IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/0234.html
   My bibliography  Save this article

Analysis of dependence between the random components of a stochastic production function for the purpose of technical efficiency estimation

Author

Listed:
  • Aivazian, Sergei

    (CEMI RAS, Moscow, Russia)

  • Afanasiev, Mikhail

    (CEMI RAS, Moscow, Russia)

  • Rudenko, Victoria

    (Moscow Engineering Physics Institute (National Research Nuclear University), Russia)

Abstract

In elaboration of the stochastic frontier methodology we offer an approach to test a statistic hypothesis about independence of random components of a stochastic production function for the purpose of estimation of technical efficiency. We describe the dependence between the error components by a copula. For parameters estimation in the econometric model in case of dependent error components the analytical expressions for log-likelihood function and its derivatives are given. The results of an experimental hypothesis test based on simulated data with dependent error components are also provided. We use two approaches for the parameters estimation: statistical package Stata 10.0 under an assumption of independence of the error components and created in MS Excel macro which gives the possibility to analyze models with dependent error components. It is shown that using non-tested assumption of independence of the random components of a stochastic production function may lead to wrong results in estimation of the technical efficiency.

Suggested Citation

  • Aivazian, Sergei & Afanasiev, Mikhail & Rudenko, Victoria, 2014. "Analysis of dependence between the random components of a stochastic production function for the purpose of technical efficiency estimation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 34(2), pages 3-18.
  • Handle: RePEc:ris:apltrx:0234
    as

