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A constrained state space approach for estimating firm efficiency

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  • Kutlu, Levent

Abstract

In this study a panel data state space approach is used to estimate efficiencies of productive units where the states are required to lie in a closed convex constraint set. The constraints are imposed by using oblique projections of state variables onto the constraint set. This approach provides a flexible approximation that can capture the time-varying pattern of firm specific efficiency.

Suggested Citation

  • Kutlu, Levent, 2017. "A constrained state space approach for estimating firm efficiency," Economics Letters, Elsevier, vol. 152(C), pages 54-56.
  • Handle: RePEc:eee:ecolet:v:152:y:2017:i:c:p:54-56
    DOI: 10.1016/j.econlet.2017.01.005
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    References listed on IDEAS

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    1. Feng, Qu & Horrace, William C., 2012. "Estimating technical efficiency in micro panels," Economics Letters, Elsevier, vol. 117(3), pages 730-733.
    2. Yunmi Kim & Chang‐Jin Kim, 2011. "Dealing with endogeneity in a time‐varying parameter model: joint estimation and two‐step estimation procedures," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 487-497, October.
    3. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676.
    4. Donald W. K. Andrews, 2000. "Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space," Econometrica, Econometric Society, vol. 68(2), pages 399-406, March.
    5. Evangelia Desli & Subhash Ray & Subal Kumbhakar, 2003. "A dynamic stochastic frontier production model with time-varying efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 10(10), pages 623-626.
    6. Allen N. Berger & Timothy H. Hannan, 1998. "The Efficiency Cost Of Market Power In The Banking Industry: A Test Of The "Quiet Life" And Related Hypotheses," The Review of Economics and Statistics, MIT Press, vol. 80(3), pages 454-465, August.
    7. Tran, Kien C. & Tsionas, Efthymios G., 2013. "GMM estimation of stochastic frontier model with endogenous regressors," Economics Letters, Elsevier, vol. 118(1), pages 233-236.
    8. 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.
    9. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    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. Kneip, Alois & Sickles, Robin C. & Song, Wonho, 2012. "A New Panel Data Treatment For Heterogeneity In Time Trends," Econometric Theory, Cambridge University Press, vol. 28(03), pages 590-628, June.
    12. 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.
    13. Kutlu, Levent, 2010. "Battese-coelli estimator with endogenous regressors," Economics Letters, Elsevier, vol. 109(2), pages 79-81, November.
    14. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    15. 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.
    16. Qu Feng & William C. Horrace, 2012. "Alternative technical efficiency measures: Skew, bias and scale," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 253-268, March.
    17. Kutlu, Levent & Sickles, Robin C., 2012. "Estimation of market power in the presence of firm level inefficiencies," Journal of Econometrics, Elsevier, vol. 168(1), pages 141-155.
    18. Seung Ahn & Robin Sickles, 2000. "Estimation of long-run inefficiency levels: a dynamic frontier approach," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 461-492.
    19. Meryem Duygun & Levent Kutlu & Robin C. Sickles, 2016. "Measuring productivity and efficiency: a Kalman filter approach," Journal of Productivity Analysis, Springer, vol. 46(2), pages 155-167, December.
    20. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    21. 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.
    22. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633, May.
    23. Parmeter, Christopher F. & Kumbhakar, Subal C., 2014. "Efficiency Analysis: A Primer on Recent Advances," Foundations and Trends(R) in Econometrics, now publishers, vol. 7(3-4), pages 191-385, December.
    24. Kutlu, Levent, 2012. "US banking efficiency, 1984–1995," Economics Letters, Elsevier, vol. 117(1), pages 53-56.
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    1. repec:kap:jproda:v:49:y:2018:i:2:d:10.1007_s11123-018-0527-9 is not listed on IDEAS
    2. repec:eee:ecolet:v:163:y:2018:i:c:p:152-154 is not listed on IDEAS
    3. repec:eee:ecolet:v:163:y:2018:i:c:p:155-157 is not listed on IDEAS

    More about this item

    Keywords

    Kalman filter; Panel data; Stochastic frontier;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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