IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v275y2019i3p1157-1164.html
   My bibliography  Save this article

Further results on estimating inefficiency effects in stochastic frontier models

Author

Listed:
  • Tsionas, Mike G.
  • Mamatzakis, Emmanuel

Abstract

Paul and Shankar [Satya Paul, Sriram Shankar, On Estimating Efficiency Effects in a Stochastic Frontier Model, European Journal of Operational Research (2018)] proposed an inefficiency effects stochastic frontier model which is easy to implement and avoids some of the difficulties of existing models. Unfortunately, the model has the restrictive feature that the ratio of the inefficiency effects of any two environmental variables remains fixed independently of the values of the variables. We modify the model so that this restriction can be avoided. Moreover, we provide a substantive extension of the model under quite general endogeneity assumptions. In turn, the model can be estimated using the Generalized Method of Moments technique allowing identification of efficiency estimates and inefficiency effects.

Suggested Citation

  • Tsionas, Mike G. & Mamatzakis, Emmanuel, 2019. "Further results on estimating inefficiency effects in stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1157-1164.
    • Handle: RePEc:eee:ejores:v:275:y:2019:i:3:p:1157-1164
    DOI: 10.1016/j.ejor.2018.12.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718310506
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.12.012?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    2. Paul, Satya & Shankar, Sriram, 2018. "On estimating efficiency effects in a stochastic frontier model," European Journal of Operational Research, Elsevier, vol. 271(2), pages 769-774.
    3. Kneip, Alois & Sickles, Robin C. & Song, Wonho, 2012. "A New Panel Data Treatment For Heterogeneity In Time Trends," Econometric Theory, Cambridge University Press, vol. 28(3), pages 590-628, June.
    4. Dominique Deprins & Léopold Simar, 1989. "Estimation de frontières déterministes avec facteurs exogénes d'inefficacité," Annals of Economics and Statistics, GENES, issue 14, pages 117-150.
    5. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
    6. repec:adr:anecst:y:1989:i:14:p:06 is not listed on IDEAS
    7. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    8. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Caroline Khan & Mike G. Tsionas, 2021. "Constraints in models of production and cost via slack-based measures," Empirical Economics, Springer, vol. 61(6), pages 3347-3374, December.
    2. Kumbhakar, Subal C. & Tsionas, Mike G., 2020. "On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model: Some new formulations and generalizations," European Journal of Operational Research, Elsevier, vol. 287(2), pages 762-775.
    3. Tsionas, Mike & Parmeter, Christopher F. & Zelenyuk, Valentin, 2023. "Bayesian Artificial Neural Networks for frontier efficiency analysis," Journal of Econometrics, Elsevier, vol. 236(2).
    4. Mamatzakis, Emmanuel & Bagntasarian, Anna, 2019. "The nexus between underlying dynamics of bank capital buffer and performance," MPRA Paper 92961, University Library of Munich, Germany.
    5. Tsionas, Mike G., 2023. "Combining data envelopment analysis and stochastic frontiers via a LASSO prior," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1158-1166.
    6. Tsionas, Mike G., 2023. "Bayesian learning in performance. Is there any?," European Journal of Operational Research, Elsevier, vol. 311(1), pages 263-282.
    7. Mike Tsionas & Christopher F. Parmeter & Valentin Zelenyuk, 2021. "Bridging the Divide? Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP082021, School of Economics, University of Queensland, Australia.
    8. N. Englezos & X. Kartala & P. Koundouri & M. Tsionas & A. Alamanos, 2023. "A Novel HydroEconomic - Econometric Approach for Integrated Transboundary Water Management Under Uncertainty," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 975-1030, April.
    9. Tsionas, Mike G., 2024. "A generalized inefficiency model with input and output dependence," European Journal of Operational Research, Elsevier, vol. 312(1), pages 315-323.
    10. Mamatzakis, Emmanuel & matousek, roman & vu, anh, 2019. "The interplay between problem loans and Japanese bank productivity," MPRA Paper 92960, University Library of Munich, Germany.
    11. Tsionas, Mike G. & Patel, Pankaj C., 2023. "Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency," International Journal of Production Economics, Elsevier, vol. 260(C).

