Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework

Timo Kuosmanen, Andrew Johnson and Antti Saastamoinen
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Andrew Johnson: Aalto University
Antti Saastamoinen: Aalto University

Chapter 7 in Data Envelopment Analysis, 2015, pp 191-244 from Springer

Abstract: Abstract Bridging the gap between axiomatic Data Envelopment Analysis (DEA) and econometric Stochastic Frontier Analysis (SFA) has been one of the most vexing problems in the field of efficiency analysis. Recent developments in multivariate convex regression, particularly Convex Nonparametric Least Squares (CNLS) method, have led to the full integration of DEA and SFA into a unified framework of productivity analysis, referred to as Stochastic Nonparametric Envelopment of Data (StoNED). The unified framework of StoNED offers a general and flexible platform for efficiency analysis and related themes such as frontier estimation and production analysis, allowing one to combine existing tools of efficiency analysis in novel ways across the DEA-SFA spectrum, facilitating new opportunities for further methodological development. This chapter provides an updated and elaborated presentation of the CNLS and StoNED methods. This chapter also extends the scope of the StoNED method in several directions. Most notably, this chapter examines quantile estimation using StoNED and an extension of the StoNED method to the general case of multiple inputs and multiple outputs. This chapter also provides a detailed discussion of how to model heteroscedasticity in the inefficiency and noise terms.

Keywords: Efficiency analysis; Frontier estimation; Multivariate convex regression; Nonparametric least squares; Productivity; Stochastic noise (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-1-4899-7553-9_7

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