Nonparametric and Stochastic Efficiency and Productivity Analysis

Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) , Kneip, Simar, and Wilson (2008) and Badunenko and Mozharovskyi (2020) ) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) , Badunenko and Kumbhakar (2016) ). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.


Reference manual

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0.8.0 by Oleg Badunenko, a year ago

Browse source code at

Authors: Oleg Badunenko [aut, cre] , Pavlo Mozharovskyi [aut] , Yaryna Kolomiytseva [aut]

Documentation:   PDF Manual  

GPL-2 license

Depends on Formula

Suggests snowFT, Rmpi

Linking to Rcpp

See at CRAN