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.
Package details |
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Author | K Hervé Dakpo [aut, cre], Yann Desjeux [aut], Arne Henningsen [aut], Laure Latruffe [aut] |
Maintainer | K Hervé Dakpo <k-herve.dakpo@inrae.fr> |
License | GPL (>= 3) |
Version | 1.0.1 |
URL | https://github.com/hdakpo/sfaR |
Package repository | View on CRAN |
Installation |
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