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Model Averaging Estimators for the Stochastic Frontier Model

Author

Listed:
  • Christopher F. Parmeter

    (University of Miami)

  • Alan T. K. Wan

    (City University of Hong Kong)

  • Xinyu Zhang

    (Academy of Mathematics and Systems Science)

Abstract

Given the concern over the impact of distributional assumptions for the stochastic frontier model, the present research proposes two distinct model averaging estimators, one which can average over distinct distributions for firm specific inefficiency and another that can average over a nested class of distributions. These estimators are shown to produce optimal weights when the criterion is to uncover conditional inefficiency. We study the finite-sample performance of the model average estimator via Monte Carlo experiments and an empirical application focusing on Philippines rice farming.

Suggested Citation

  • Christopher F. Parmeter & Alan T. K. Wan & Xinyu Zhang, 2016. "Model Averaging Estimators for the Stochastic Frontier Model," Working Papers 2016-09, University of Miami, Department of Economics.
    • Handle: RePEc:mia:wpaper:2016-09
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    References listed on IDEAS

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    Cited by:

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    2. Zangin Zeebari & Kristofer Månsson & Pär Sjölander & Magnus Söderberg, 2023. "Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market," Journal of Productivity Analysis, Springer, vol. 59(1), pages 79-97, February.
    3. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    4. Xu Guo & Gao-Rong Li & Michael McAleer & Wing-Keung Wong, 2018. "Specification Testing of Production in a Stochastic Frontier Model," Sustainability, MDPI, vol. 10(9), pages 1-10, August.
    5. Kassoum Ayouba, 2023. "Spatial dependence in production frontier models," Journal of Productivity Analysis, Springer, vol. 60(1), pages 21-36, August.
    6. 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.
    7. Tsionas, Mike G. & Assaf, A. George & Andrikopoulos, Athanasios, 2020. "Quantile stochastic frontier models with endogeneity," Economics Letters, Elsevier, vol. 188(C).
    8. Quang Nguyen & Sean Pascoe & Louisa Coglan & Son Nghiem, 2021. "The sensitivity of efficiency scores to input and other choices in stochastic frontier analysis: an empirical investigation," Journal of Productivity Analysis, Springer, vol. 55(1), pages 31-40, February.
    9. E. Fusco & R. Benedetti & F. Vidoli, 2023. "Stochastic frontier estimation through parametric modelling of quantile regression coefficients," Empirical Economics, Springer, vol. 64(2), pages 869-896, February.
    10. Ali M. Oumer & Amin Mugera & Michael Burton & Atakelty Hailu, 2022. "Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia," Journal of Productivity Analysis, Springer, vol. 57(2), pages 213-241, April.
    11. Cinzia Daraio, 2017. "A framework for the Assessment of Research and its impacts," DIAG Technical Reports 2017-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

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    More about this item

    Keywords

    Jackknife; optimal; focus criterion; leave-one-out Publication Status: Under Review;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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