Reference : How to generate regularly behaved production data? A Monte Carlo experimentation on D...
Scientific journals : Article
Business & economic sciences : Economic systems & public economics
How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement
Santin, Daniel [ > > ]
Perelman, Sergio mailto [Université de Liège - ULiège > HEC-Ecole de gestion : UER > Economie publique appliquée >]
European Journal of Operational Research
Elsevier Science
Yes (verified by ORBi)
The Netherlands
[en] Parametric distance function ; DEA ; Technical efficiency ; Scale efficiency ; Monte Carlo experiments
[en] Monte Carlo experimentation is a well-known approach used to test the performance of alternative
methodologies under different hypotheses. In the frontier analysis framework, whatever the parametric
or non-parametric methods tested, experiments to date have been developed assuming single output
multi-input production functions. The data generated have mostly assumed a Cobb–Douglas technology.
Among other drawbacks, this simple framework does not allow the evaluation of DEA performance on
scale efficiency measurement. The aim of this paper is twofold. On the one hand, we show how reliable
two-output two-input production data can be generated using a parametric output distance function
approach. A variable returns to scale translog technology satisfying regularity conditions is used for this
purpose. On the other hand, we evaluate the accuracy of DEA technical and scale efficiency measurement
when sample size and output ratios vary. Our Monte Carlo experiment shows that the correlation
between true and estimated scale efficiency is dramatically low when DEA analysis is performed with
small samples and wide output ratio variations.
Centre de Recherche en Économie Publique et de la Population - C.R.E.P.P

File(s) associated to this reference

Fulltext file(s):

Restricted access
87. EJOR Perelman-Santin 199 (2009) 303-310.pdfPublisher postprint233.13 kBRequest copy

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.