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2014_2_03-18.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hung-pin Lai & Cliff Huang, 2013. "Maximum likelihood estimation of seemingly unrelated stochastic frontier regressions," Journal of Productivity Analysis, Springer, vol. 40(1), pages 1-14, August.
    2. Murray D. Smith, 2008. "Stochastic frontier models with dependent error components," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 172-192, March.
    3. Rosenberg, Joshua V. & Schuermann, Til, 2006. "A general approach to integrated risk management with skewed, fat-tailed risks," Journal of Financial Economics, Elsevier, vol. 79(3), pages 569-614, March.
    4. Christian Genest & Bruno Rémillard, 2004. "Test of independence and randomness based on the empirical copula process," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 335-369, December.
    5. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    6. Christine Amsler & Artem Prokhorov & Peter Schmidt, 2014. "Using Copulas to Model Time Dependence in Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 497-522, August.
    7. Penikas, Henry & Simakova, Varvara, 2009. "Interest Rate Risk Management Based on Copula-GARCH Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 3-36.
    8. Aigner, D J & Amemiya, Takeshi & Poirier, Dale J, 1976. "On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 17(2), pages 377-396, June.
    9. Blagoveschensky, Yury, 2012. "Basics of copula’s theory," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 26(2), pages 113-130.
    10. Carta, Alessandro & Steel, Mark F.J., 2012. "Modelling multi-output stochastic frontiers using copulas," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3757-3773.
    11. Aivazian, Sergei & Afanasiev, Mikhail & Rudenko, Victoria, 2012. "Some specification aspects for three-factor models of a company's production potential taking into account intellectual capital," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 36-69.
    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. Tai-Hsin Huang & Nan-Hung Liu & Subal C. Kumbhakar, 2018. "Joint estimation of the Lerner index and cost efficiency using copula methods," Empirical Economics, Springer, vol. 54(2), pages 799-822, March.
    2. Huang, Tai-Hsin & Chen, Kuan-Chen & Lin, Chung-I, 2018. "An extension from network DEA to copula-based network SFA: Evidence from the U.S. commercial banks in 2009," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 51-62.
    3. Graziella Bonanno & Domenico De Giovanni & Filippo Domma, 2017. "The ‘wrong skewness’ problem: a re-specification of stochastic frontiers," Journal of Productivity Analysis, Springer, vol. 47(1), pages 49-64, February.
    4. Huang, Tai-Hsin & Lin, Chung-I & Chen, Kuan-Chen, 2017. "Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies," Pacific-Basin Finance Journal, Elsevier, vol. 41(C), pages 93-110.
    5. Huang, Tai-Hsin & Hu, Chu-Nan & Chang, Bao-Guang, 2018. "Competition, efficiency, and innovation in Taiwan’s banking industry — An application of copula methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 362-375.
    6. Christine Amsler & Peter Schmidt & Wen-Jen Tsay, 2019. "Evaluating the CDF of the distribution of the stochastic frontier composed error," Journal of Productivity Analysis, Springer, vol. 52(1), pages 29-35, December.
    7. Huang, Tai-Hsin & Chiang, Dien-Lin & Chao, Shih-Wei, 2017. "A new approach to jointly estimating the Lerner index and cost efficiency for multi-output banks under a stochastic meta-frontier framework," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 212-226.
    8. Randrianarisoa, Laingo Manitra & Bolduc, Denis & Choo, Yap Yin & Oum, Tae Hoon & Yan, Jia, 2015. "Effects of corruption on efficiency of the European airports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 79(C), pages 65-83.
    9. Hung-pin Lai & Cliff Huang, 2013. "Maximum likelihood estimation of seemingly unrelated stochastic frontier regressions," Journal of Productivity Analysis, Springer, vol. 40(1), pages 1-14, August.
    10. Петрова Екатерина Александровна, 2014. "Оценка Риска Остаточной Стоимости Секьюритизированного Пула Активов Оперативного Лизинга," Вестник Финансового университета, CyberLeninka;Федеральное государственное образовательное бюджетное учреждение высшего профессионального образования «Финансовый университет при Правительстве Российской Федерации» (Финансовый университет), issue 3, pages 127-138.
    11. Marijn Verschelde & Michel Dumont & Glenn Rayp & Bruno Merlevede, 2016. "Semiparametric stochastic metafrontier efficiency of European manufacturing firms," Journal of Productivity Analysis, Springer, vol. 45(1), pages 53-69, February.
    12. El Mehdi, Rachida & Hafner, Christian M., 2014. "Inference in stochastic frontier analysis with dependent error terms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 102(C), pages 104-116.
    13. Emilio Gómez-Déniz & Jorge Pérez-Rodríguez, 2015. "Closed-form solution for a bivariate distribution in stochastic frontier models with dependent errors," Journal of Productivity Analysis, Springer, vol. 43(2), pages 215-223, April.
    14. Penikas, Henry, 2014. "Investment portfolio risk modelling based on hierarchical copulas," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 18-38.
    15. Carta, Alessandro & Steel, Mark F.J., 2012. "Modelling multi-output stochastic frontiers using copulas," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3757-3773.
    16. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
    17. Huang, Tai-Hsin & Lin, Chung-I & Wu, Ruei-Cian, 2019. "Assessing the marketing and investment efficiency of Taiwan’s life insurance firms under network structures," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 132-147.
    18. Ajuruchukwu Obi & Balogun Taofeek Ayodeji, 2020. "Determinants of Economic Farm-Size–Efficiency Relationship in Smallholder Maize Farms in the Eastern Cape Province of South Africa," Agriculture, MDPI, Open Access Journal, vol. 10(4), pages 1-18, April.
    19. Cullinane, Kevin & Song, Dong-Wook, 2006. "Estimating the Relative Efficiency of European Container Ports: A Stochastic Frontier Analysis," Research in Transportation Economics, Elsevier, vol. 16(1), pages 85-115, January.
    20. Cullinane, Kevin & Song, Dong-Wook & Gray, Richard, 2002. "A stochastic frontier model of the efficiency of major container terminals in Asia: assessing the influence of administrative and ownership structures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(8), pages 743-762, October.

    More about this item

    Keywords

    econometric model; production potential; production factors; intellectual capital; copula; normal copula; dependence of error components.;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

    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:ris:apltrx:0234. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    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: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.