IDEAS home Printed from https://ideas.repec.org/a/kap/jproda/v61y2024i1d10.1007_s11123-023-00700-4.html
   My bibliography  Save this article

A note on functional form specification in random coefficients stochastic frontier models

Author

Listed:
  • Ioannis Skevas

    (University of Macedonia)

Abstract

This study presents a random coefficients stochastic frontier model that can accommodate the flexible translog functional form without being computationally demanding and thus time consuming to estimate. This is achieved by restricting the second-order frontier parameters to be common to all firms. For comparison, random coefficients stochastic frontier models with Cobb–Douglas, semi-translog and translog specifications with all parameters being firm-specific are estimated. The models are applied to an unbalanced panel of German dairy farms, and Bayesian techniques are used for the estimation. The results suggest that the time needed for the sampler to complete in the proposed model reduces dramatically as opposed to a translog model where all parameters are firm-specific. The elasticities exhibit some differences, depending on the choice of functional form, whilst the efficiency scores are less affected. Bayes factors suggest that the proposed model fits the data best.

Suggested Citation

  • Ioannis Skevas, 2024. "A note on functional form specification in random coefficients stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 61(1), pages 43-46, February.
    • Handle: RePEc:kap:jproda:v:61:y:2024:i:1:d:10.1007_s11123-023-00700-4
    DOI: 10.1007/s11123-023-00700-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11123-023-00700-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11123-023-00700-4?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. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    2. Ioannis Skevas, 2023. "A novel modeling framework for quantifying spatial spillovers on total factor productivity growth and its components," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(4), pages 1221-1247, August.
    3. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    4. Kalirajan, K P & Obwona, M B, 1994. "Frontier Production Function: The Stochastic Coefficients Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 56(1), pages 87-96, February.
    5. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, October.
    6. Grigorios Emvalomatis, 2012. "Productivity Growth in German Dairy Farming using a Flexible Modelling Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(1), pages 83-101, February.
    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. Ioannis Skevas, 2019. "A Hierarchical Stochastic Frontier Model for Efficiency Measurement Under Technology Heterogeneity," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 513-524, September.
    2. Jerzy Marzec & Andrzej Pisulewski, 2021. "Measurement of technical efficiency in the case of heterogeneity of technologies used between firms - Based on evidence from Polish crop farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(4), pages 152-161.
    3. Ioannis Skevas, 2023. "A novel modeling framework for quantifying spatial spillovers on total factor productivity growth and its components," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(4), pages 1221-1247, August.
    4. Ho-chuan Huang, 2004. "Estimation of Technical Inefficiencies with Heterogeneous Technologies," Journal of Productivity Analysis, Springer, vol. 21(3), pages 277-296, May.
    5. Jerzy Marzec & Andrzej Pisulewski, 2020. "Pomiar efektywności zróżnicowanych technologicznie gospodarstw rolnych w Unii Europejskiej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 111-137.
    6. Fleur Wouterse, 2016. "Can human capital variables be technology changing? An empirical test for rural households in Burkina Faso," Journal of Productivity Analysis, Springer, vol. 45(2), pages 157-172, April.
    7. Lajos Baráth & Imre Fertő & Heinrich Hockmann, 2020. "Technological Differences, Theoretical Consistency, and Technical Efficiency: The Case of Hungarian Crop-Producing Farms," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    8. Lukas Cechura & Aaron Grau & Heinrich Hockmann & Inna Levkovych & Zdenka Kroupova, 2017. "Catching Up or Falling Behind in European Agriculture: The Case of Milk Production," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(1), pages 206-227, February.
    9. Marta Arbelo-Pérez & Pilar Pérez-Gómez & Antonio Arbelo, 2023. "Profit efficiency and its determinants in the agricultural sector: A Bayesian approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(11), pages 436-445.
    10. Kellermann, Magnus A., 2015. "Total Factor Productivity Decomposition and Unobserved Heterogeneity in Stochastic Frontier Models," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 44(1), pages 1-25, April.
    11. Marta Arbelo-Pérez & Pilar Pérez-Gómez & Antonio Arbelo, . "Profit efficiency and its determinants in the agricultural sector: A Bayesian approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 0.
    12. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    13. Giannis Karagiannis & Vangelis Tzouvelekas, 2009. "Measuring technical efficiency in the stochastic varying coefficient frontier model," Agricultural Economics, International Association of Agricultural Economists, vol. 40(4), pages 389-396, July.
    14. Eric Njuki & Boris E Bravo-Ureta & Víctor E Cabrera, 2020. "Climatic effects and total factor productivity: econometric evidence for Wisconsin dairy farms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 1276-1301.
    15. Anup Kumar Bhandari & Pradip Maiti, 2007. "Efficiency of Indian Manufacturing Firms: Textile Industry as a Case Study," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 6(1), pages 71-88, April.
    16. Michée A. Lachaud & Boris E. Bravo‐Ureta, 2021. "Agricultural productivity growth in Latin America and the Caribbean: an analysis of climatic effects, catch‐up and convergence," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(1), pages 143-170, January.
    17. Juan Cabas Monje & Bouali Guesmi & Amer Ait Sidhoum & José María Gil, 2023. "Measuring technical efficiency of Spanish pig farming: Quantile stochastic frontier approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(4), pages 688-703, October.
    18. Zdenka ZAKOVA KROUPOVA & Lukas CECHURA & Michaela HAVLIKOVA & Pavlina HALOVA & Michal MALY, 2018. "Shadow prices of greenhouse gas emissions: An application to the Czech dairy production," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 64(7), pages 291-300.
    19. Martinez Cillero, Maria & Breen, James & Thorne, Fiona & Wallace, Michael & Hennessy, Thia, 2016. "Technical efficiency and technology heterogeneity of beef farms: a latent class stochastic frontier approach," 90th Annual Conference, April 4-6, 2016, Warwick University, Coventry, UK 236351, Agricultural Economics Society.
    20. Assaf, A. George & Barros, Carlos Pestana & Managi, Shunsuke, 2011. "Cost efficiency of Japanese steam power generation companies: A Bayesian comparison of random and fixed frontier models," Applied Energy, Elsevier, vol. 88(4), pages 1441-1446, April.

    More about this item

    Keywords

    Random coefficients; Stochastic frontier; Functional form; Translog;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

    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:61:y:2024:i:1:d:10.1007_s11123-023-00700-4. 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.