Title
Technical Efficiency Estimation with Multiple Inputs and Multiple Outputs Using Regression Analysis
Document Type
Article
Publication Date
1-2011
Publication Source
European Journal of Operational Research
Abstract
Regression and linear programming provide the basis for popular techniques for estimating technical efficiency. Regression-based approaches are typically parametric and can be both deterministic or stochastic where the later allows for measurement error. In contrast, linear programming models are nonparametric and allow multiple inputs and outputs. The purported disadvantage of the regression-based models is the inability to allow multiple outputs without additional data on input prices. In this paper, deterministic cross-sectional and stochastic panel data regression models that allow multiple inputs and outputs are developed. Notably, technical efficiency can be estimated using regression models characterized by multiple input, multiple output environments without input price data. We provide multiple examples including a Monte Carlo analysis.
Inclusive pages
153-160
ISBN/ISSN
0377-2217
Publisher
Elsevier
Volume
208
Peer Reviewed
yes
Issue
2
eCommons Citation
Collier, Trevor and Johnson, Andrew L., "Technical Efficiency Estimation with Multiple Inputs and Multiple Outputs Using Regression Analysis" (2011). Economics and Finance Faculty Publications. 11.
https://ecommons.udayton.edu/eco_fac_pub/11
COinS