A quantile-based approach for relative efficiency measurement

Paul M. Griffin and Paul H. Kvam
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Paul M. Griffin: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA, Postal: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
Paul H. Kvam: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA, Postal: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Managerial and Decision Economics, 1999, vol. 20, issue 8, 403-410

Abstract: Two popular approaches for efficiency measurement are a non-stochastic approach called data envelopment analysis (DEA) and a parametric approach called stochastic frontier analysis (SFA). Both approaches have modeling difficulty, particularly for ranking firm efficiencies. In this paper, a new parametric approach using quantile statistics is developed. The quantile statistic relies less on the stochastic model than SFA methods, and accounts for a firm's relationship to the other firms in the study by acknowledging the firm's influence on the empirical model, and its relationship, in terms of similarity of input levels, to the other firms. Copyright © 1999 John Wiley & Sons, Ltd.

Date: 1999
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Persistent link: https://EconPapers.repec.org/RePEc:wly:mgtdec:v:20:y:1999:i:8:p:403-410

DOI: 10.1002/1099-1468(199912)20:8<403::AID-MDE956>3.0.CO;2-E

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