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Estimation of a panel stochastic frontier model with unobserved common shocks

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  • Hsu, Chih-Chiang
  • Lin, Chang-Ching
  • Yin, Shou-Yung
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    Abstract

    This paper develops panel stochastic frontier models with unobserved common correlated effects. The common correlated effects provide a way of modeling cross-sectional dependence and represent heterogeneous impacts on individuals resulting from unobserved common shocks. Traditional panel stochastic frontier models do not distinguish between common correlated effects and technical inefficiency. In this paper, we propose a modified maximum likelihood estimator (MLE) that does not require estimating unobserved common correlated effects. We show that the proposed method can control the common correlated effects and obtain consistent estimates of parameters and technical efficiency for the panel stochastic frontier model. Our Monte Carlo simulations show that the modified MLE has satisfactory finite sample properties under a significant degree of cross-sectional dependence for relatively small T. The proposed method is also illustrated in applications based on a cross country comparison of the efficiency of banking industries.

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    File URL: http://mpra.ub.uni-muenchen.de/56333/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 37313.

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    Date of creation: Mar 2012
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    Handle: RePEc:pra:mprapa:37313

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    Keywords: fixed effects; common correlated effects; factor structure; cross-sectional dependence; stochastic frontier;

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    1. Robert Lensink & Aljar Meesters & Ilko Naaborg, 2008. "Bank efficiency and foreign ownership: do good institutions matter?," ULB Institutional Repository 2013/14283, ULB -- Universite Libre de Bruxelles.
    2. Kiviet, Jan F. & Phillips, Garry D. A., 1994. "Bias assessment and reduction in linear error-correction models," Journal of Econometrics, Elsevier, vol. 63(1), pages 215-243, July.
    3. Ackerberg, Daniel & Caves, Kevin & Frazer, Garth, 2006. "Structural identification of production functions," MPRA Paper 38349, University Library of Munich, Germany.
    4. Lee, Young Hoon, 2006. "A stochastic production frontier model with group-specific temporal variation in technical efficiency," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1616-1630, November.
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    7. Berger, Allen N. & Hasan, Iftekhar & Zhou, Mingming, 2009. "Bank ownership and efficiency in China: What will happen in the world's largest nation?," Journal of Banking & Finance, Elsevier, vol. 33(1), pages 113-130, January.
    8. Delis, Manthos D & Papanikolaou, Nikolaos I, 2009. "Determinants of bank efficiency: Evidence from a semi-parametric methodology," MPRA Paper 13893, University Library of Munich, Germany.
    9. 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.
    10. William Greene, 2002. "Fixed and Random Effects in Stochastic Frontier Models," Working Papers 02-16, New York University, Leonard N. Stern School of Business, Department of Economics.
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    12. Seung Ahn & Young Lee & Peter Schmidt, 2007. "Stochastic frontier models with multiple time-varying individual effects," Journal of Productivity Analysis, Springer, vol. 27(1), pages 1-12, February.
    13. Peter Schmidt & Chirok Han & Luis Orea, 2004. "Estimation of a Panel Data Model with Parametric Temporal Variation in Individual Effects," Econometric Society 2004 Far Eastern Meetings 519, Econometric Society.
    14. Allen N. Berger & Loretta J. Mester, 1997. "Inside the black box: what explains differences in the efficiencies of financial institutions?," Finance and Economics Discussion Series 1997-10, Board of Governors of the Federal Reserve System (U.S.).
    15. 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.
    16. Wang, Hung-jen & Schmidt, Peter, 2001. "One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels," MPRA Paper 31075, University Library of Munich, Germany, revised Mar 2002.
    17. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, 07.
    18. Wang, Hung-Jen & Ho, Chia-Wen, 2009. "Estimating fixed-effect panel stochastic frontier models by model transformation," MPRA Paper 31081, University Library of Munich, Germany.
    19. 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.
    20. William Greene, 2003. "Distinguishing Between Heterogeneity and Inefficiency: Stochastic Frontier Analysis of the World Health Organization’s Panel Data on National Health Care Systems," Working Papers 03-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    21. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, 07.
    22. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
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    Cited by:
    1. Massimo Filippini & Elisa Tosetti, 2014. "Stochastic Frontier Models for Long Panel Data Sets: Measurement of the Underlying Energy Efficiency for the OECD Countries," CER-ETH Economics working paper series 14/198, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.

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