IDEAS home Printed from https://ideas.repec.org/a/kap/jproda/v43y2015i1p29-46.html
   My bibliography  Save this article

A finite sample improvement of the fixed effects estimator applied to technical inefficiency

Author

Listed:
  • Daniel Wikström

Abstract

The fixed effects (‘FE’) estimator of technical inefficiency performs poorly when N (the ’number of firms’) is large and T (the ‘number of time observations’) is small. We propose kernel estimators, which includes the FE estimator as a special case. In terms of criteria based on collective conditional ‘mean square error’, it is demonstrated that some kernel estimators are more efficient than the FE estimators of firm effects and inefficiencies in finite sample settings. Monte Carlo simulations support our theoretical findings, and we use an empirical example to show how FE estimation and kernel estimation lead to very different conclusions about technical inefficiency among Indonesian rice farmers. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Daniel Wikström, 2015. "A finite sample improvement of the fixed effects estimator applied to technical inefficiency," Journal of Productivity Analysis, Springer, vol. 43(1), pages 29-46, February.
    • Handle: RePEc:kap:jproda:v:43:y:2015:i:1:p:29-46
    DOI: 10.1007/s11123-014-0424-9
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11123-014-0424-9
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11123-014-0424-9?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. Wang, Wei Siang & Schmidt, Peter, 2009. "On the distribution of estimated technical efficiency in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 148(1), pages 36-45, January.
    2. PARK, Byeong & SIMAR, Léopold, 1992. "Efficient semiparametric estimation in stochastic frontier model," LIDAM Discussion Papers CORE 1992013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    4. Park, B. U. & Sickles, R. C. & Simar, L., 1998. "Stochastic panel frontiers: A semiparametric approach," Journal of Econometrics, Elsevier, vol. 84(2), pages 273-301, June.
    5. Leopold SIMAR & Wolfgang HAERDLE, "undated". "Iterated bootstrap with applications to frontier models," Statistic und Oekonometrie 9302, Humboldt Universitaet Berlin.
    6. Park, Byeong U. & Sickles, Robin C. & Simar, Leopold, 2003. "Semiparametric-efficient estimation of AR(1) panel data models," Journal of Econometrics, Elsevier, vol. 117(2), pages 279-309, December.
    7. Panutat Satchachai & Peter Schmidt, 2010. "Estimates of technical inefficiency in stochastic frontier models with panel data: generalized panel jackknife estimation," Journal of Productivity Analysis, Springer, vol. 34(2), pages 83-97, October.
    8. Myungsup Kim & Yangseon Kim & Peter Schmidt, 2007. "On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data," Journal of Productivity Analysis, Springer, vol. 28(3), pages 165-181, December.
    9. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    10. Nicholas Kiefer & Jeffrey Racine, 2009. "The smooth Colonel meets the Reverend," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(5), pages 521-533.
    11. Park, Byeong U. & Sickles, Robin C. & Simar, Leopold, 2007. "Semiparametric efficient estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 136(1), pages 281-301, January.
    12. Ouyang, Desheng & Li, Qi & Racine, Jeffrey S., 2009. "Nonparametric Estimation Of Regression Functions With Discrete Regressors," Econometric Theory, Cambridge University Press, vol. 25(1), pages 1-42, February.
    13. Racine, Jeffrey S., 2008. "Nonparametric Econometrics: A Primer," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(1), pages 1-88, March.
    14. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    15. Li, Qi & Racine, Jeffrey S. & Wooldridge, Jeffrey M., 2009. "Efficient Estimation of Average Treatment Effects with Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 206-223.
    16. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
    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. Inanoglu, Hulusi & Jacobs, Michael, Jr. & Liu, Junrong & Sickles, Robin, 2015. "Analyzing Bank Efficiency: Are "Too-Big-to-Fail" Banks Efficient?," Working Papers 15-016, Rice University, Department of Economics.
    2. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    3. 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.
    4. Barnabé Walheer, 2016. "Multi-Sector Nonparametric Production-Frontier Analysis of the Economic Growth and the Convergence of the European Countries," Pacific Economic Review, Wiley Blackwell, vol. 21(4), pages 498-524, October.
    5. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    6. Panutat Satchachai & Peter Schmidt, 2010. "Estimates of technical inefficiency in stochastic frontier models with panel data: generalized panel jackknife estimation," Journal of Productivity Analysis, Springer, vol. 34(2), pages 83-97, October.
    7. 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.
    8. Eduardo Fé & Richard Hofler, 2013. "Count data stochastic frontier models, with an application to the patents–R&D relationship," Journal of Productivity Analysis, Springer, vol. 39(3), pages 271-284, June.
    9. Blazek, David & Sickles, Robin C., 2010. "The impact of knowledge accumulation and geographical spillovers on productivity and efficiency: The case of U. S. shipbuilding during WWII," Economic Modelling, Elsevier, vol. 27(6), pages 1484-1497, November.
    10. Daniel Wikström, 2016. "Modified fixed effects estimation of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 46(1), pages 83-86, August.
    11. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    12. Feng, Qu & Horrace, William C., 2012. "Estimating technical efficiency in micro panels," Economics Letters, Elsevier, vol. 117(3), pages 730-733.
    13. Yangseon Kim & Peter Schmidt, 2000. "A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data," Journal of Productivity Analysis, Springer, vol. 14(2), pages 91-118, September.
    14. Park, Byeong U. & Sickles, Robin C. & Simar, Leopold, 2003. "Semiparametric-efficient estimation of AR(1) panel data models," Journal of Econometrics, Elsevier, vol. 117(2), pages 279-309, December.
    15. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    16. Peng Shi & Wei Zhang, 2011. "A copula regression model for estimating firm efficiency in the insurance industry," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2271-2287.
    17. Tomasz Czekaj & Arne Henningsen, 2013. "Panel Data Specifications in Nonparametric Kernel Regression: An Application to Production Functions," IFRO Working Paper 2013/5, University of Copenhagen, Department of Food and Resource Economics.
    18. Mengistu Assefa Wendimu & Arne Henningsen & Tomasz Gerard Czekaj, 2017. "Incentives and moral hazard: plot level productivity of factory-operated and outgrower-operated sugarcane production in Ethiopia," Agricultural Economics, International Association of Agricultural Economists, vol. 48(5), pages 549-560, September.
    19. Myungsup Kim & Yangseon Kim & Peter Schmidt, 2007. "On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data," Journal of Productivity Analysis, Springer, vol. 28(3), pages 165-181, December.
    20. Park, Byeong U. & Sickles, Robin C. & Simar, Leopold, 2007. "Semiparametric efficient estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 136(1), pages 281-301, January.

    More about this item

    Keywords

    Technical output inefficiency; Nonparametric kernel estimation; Panel data; C13; C14; C23;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    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:43:y:2015:i:1:p:29-46. 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.