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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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, December.
    5. 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.
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    Full references (including those not matched with items on IDEAS)

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    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

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