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An Introduction to CNLS and StoNED Methods for Efficiency Analysis: Economic Insights and Computational Aspects

In: Benchmarking for Performance Evaluation

Author

Listed:
  • Andrew L. Johnson

    (Texas A&M University
    Aalto University School of Business)

  • Timo Kuosmanen

    (Aalto University School of Business)

Abstract

This chapter describes the economic insights of the unifying framework known as Stochastic semi-Nonparametric Envelopment of Data (StoNED), which combines the virtues of the widely used neoclassic production models, Data Envelopment Analysis (DEA), and Stochastic Frontier Analysis (SFA). Like DEA, StoNED is able to estimate an axiomatic production function relaxing the functional form specification required in most implementations of SFA. However, StoNED is also consistent with the econometric models of noise, providing a distinct advantage over standard DEA models. Further, StoNED allows for the possibility that systematic inefficiency is negligible consistent with neoclassical theory, thus providing a unifying framework. StoNED is implemented by estimating a conditional mean using convex nonparametric least squares (CNLS) followed by using standard SFA techniques to estimate the average efficiency and decompose the residual. Detailed descriptions of General Algebraic Modeling System (GAMS) and matrix laboratory (MATLAB) code will aid readers in implementing the StoNED estimator.

Suggested Citation

  • Andrew L. Johnson & Timo Kuosmanen, 2015. "An Introduction to CNLS and StoNED Methods for Efficiency Analysis: Economic Insights and Computational Aspects," Springer Books, in: Subhash C. Ray & Subal C. Kumbhakar & Pami Dua (ed.), Benchmarking for Performance Evaluation, edition 127, chapter 0, pages 117-186, Springer.
    • Handle: RePEc:spr:sprchp:978-81-322-2253-8_3
    DOI: 10.1007/978-81-322-2253-8_3
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    Citations

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    Cited by:

    1. Cristina Polo & Julián Ramajo & Alejandro Ricci‐Risquete, 2021. "A stochastic semi‐non‐parametric analysis of regional efficiency in the European Union," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 7-24, February.
    2. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    3. Olesen, Ole B. & Petersen, Niels Christian, 2016. "Stochastic Data Envelopment Analysis—A review," European Journal of Operational Research, Elsevier, vol. 251(1), pages 2-21.
    4. Julia Schaefer & Marcel Clermont, 2018. "Stochastic non-smooth envelopment of data for multi-dimensional output," Journal of Productivity Analysis, Springer, vol. 50(3), pages 139-154, December.

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