sfaR-package {sfaR}

sfaR: A package for estimating stochastic frontier models

Description

The sfaR package provides a set of tools (maximum likelihood - ML and maximum simulated likelihood - MSL) for various specifications of stochastic frontier analysis (SFA).

Details

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

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

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

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.

Bugreport

Any bug or suggestion can be reported using the sfaR tracker facilities at: https://github.com/hdakpo/sfaR/issues

Author(s)

K Hervé Dakpo, Yann Desjeux, Arne Henningsen and Laure Latruffe