Skip to content
Register Sign in Wishlist

A Practitioner's Guide to Stochastic Frontier Analysis Using Stata


  • Date Published: February 2015
  • availability: Available
  • format: Paperback
  • isbn: 9781107609464

£ 37.99

Add to cart Add to wishlist

Other available formats:
Hardback, eBook

Request inspection copy

Lecturers may request a copy of this title for inspection

Product filter button
About the Authors
  • A Practitioner's Guide to Stochastic Frontier Analysis Using Stata provides practitioners in academia and industry with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach. The authors explain in detail how to estimate production, cost, and profit efficiency and introduce the basic theory of each model in an accessible way, using empirical examples that demonstrate the interpretation and application of models. This book also provides computer code, allowing users to apply the models in their own work, and incorporates the most recent stochastic frontier models developed in academic literature. Such recent developments include models of heteroscedasticity and exogenous determinants of inefficiency, scaling models, panel models with time-varying inefficiency, growth models, and panel models that separate firm effects and persistent and transient inefficiency. Immensely helpful to applied researchers, this book bridges the chasm between theory and practice, expanding the range of applications in which production frontier analysis may be implemented.

    • Provides stand-alone reference material for the reader to gain the practical understanding of estimating production, profit and cost efficiency
    • Includes a theoretical explanation of the stochastic frontier model interwoven with worked examples of applying the theory to real data
    • Provides tools to apply models to real data by creating a series of programs written for use in Stata
    Read more

    Reviews & endorsements

    'A competent empirical application of Stochastic Frontier Analysis (SFA) requires a clear understanding of both the production economics and the econometric theory behind the specified model side by side with adequate programming skills to write the necessary software codes. Apart from a clear exposition of the economic theory behind various stochastic frontier models that represent the technology (like the Distance Functions) and/or producer behavior (like the Cost or Profit Functions) and the relevant econometric theory, the authors offer detailed instructions on how to write the commands for various models in Stata and explain how to interpret the results. This book will prove to be invaluable for every serious researcher using SFA to measure production efficiency.' Subhash C. Ray, University of Connecticut

    'This book is a significant contribution to an applied introduction to stochastic frontier analysis. The authors explain clearly many of the models used in efficiency estimation, which has become a standard tool in the arsenal of applied economics. They explain clearly the models and the assumptions and provide a thorough introduction to estimating performance and efficiency for the practitioner. The many scientific fields in which efficiency and performance measurement are important will benefit immensely from the book not only because of its clarity and concreteness but also because the models are taken directly to practice using Stata, standard software used by many researchers. The combination of theory and practical application is masterfully done in this book, and practitioners in a vast number of fields will find it indispensable for their research.' Mike G. Tsionas, Athens University of Economics and Business

    See more reviews

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity


    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?


    Product details

    • Date Published: February 2015
    • format: Paperback
    • isbn: 9781107609464
    • length: 374 pages
    • dimensions: 254 x 175 x 25 mm
    • weight: 0.68kg
    • contains: 56 b/w illus.
    • availability: Available
  • Table of Contents

    1. Introduction
    2. Production, distance, cost, and profit functions
    3. Production frontier models
    4. Cost frontier models
    5. Profit frontier models
    6. Cost system models
    7. Profit system models
    8. Primal cost models
    9. Profit primal models
    10. Panel models
    11. Productivity and profitability
    12. Looking ahead.

  • Authors

    Subal C. Kumbhakar, Binghamton University, State University of New York
    Subal C. Kumbhakar is a distinguished research professor at the State University of New York, Binghamton. He specializes in productivity and efficiency analysis, with particular emphasis on the theory and application of stochastic frontier (SF) models. He has developed numerous SF models for both cross-sectional and panel models in a single-equation set-up, as well as in a set-up with simultaneous equations. He is co-editor of Empirical Economics and guest editor of special issues of the Journal of Econometrics, Empirical Economics, the Journal of Productivity Analysis, and the Indian Economic Review. He is associate editor and editorial board member of Technological Forecasting and Social Change: An International Journal, the Journal of Productivity Analysis, the International Journal of Business and Economics, and Macroeconomics and Finance in Emerging Market Economies. He is also the co-author of Stochastic Frontier Analysis (Cambridge, 2000).

    Hung-Jen Wang, National Taiwan University
    Hung-Jen Wang is Professor of Economics at the National Taiwan University. His research interests include stochastic frontier analysis and empirical macroeconomics. He has published research papers in the Journal of Econometrics, the Journal of Business and Economic Statistics, Econometric Review, Economic Inquiry, the Journal of Productivity Analysis, and Economics Letters. He was a co-editor of Pacific Economic Review and is currently associate editor of Empirical Economics and the Journal of Productivity Analysis.

    Alan P. Horncastle, Oxera Consulting, Oxford
    Alan P. Horncastle has been a professional economist for more than twenty years and leads Oxera's work on performance assessment. He has provided efficiency advice for companies and regulatory authorities in the energy, transport, water, financial services, and communications sectors across Europe for business planning, transactions, regulatory reviews, Competition Commission cases, and court hearings. He has published papers in the Journal of the Operational Research Society, the Journal of Regulatory Economics, the Competition Law Journal, and Utilities Policy and has contributed chapters to Liberalization of the Postal and Delivery Sector and Emerging Issues in Competition, Collusion and Regulation of Network Industries.

Sign In

Please sign in to access your account


Not already registered? Create an account now. ×

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner Please see the permission section of the catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close

Are you sure you want to delete your account?

This cannot be undone.


Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

Please fill in the required fields in your feedback submission.