green

The dataset consists of 123 American electric utility companies. As in the set-up of Gijbels et al. (1999), 
we used the measurements of the variables \(y_i = \log(q_i)\) and 
\(x_i = \log(c_i)\), where \(q_i\) is the production output 
of the company \(i\) and \(c_i\) is the total cost involved in the production. 
For a detailed description and analysis of these data see, e.g., Christensen and Greene (1976) and Greene (1990).

datasets

A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.

Thibault Laurent

npbr

Nonparametric Boundary Regression

green function

A data frame with 123 observations on the following 2 variables.<dl>
 <dt><code>COST</code></dt>
<dd>a numeric vector.</dd> <dt><code>OUTPUT</code></dt>
<dd>a numeric vector.</dd> 
</dl>

Format

American electric utility companies — green

A data frame with 123 observations on the following 2 variables.<dl>
 <dt><code>COST</code></dt>
<dd>a numeric vector.</dd>

 <dt><code>OUTPUT</code></dt>
<dd>a numeric vector.</dd>

 
</dl>

green: American electric utility companies

Description

Usage

Arguments

Format

References

Examples