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, teradialbc, nptestrts, 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 nptestrts. Finally, teradialbc performs bias correction of the radial Debrue-Farrell input- or output-based measure of technical efficiency, computes bias and constructs confidence intervals.
Computer intensive functions teradialbc and 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.
We use own LP solver for solving linear programming problems.
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 exogenous variable(s) 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 data and subset (if specified) or number of rows in matrix outputs. esample equals TRUE if this data point parted in estimation procedure, and FALSE otherwise.
Results can be summarized using summary.npsf.
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