npsf: Nonparametric and Stochastic Efficiency and Productivity
Analysis
Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) 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) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). 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.
| Version: |
0.8.0 |
| Depends: |
Formula |
| LinkingTo: |
Rcpp |
| Suggests: |
snowFT, Rmpi |
| Published: |
2020-11-22 |
| Author: |
Oleg Badunenko [aut, cre],
Pavlo Mozharovskyi [aut],
Yaryna Kolomiytseva [aut] |
| Maintainer: |
Oleg Badunenko <oleg.badunenko at brunel.ac.uk> |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| Materials: |
ChangeLog |
| CRAN checks: |
npsf results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=npsf
to link to this page.