Data Envelopment Analysis as Nonparametric Least-Squares Regression

• 成果类型: Article
• 署名作者: Kuosmanen, Timo; Johnson, Andrew L.
• 署名单位: Natural Resources Institute Finland (Luke); Aalto University; Texas A&M University System; Texas A&M University College Station
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.1090.0722
• 发表日期: 2010
• 页码: 149-160
• 关键词: dual bases algorithm; models; convex; Consistency; constraints; EFFICIENCY

摘要

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 ((CNLS)-N-2), is consistent and asymptotically unbiased. The linkages established in this paper contribute to further integration of the econometric and axiomatic approaches to efficiency analysis.