sfa - Stochastic Frontier Analysis
Maximum likelihood estimation of stochastic frontier
models, in which observed output falls short of an unobserved
production frontier by a one-sided inefficiency term in
addition to symmetric noise, following Aigner, Lovell and
Schmidt (1977) <doi:10.1016/0304-4076(77)90052-5> and Meeusen
and van den Broeck (1977) <doi:10.2307/2525757>.
Cross-sectional models cover seventeen noise and inefficiency
distribution pairs, with heteroskedasticity permitted in either
error component. Panel models include the true fixed and random
effects estimators of Greene (2005)
<doi:10.1016/j.jeconom.2004.05.003> and the four-component
model of Colombi, Kumbhakar, Martini and Vittadini (2014)
<doi:10.1007/s11123-014-0386-y>. Further entry points address
zero inefficiency, latent classes, two-tier frontiers, sample
selection, nonparametric frontiers, dependence between the
error components, and endogenous regressors after Amsler,
Prokhorov and Schmidt (2016)
<doi:10.1016/j.jeconom.2015.06.013>. Technical efficiency
prediction, marginal effects, and specification tests for wrong
skewness, for the assumed inefficiency distribution and for
heteroskedasticity are provided.
