IDEAS home Printed from https://ideas.repec.org/p/mia/wpaper/2016-09.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://bus.miami.edu/_assets/files/repec/WP2016-09.pdf
    File Function: First version, 2016
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Hung-pin Lai & Cliff Huang, 2010. "Likelihood ratio tests for model selection of stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 34(1), pages 3-13, August.
    3. Kneip, Alois & Simar, Léopold & Van Keilegom, Ingrid, 2015. "Frontier estimation in the presence of measurement error with unknown variance," Journal of Econometrics, Elsevier, vol. 184(2), pages 379-393.
    4. Seunghwa Rho & Peter Schmidt, 2015. "Are all firms inefficient?," Journal of Productivity Analysis, Springer, vol. 43(3), pages 327-349, June.
    5. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    6. Wan, Alan T.K. & Zhang, Xinyu & Wang, Shouyang, 2014. "Frequentist model averaging for multinomial and ordered logit models," International Journal of Forecasting, Elsevier, vol. 30(1), pages 118-128.
    7. Li, Qi, 1996. "Estimating a stochastic production frontier when the adjusted error is symmetric," Economics Letters, Elsevier, vol. 52(3), pages 221-228, September.
    8. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    9. Rafik Baccouche & Mokhtar Kouki, 2003. "Stochastic Production Frontier and Technical Inefficiency: A Sensitivity Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 22(1), pages 79-91, February.
    10. Kumbhakar, Subal C. & Parmeter, Christopher F. & Tsionas, Efthymios G., 2013. "A zero inefficiency stochastic frontier model," Journal of Econometrics, Elsevier, vol. 172(1), pages 66-76.
    11. 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.
    12. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    13. Magnus, Jan R. & Powell, Owen & Prüfer, Patricia, 2010. "A comparison of two model averaging techniques with an application to growth empirics," Journal of Econometrics, Elsevier, vol. 154(2), pages 139-153, February.
    14. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    15. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    16. Yang Y., 2001. "Adaptive Regression by Mixing," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 574-588, June.
    17. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
    18. Cliff Huang & Hung-pin Lai, 2012. "Estimation of stochastic frontier models based on multimodel inference," Journal of Productivity Analysis, Springer, vol. 38(3), pages 273-284, December.
    19. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
    20. Antonio Alvarez & Christine Amsler & Luis Orea & Peter Schmidt, 2006. "Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics," Journal of Productivity Analysis, Springer, vol. 25(3), pages 201-212, June.
    21. Hansen, Bruce E. & Racine, Jeffrey S., 2012. "Jackknife model averaging," Journal of Econometrics, Elsevier, vol. 167(1), pages 38-46.
    22. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    23. Liang, Hua & Zou, Guohua & Wan, Alan T. K. & Zhang, Xinyu, 2011. "Optimal Weight Choice for Frequentist Model Average Estimators," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1053-1066.
    24. Olesen, O.B. & Ruggiero, J., 2018. "An improved Afriat–Diewert–Parkan nonparametric production function estimator," European Journal of Operational Research, Elsevier, vol. 264(3), pages 1172-1188.
    25. Mike G. Tsionas, 2017. "“When, Where, and How” of Efficiency Estimation: Improved Procedures for Stochastic Frontier Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 948-965, July.
    26. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    27. Sickles, Robin C., 2005. "Panel estimators and the identification of firm-specific efficiency levels in parametric, semiparametric and nonparametric settings," Journal of Econometrics, Elsevier, vol. 126(2), pages 305-334, June.
    28. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
    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. 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.
    2. Xu Guo & Gao-Rong Li & Michael McAleer & Wing-Keung Wong, 2018. "Specification Testing of Production in a Stochastic Frontier Model," Sustainability, MDPI, Open Access Journal, vol. 10(9), pages 1-10, August.
    3. 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.
    4. Tsionas, Mike G. & Assaf, A. George & Andrikopoulos, Athanasios, 2020. "Quantile stochastic frontier models with endogeneity," Economics Letters, Elsevier, vol. 188(C).
    5. 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.
    6. 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".

    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. Paul, Satya & Shankar, Sriram, 2018. "Modelling Efficiency Effects in a True Fixed Effects Stochastic Frontier," MPRA Paper 87437, University Library of Munich, Germany.
    2. Satya Paul & Sriram Shankar, 2020. "Estimating efficiency effects in a panel data stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 53(2), pages 163-180, April.
    3. Aman Ullah & Alan T. K. Wan & Huansha Wang & Xinyu Zhang & Guohua Zou, 2017. "A semiparametric generalized ridge estimator and link with model averaging," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 370-384, March.
    4. 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.
    5. Farsi, Mehdi & Filippini, Massimo, 2009. "An analysis of cost efficiency in Swiss multi-utilities," Energy Economics, Elsevier, vol. 31(2), pages 306-315, March.
    6. Christopher F. Parmeter, 2018. "Estimation of the two-tiered stochastic frontier model with the scaling property," Journal of Productivity Analysis, Springer, vol. 49(1), pages 37-47, February.
    7. Christopher F. Parmeter & Valentin Zelenyuk, 2019. "Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis," Operations Research, INFORMS, vol. 67(6), pages 1628-1658, November.
    8. Alecos Papadopoulos, 2021. "Stochastic frontier models using the Generalized Exponential distribution," Journal of Productivity Analysis, Springer, vol. 55(1), pages 15-29, February.
    9. Meryem Duygun & Jiaqi Hao & Anders Isaksson & Robin C. Sickles, 2017. "World Productivity Growth: A Model Averaging Approach," Pacific Economic Review, Wiley Blackwell, vol. 22(4), pages 587-619, October.
    10. Taining Wang & Jinjing Tian & Feng Yao, 2021. "Does high debt ratio influence Chinese firms’ performance? A semiparametric stochastic frontier approach with zero inefficiency," Empirical Economics, Springer, vol. 61(2), pages 587-636, August.
    11. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2017. "Stochastic Frontier Analysis: Foundations and Advances," Working Papers 2017-10, University of Miami, Department of Economics.
    12. Hakan Güneş & Dilem Yıldırım, 2016. "Estimating Cost Efficiency of Turkish Commercial Banks under Unobserved Heterogeneity with Stochastic Frontier Models," ERC Working Papers 1603, ERC - Economic Research Center, Middle East Technical University, revised Mar 2016.
    13. Liu, Chu-An, 2015. "Distribution theory of the least squares averaging estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
    14. 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.
    15. Wei, Yuting & Wang, Qihua, 2021. "Cross-validation-based model averaging in linear models with response missing at random," Statistics & Probability Letters, Elsevier, vol. 171(C).
    16. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    17. Zeebari, Zangin & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2021. "Regularized Conditional Estimators of Unit Inefficiency in Stochastic Frontier Analysis, with Application to Electricity Distribution Market," Ratio Working Papers 345, The Ratio Institute.
    18. Schomaker, Michael & Heumann, Christian, 2014. "Model selection and model averaging after multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 758-770.
    19. Jan R. Magnus & Wendun Wang & Xinyu Zhang, 2016. "Weighted-Average Least Squares Prediction," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1040-1074, June.
    20. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:mia:wpaper:2016-09. 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: https://edirc.repec.org/data/demiaus.html .

    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: Christopher Parmeter (email available below). General contact details of provider: https://edirc.repec.org/data/demiaus.html .

    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.