DOI
10.1002/1099-1468(199912)20:8<403::AID-MDE956>3.0.CO;2-E
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.
Document Type
Post-print Article
Publication Date
1999
Publisher Statement
Copyright © 1999 John Wiley & Sons, Ltd.
DOI: 10.1002/1099-1468(199912)20:8<403::AID-MDE956>3.0.CO;2-E
The definitive version is available at: https://onlinelibrary.wiley.com/doi/abs/10.1002/1099-1468%28199912%2920%3A8%3C403%3A%3AAID-MDE956%3E3.0.CO%3B2-E
Full Citation:
Griffin, Paul M., and Paul H. Kvam. "A Quantile‐based Approach for Relative Efficiency Measurement." Managerial and Decision Economics 20, no. 8 (1999): 403-10. doi:10.1002/1099-1468(199912)20:83.0.CO;2-E.
Recommended Citation
Griffin, Paul M. and Kvam, Paul H., "A quantile‐based approach for relative efficiency measurement" (1999). Department of Math & Statistics Faculty Publications. 190.
https://scholarship.richmond.edu/mathcs-faculty-publications/190