Description Details Author(s) References
This package provides a variety of tools for nonparametric and parametric efficiency measurement.
The nonparametric models in
npsf comprise nonradial efficiency measurement (
tenonradial), where non-proportional reductions (expansions) in each positive input (output) are allowed, as well as popular radial efficiency measurement (
teradial), where movements to the frontier are proportional.
Using bootstrapping techniques,
nptestind deal with statistical inference about the radial efficiency measurement.
nptestind helps in deciding which type of the bootstrap to employ. Global return to scale and individual scale efficiency is tested by
tenonradialbc, performs bias correction of the radial Debrue-Farrell and nonradial Russell input- or output-based measure of technical efficiency, computes bias and constructs confidence intervals.
Computer intensive functions
nptestrts allow making use of parallel computing, even on a single machine with multiple cores. Help files contain examples that are intended to introduce the usage.
The parametric stochastic frontier models in
npsf can be estimated by
sf, which performs maximum likelihood estimation of the frontier parameters and technical or cost efficiencies. Inefficiency error component can be assumed to be have either half-normal or truncated normal distribution.
sf allows modelling multiplicative heteroskedasticity of either inefficiency or random noise component, or both. Additionally, marginal effects of determinants on the expected value of inefficiency term can be computed.
For details of the respective method please see the reference at the end of this introduction and of the respective help file.
All function in
npsf accept formula with either names of variables in the data set, or names of the matrices. Except for
nptestind, all function return
esample, a logical vector length of which is determined by
subset (if specified) or number of rows in matrix
TRUE if this data point parted in estimation procedure, and
Results can be summarized using
Oleg Badunenko, <firstname.lastname@example.org>
Pavlo Mozharovskyi, <email@example.com>
Yaryna Kolomiytseva, <firstname.lastname@example.org>
Maintainer: Oleg Badunenko <email@example.com>
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Badunenko, O. and Mozharovskyi, P. (2016), Nonparametric Frontier Analysis using Stata, Stata Journal, 163, 550–89, doi: 10.1177/1536867X1601600302
Badunenko, O. and Mozharovskyi, P. (2020), Statistical inference for the Russell measure of technical efficiency, Journal of the Operational Research Society, 713, 517–527, doi: 10.1080/01605682.2019.1599778
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Debreu, G. 1951. The Coefficient of Resource Utilization. Econometrica 19: 273–292
Färe, R. and Lovell, C. A. K. (1978), Measuring the technical efficiency of production, Journal of Economic Theory, 19, 150–162, doi: 10.1016/0022-0531(78)90060-1
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Jondrow, J., Lovell, C., Materov, I., Schmidt, P. (1982), On estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19, 233–238
Kneip, A., Simar L., and P.W. Wilson (2008), Asymptotics and Consistent Bootstraps for DEA Estimators in Nonparametric Frontier Models, Econometric Theory, 24, 1663–1697, doi: 10.1017/S0266466608080651
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Kumbhakar, S. (1990), Production Frontiers, Panel Data, and Time-varying Technical Inefficiency. Journal of Econometrics, 46, 201–211
Kumbhakar, S. and Lovell, C. (2003), Stochastic Frontier Analysis. Cambridge: Cambridge University Press, doi: 10.1017/CBO9781139174411
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Simar, L. and P.W. Wilson (1998), Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models, Management Science, 44, 49–61, doi: 10.1287/mnsc.44.1.49
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Wang, H.-J. (2002), Heteroskedasticity and non-monotonic efficiency effects of a stochastic frontier model. Journal of Productivity Analysis, 18, 241–253
Wilson P.W. (2003), Testing Independence in Models of Productive Efficiency, Journal of Productivity Analysis, 20, 361–390, doi: 10.1023/A:1027355917855
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