is used with the possibility of four specific
distributions to construct the draws: halton, generalized halton, sobol and
uniform. Heteroscedasticity in both error terms can be implemented, in
addition to heterogeneity in the truncated mean parameter in the case of the
truncated normal and lognormal distributions. In addition, in the case of the
truncated normal distribution, the scaling property can be estimated.
sfalcmcross
estimates latent class
stochastic frontier models (LCM) for cross-sectional or pooled data.
It accounts for technological heterogeneity by splitting the observations
into a maximum number of five classes. The classification operates based on
a logit functional form that can be specified using some covariates (namely,
the separating variables allowing the separation of observations in several
classes). Only the half normal distribution is available for the one-sided
error term. Heteroscedasticity in both error terms is possible. The choice of
the number of classes can be guided by several information criteria (i.e.,
AIC, BIC, or HQIC).
sfaselectioncross
estimates the
frontier for cross-sectional or pooled data in the presence of sample
selection. The model solves the selection bias due to the correlation
between the two-sided error terms in both the selection and the frontier
equations. The likelihood can be estimated using five different
possibilities: gauss-kronrod quadrature, adaptive integration over hypercubes
(hcubature and pcubature), gauss-hermite quadrature, and
maximum simulated likelihood. Only the half normal
distribution is available for the one-sided error term. Heteroscedasticity
in both error terms is possible.
Any bug or suggestion can be reported using the
sfaR
tracker facilities at:
https://github.com/hdakpo/sfaR/issues
K Hervé Dakpo, Yann Desjeux, Arne Henningsen and Laure Latruffe
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