Expected Efficiency Ranks From Parametric Stochastic Fronteir Models

William Horrace and Seth Richards-Shubik ()

No 153, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: In the stochastic frontier model we extend the multivariate probability statements of Horrace (2005) to calculate the conditional probability that a firm is any particular efficiency rank in the sample. From this we construct the conditional expected efficiency rank for each firm. Compared to the traditional ranked efficiency point estimates, firm-level conditional expected ranks are more informative about the degree of uncertainty of the ranking. The conditional expect ranks may be useful for empiricists. A Monte Carlo study and an empirical example are provided. Key Words: Efficiency estimation, Order statistics, Multivariate inference, Multiplicity JEL No. C12, C16, C44, D24

Pages: 31 pages
Date: 2013-02
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://surface.syr.edu/cpr/394/ (application/pdf)

Related works:
Journal Article: Expected efficiency ranks from parametric stochastic frontier models (2015)
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: https://EconPapers.repec.org/RePEc:max:cprwps:153

Access Statistics for this paper

More papers in Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University 426 Eggers Hall, Syracuse, New York USA 13244-1020. Contact information at EDIRC.
Bibliographic data for series maintained by Katrina Fiacchi ().