Data Envelopment Analysis as Nonparametric Least-Squares Regression Academic Article

abstract

• Data envelopment analysis (DEA) is known as a nonparametric mathematical programming approach to productive effi-ciency 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. © 2010 INFORMS.

authors

• Johnson, Andrew

published proceedings

• Operations Research

author list (cited authors)

• Kuosmanen, T., & Johnson, A. L.

citation count

• 179

complete list of authors

• Kuosmanen, Timo||Johnson, Andrew L

publication date

• February 2010

publisher

• Institute for Operations Research and the Management Sciences (INFORMS)  Publisher

published in

• Operations Research  Journal

keywords

• Benchmarking
• Frontier Estimation
• Mathematical Programming
• Nonparametric Estimation
• Performance Measurement

Digital Object Identifier (DOI)

• 10.1287/opre.1090.0722

start page

• 149

end page

• 160

volume

• 58

issue

• 1