    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. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    2. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    3. Robin C. Sickles & Jiaqi Hao & Chenjun Shang, 2014. "Panel data and productivity measurement: an analysis of Asian productivity trends," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 12(3), pages 211-231, August.
    4. Mastromarco Camilla & Laura Serlenga & Yongcheol Shin, 2013. "Globalisation and technological convergence in the EU," Journal of Productivity Analysis, Springer, vol. 40(1), pages 15-29, August.
    5. William Horrace & Seth Richards-Shubik & Ian Wright, 2015. "Expected efficiency ranks from parametric stochastic frontier models," Empirical Economics, Springer, vol. 48(2), pages 829-848, March.
    6. Bada, O. & Kneip, A. & Liebl, D. & Mensinger, T. & Gualtieri, J. & Sickles, R.C., 2022. "A wavelet method for panel models with jump discontinuities in the parameters," Journal of Econometrics, Elsevier, vol. 226(2), pages 399-422.
    7. Bada, Oualid & Kneip, Alois, 2014. "Parameter cascading for panel models with unknown number of unobserved factors: An application to the credit spread puzzle," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 95-115.
    8. Binlei Gong & Robin C. Sickles, 2020. "Non-structural and structural models in productivity analysis: study of the British Isles during the 2007–2009 financial crisis," Journal of Productivity Analysis, Springer, vol. 53(2), pages 243-263, April.
    9. Bada, Oualid & Liebl, Dominik, 2014. "phtt: Panel Data Analysis with Heterogeneous Time Trends in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i06).
    10. Meryem Duygun & Levent Kutlu & Robin C. Sickles, 2016. "Measuring productivity and efficiency: a Kalman filter approach," Journal of Productivity Analysis, Springer, vol. 46(2), pages 155-167, December.
    11. Pavlos Almanidis & Giannis Karagiannis & Robin Sickles, 2015. "Semi-nonparametric spline modifications to the Cornwell–Schmidt–Sickles estimator: an analysis of US banking productivity," Empirical Economics, Springer, vol. 48(1), pages 169-191, February.
    12. Badi H. Baltagi & Georges Bresson & Jean-Michel Etienne, 2020. "Growth Empirics: a Bayesian Semiparametric Model With Random Coefficients for a Panel of OECD Countries," Advances in Econometrics, in: Essays in Honor of Cheng Hsiao, volume 41, pages 217-253, Emerald Group Publishing Limited.
    13. Bartolucci, Francesco & Belotti, Federico & Peracchi, Franco, 2015. "Testing for time-invariant unobserved heterogeneity in generalized linear models for panel data," Journal of Econometrics, Elsevier, vol. 184(1), pages 111-123.
    14. Ding, Dong & Sickles, Robin C., 2018. "Capital Regulation, Efficiency, and Risk Taking: A Spatial Panel Analysis of U.S. Banks," Working Papers 18-004, Rice University, Department of Economics.
    15. Shasha Liu & Robin Sickles, 2021. "The agency problem revisited: a structural analysis of managerial productivity and CEO compensation in large US commercial banks," Empirical Economics, Springer, vol. 60(1), pages 391-418, January.
    16. Lina Lu, 2017. "Simultaneous Spatial Panel Data Models with Common Shocks," Supervisory Research and Analysis Working Papers RPA 17-3, Federal Reserve Bank of Boston.
    17. Anthony N. Rezitis, 2015. "Empirical Analysis of Agricultural Commodity Prices, Crude Oil Prices and US Dollar Exchange Rates using Panel Data Econometric Methods," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 851-868.
    18. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    19. Hyungsik Roger Moon & Martin Weidner, 2015. "Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects," Econometrica, Econometric Society, vol. 83(4), pages 1543-1579, July.
    20. Shi, Wei & Lee, Lung-fei, 2018. "A spatial panel data model with time varying endogenous weights matrices and common factors," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 6-34.

    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:eee:ejores:v:275:y:2019:i:3:p:1157-1164. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.