[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-1-4899-7553-9_7.html
   My bibliography  Save this book chapter

Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework

In: Data Envelopment Analysis

Author

Listed:
  • Timo Kuosmanen

    (Aalto University)

  • Andrew Johnson

    (Aalto University
    Texas A&M University)

  • Antti Saastamoinen

    (Aalto University)

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.

Suggested Citation

  • Timo Kuosmanen & Andrew Johnson & Antti Saastamoinen, 2015. "Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 7, pages 191-244, Springer.
    • Handle: RePEc:spr:isochp:978-1-4899-7553-9_7
    DOI: 10.1007/978-1-4899-7553-9_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:isochp:978-1-4899-7553-9_7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.