IDEAS home Printed from https://ideas.repec.org/a/kap/jproda/v34y2010i3p239-255.html
   My bibliography  Save this article

Fractional regression models for second stage DEA efficiency analyses

Author

Listed:
  • Esmeralda Ramalho
  • Joaquim Ramalho
  • Pedro Henriques

Abstract

Data envelopment analysis (DEA) is commonly used to measure the relative efficiency of decision-making units. Often, in a second stage, a regression model is estimated to relate DEA efficiency scores to exogenous factors. In this paper, we argue that the traditional linear or tobit approaches to second-stage DEA analysis do not constitute a reasonable data-generating process for DEA scores. Under the assumption that DEA scores can be treated as descriptive measures of the relative performance of units in the sample, we show that using fractional regression models are the most natural way of modeling bounded, proportional response variables such as DEA scores. We also propose generalizations of these models and, given that DEA scores take frequently the value of unity, examine the use of two-part models in this framework. Several tests suitable for assessing the specification of each alternative model are also discussed.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Esmeralda Ramalho & Joaquim Ramalho & Pedro Henriques, 2010. "Fractional regression models for second stage DEA efficiency analyses," Journal of Productivity Analysis, Springer, vol. 34(3), pages 239-255, December.
    • Handle: RePEc:kap:jproda:v:34:y:2010:i:3:p:239-255
    DOI: 10.1007/s11123-010-0184-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11123-010-0184-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11123-010-0184-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Davidson, Russell & MacKinnon, James G, 1981. "Several Tests for Model Specification in the Presence of Alternative Hypotheses," Econometrica, Econometric Society, vol. 49(3), pages 781-793, May.
    2. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, September.
    3. Kneip, Alois & Park, Byeong U. & Simar, Léopold, 1998. "A Note On The Convergence Of Nonparametric Dea Estimators For Production Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 14(6), pages 783-793, December.
    4. McDonald, John F & Moffitt, Robert A, 1980. "The Uses of Tobit Analysis," The Review of Economics and Statistics, MIT Press, vol. 62(2), pages 318-321, May.
    5. Poirier, Dale J., 1980. "A lagrange multiplier test for skewness in binary logit models," Economics Letters, Elsevier, vol. 5(2), pages 141-143.
    6. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    7. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    8. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    9. Valentin Zelenyuk & Vitaliy Zheka, 2006. "Corporate Governance and Firm’s Efficiency: The Case of a Transitional Country, Ukraine," Journal of Productivity Analysis, Springer, vol. 25(1), pages 143-157, April.
    10. Laure Latruffe & Sophia Davidova & Kelvin Balcombe, 2008. "Application of a double bootstrap to investigation of determinants of technical efficiency of farms in Central Europe," Journal of Productivity Analysis, Springer, vol. 29(2), pages 183-191, April.
    11. Daryl Pregibon, 1980. "Goodness of Link Tests for Generalized Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 15-24, March.
    12. Pagan, Adrian & Vella, Frank, 1989. "Diagnostic Tests for Models Based on Individual Data: A Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(S), pages 29-59, Supplemen.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Esmeralda A. Ramalho & Joaquim J.S. Ramalho & José M.R. Murteira, 2011. "Alternative Estimating And Testing Empirical Strategies For Fractional Regression Models," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 19-68, February.
    2. McDonald, John, 2009. "Using least squares and tobit in second stage DEA efficiency analyses," European Journal of Operational Research, Elsevier, vol. 197(2), pages 792-798, September.
    3. Glass, J. Colin & McKillop, Donal G. & Rasaratnam, Syamarlah, 2010. "Irish credit unions: Investigating performance determinants and the opportunity cost of regulatory compliance," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 67-76, January.
    4. Bernardino Benito & José Solana & María-Rocío Moreno, 2014. "Explaining efficiency in municipal services providers," Journal of Productivity Analysis, Springer, vol. 42(3), pages 225-239, December.
    5. De Witte, Kristof & Geys, Benny, 2013. "Citizen coproduction and efficient public good provision: Theory and evidence from local public libraries," European Journal of Operational Research, Elsevier, vol. 224(3), pages 592-602.
    6. Halkos, George E. & Tzeremes, Nickolaos G., 2013. "A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions," European Journal of Operational Research, Elsevier, vol. 227(1), pages 182-189.
    7. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    8. Amir Moradi-Motlagh & Ali Emrouznejad, 2022. "The origins and development of statistical approaches in non-parametric frontier models: a survey of the first two decades of scholarly literature (1998–2020)," Annals of Operations Research, Springer, vol. 318(1), pages 713-741, November.
    9. Natalya Zelenyuk & Valentin Zelenyuk, 2015. "Productivity Drivers of Efficiency in Banking: Importance of Model Specifications," CEPA Working Papers Series WP082015, School of Economics, University of Queensland, Australia.
    10. Natalya Zelenyuk & Valentin Zelenyuk, 2014. "Regional and Ownership Drivers of Bank Efficiency," CEPA Working Papers Series WP112014, School of Economics, University of Queensland, Australia.
    11. Banker, Rajiv & Natarajan, Ram & Zhang, Daqun, 2019. "Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using Data Envelopment Analysis: Second stage OLS versus bootstrap approaches," European Journal of Operational Research, Elsevier, vol. 278(2), pages 368-384.
    12. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    13. Devicienti, Francesco & Manello, Alessandro & Vannoni, Davide, 2017. "Technical efficiency, unions and decentralized labor contracts," European Journal of Operational Research, Elsevier, vol. 260(3), pages 1129-1141.
    14. Kourtesi, Sofia & De Witte, Kristof & Polymeros, Apostolos, 2016. "Technical Efficiency in the Agricultural Sector - Evidence from a Conditional Quantile-Based Approach," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 17(2), June.
    15. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "Data envelopment analysis 1978–2010: A citation-based literature survey," Omega, Elsevier, vol. 41(1), pages 3-15.
    16. Bernardino Benito & José Solana & Pilar López, 2014. "Determinants of Spanish Regions' Tourism Performance: A Two-Stage, Double-Bootstrap Data Envelopment Analysis," Tourism Economics, , vol. 20(5), pages 987-1012, October.
    17. María José Barrio-Tellado & Luis César Herrero-Prieto, 2019. "Modelling museum efficiency in producing inter-reliant outputs," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 43(3), pages 485-512, September.
    18. Gil, Guilherme Dôco Roberti & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & Mayrink, Vinícius Diniz, 2017. "Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies," Energy Economics, Elsevier, vol. 64(C), pages 373-383.
    19. Worthington, Andrew C. & Zelenyuk, Valentin, 2018. "Data envelopment analysis, truncated regression and double-bootstrap for panel data with application to Chinese bankingAuthor-Name: Du, Kai," European Journal of Operational Research, Elsevier, vol. 265(2), pages 748-764.
    20. Roland Banya & Nicholas Biekpe, 2018. "Banking efficiency and its determinants in selected frontier african markets," Economic Change and Restructuring, Springer, vol. 51(1), pages 69-95, February.

    More about this item

    Keywords

    Second-stage DEA; Fractional data; Specification tests; One outcomes; Two-part models; C12; C13; C25; C51;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    Statistics

    Access and download statistics

    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:kap:jproda:v:34:y:2010:i:3:p:239-255. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.