Package: sfa 1.2.1

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.

Authors:David Bernstein [aut, cre], Christopher Parmeter [aut], Alexander Stead [aut]