Data Envelopment Analysis as nonparametric least-squares regression

Data Envelopment Analysis as nonparametric least-squares regression

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Article ID: iaor20101091
Volume: 58
Issue: 1
Start Page Number: 149
End Page Number: 160
Publication Date: Jan 2010
Journal: Operations Research
Authors: ,
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 (1968) 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.

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