An enhanced BAM for unbounded or partially bounded CRS additive models
Jesus Pastor,
Juan Aparicio,
Javier Alcaraz,
Fernando Vidal Giménez and
Diego Pastor
Omega, 2015, vol. 56, issue C, 16-24
Abstract:
The Bounded Adjusted Measure (BAM), initially defined for the additive model, which is a variable returns to scale (VRS) model, was extended to the constant returns to scale (CRS) case [7]. The added range-bounds, which maintain unaltered the production possibility set (PPS) under VRS, showed an influential effect under CRS, reducing the corresponding PPS, as well as a negative effect, excluding some of the original CRS projections. Here we propose an enhanced extension that, by considering a different set of less restrictive bounds, eliminates the negative effect. Moreover, we customize this new extension for the family of partially bounded CRS additive models, i.e., models where at least one variable is naturally bounded from below, if it is an input, or from above, if it is an output.
Keywords: Data Envelopment Analysis; CRS additive model; Bounded CRS additive model; Partially bounded CRS additive model (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0305048315000407
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: https://EconPapers.repec.org/RePEc:eee:jomega:v:56:y:2015:i:c:p:16-24
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
DOI: 10.1016/j.omega.2015.02.009
Access Statistics for this article
Omega is currently edited by B. Lev
More articles in Omega from Elsevier
Bibliographic data for series maintained by Catherine Liu ().