Data Envelopment Analysis as Nonparametric Least-Squares Regression

Authors:
Timo Kuosmanen;Andrew L. Johnson
Affiliations:
Economic Research Unit, MTT Agrifood Research Finland, 00410 Helsinki, Finland, and Department of Business Technology, Helsinki School of Economics, 00101 Helsinki, Finland;Department of Industrial and Systems Engineering, Texas A&M University, College Station, Texas 77843
Venue:
Operations Research
Year:
2010

Citing 2
Cited 3

A model to evaluate variables impacting the productivity of software maintenance projects

Management Science
Maximum likelihood, consistency and data envelopment analysis: a statistical foundation

Management Science

Large-scale Internet benchmarking: Technology and application in warehousing operations

Computers in Industry
Measuring Eco-Inefficiency: A New Frontier Approach

Operations Research
Multivariate convex regression with adaptive partitioning

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data envelopment analysis (DEA) is known as a nonparametric mathematical programming approach to productive efficiency analysis. In this paper, we show that DEA can be alternatively interpreted as nonparametric least-squares regression subject to shape constraints on the frontier and sign constraints on residuals. This reinterpretation reveals the classic parametric programming model by Aigner and Chu [Aigner, D., S. Chu. 1968. On estimating the industry production function. Amer. Econom. Rev.58 826--839] as a constrained special case of DEA. Applying these insights, we develop a nonparametric variant of the corrected ordinary least-squares (COLS) method. We show that this new method, referred to as corrected concave nonparametric least squares (C2NLS), is consistent and asymptotically unbiased. The linkages established in this paper contribute to further integration of the econometric and axiomatic approaches to efficiency analysis.