Stochastic Frontier Analysis Routines
Extract coefficients of stochastic frontier models
Compute conditional (in-)efficiency estimates of stochastic frontier m...
Extract frontier information to be used with texreg package
Extract fitted values of stochastic frontier models
Extract information criteria of stochastic frontier models
Extract log-likelihood value of stochastic frontier models
Marginal effects of the inefficiency drivers in stochastic frontier mo...
Extract total number of observations used in frontier models
Extract residuals of stochastic frontier models
Stochastic frontier estimation using cross-sectional data
Latent class stochastic frontier using cross-sectional data
Deprecated functions of sfaR
sfaR: A package for estimating stochastic frontier models
Sample selection in stochastic frontier estimation using cross-section...
Skewness test for stochastic frontier models
Summary of results for stochastic frontier models
Compute variance-covariance matrix of stochastic frontier models
Maximum likelihood estimation for stochastic frontier analysis (SFA) of production (profit) and cost functions. The package includes the basic stochastic frontier for cross-sectional or pooled data with several distributions for the one-sided error term (i.e., Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential and truncated skewed Laplace), the latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021) <doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data, and the sample selection model as described in Greene (2010) <doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021) <doi:10.1111/agec.12683>. Several possibilities in terms of optimization algorithms are proposed.