Quantile regression for robust bank efficiency score estimation

Andreas Behr

European Journal of Operational Research, 2010, vol. 200, issue 2, pages 568-581

Abstract: 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.

Keywords: Efficiency; Quantile; regression; Banking (search for similar items in EconPapers)
Date: 2010

Downloads: (external link)
http://www.sciencedirect.com/science/article/B6VCT ... 4d76f9bfc73218657d69
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:eee:ejores:v:200:y:2010:i:2:p:568-581

Access Statistics for this article

European Journal of Operational Research is edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Series data maintained by Heidi Boesdal ().