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Quantile regression for robust bank efficiency score estimation


  • Behr, Andreas


We discuss quantile regression techniques as a robust and easy to implement alternative for estimating Farell technical efficiency scores. The quantile regression approach estimates the production process for benchmark banks located at top conditional quantiles. Monte Carlo simulations reveal that even when generating data according to the assumptions of the stochastic frontier model (SFA), efficiency estimates obtained from quantile regressions resemble SFA-efficiency estimates. We apply the SFA and the quantile regression approach to German bank data for three banking groups, commercial banks, savings banks and cooperative banks to estimate efficiency scores based on a simple value added function and a multiple-input-multiple-output cost function. The results reveal that the efficient (benchmark) banks have production and cost elasticities which differ considerably from elasticities obtained from conditional mean functions and stochastic frontier functions.

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  • Behr, Andreas, 2010. "Quantile regression for robust bank efficiency score estimation," European Journal of Operational Research, Elsevier, vol. 200(2), pages 568-581, January.
  • Handle: RePEc:eee:ejores:v:200:y:2010:i:2:p:568-581

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    References listed on IDEAS

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    Cited by:

    1. Aiello, Francesco & Bonanno, Graziella, 2014. "On the Sources of Heterogeneity in Banking Efficiency Literature," MPRA Paper 58591, University Library of Munich, Germany.
    2. Francesco Aiello & Graziella Bonanno, 2016. "Efficiency in banking: a meta-regression analysis," International Review of Applied Economics, Taylor & Francis Journals, vol. 30(1), pages 112-149, January.
    3. Koutsomanoli-Filippaki, Anastasia I. & Mamatzakis, Emmanuel C., 2011. "Efficiency under quantile regression: What is the relationship with risk in the EU banking industry?," Review of Financial Economics, Elsevier, vol. 20(2), pages 84-95, May.
    4. Per J. Agrell & Mehdi Farsi & Massimo Filippini & Martin Koller, 2013. "Unobserved heterogeneous effects in the cost efficiency analysis of electricity distribution systems," CER-ETH Economics working paper series 13/171, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    5. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, Center for Economic and Financial Research (CEFIR).
    6. repec:eee:empfin:v:42:y:2017:i:c:p:66-89 is not listed on IDEAS
    7. Galina Besstremyannaya, 2015. "Heterogeneous effect of residency matching and prospective payment on labor returns and hospital scale economies," Discussion Papers 15-001, Stanford Institute for Economic Policy Research.
    8. Wang, Yongqiao & Wang, Shouyang & Dang, Chuangyin & Ge, Wenxiu, 2014. "Nonparametric quantile frontier estimation under shape restriction," European Journal of Operational Research, Elsevier, vol. 232(3), pages 671-678.
    9. Tsionas, Mike G., 2017. "Microfoundations for stochastic frontiers," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1165-1170.
    10. Mamatzakis, E & Koutsomanoli-Filippaki, Anastasia & Pasiouras, Fotios, 2012. "A quantile regression approach to bank efficiency measurement," MPRA Paper 51879, University Library of Munich, Germany.
    11. Menegaki, Angeliki N., 2013. "Accounting for unobserved management in renewable energy & growth," Energy, Elsevier, vol. 63(C), pages 345-355.
    12. Allen, D.E. & Powell, R.J. & Singh, A.K., 2016. "Take it to the limit: Innovative CVaR applications to extreme credit risk measurement," European Journal of Operational Research, Elsevier, vol. 249(2), pages 465-475.


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