A general methodology for bootstrapping in non-parametric frontier models
Leopold Simar () and
Paul Wilson ()
Journal of Applied Statistics, 2000, vol. 27, issue 6, 779-802
The Data Envelopment Analysis method has been extensively used in the literature to provide measures of firms' technical efficiency. These measures allow rankings of firms by their apparent performance. The underlying frontier model is non-parametric since no particular functional form is assumed for the frontier model. Since the observations result from some data-generating process, the statistical properties of the estimated efficiency measures are essential for their interpretations. In the general multi-output multi-input framework, the bootstrap seems to offer the only means of inferring these properties (i.e. to estimate the bias and variance, and to construct confidence intervals). This paper proposes a general methodology for bootstrapping in frontier models, extending the more restrictive method proposed in Simar & Wilson (1998) by allowing for heterogeneity in the structure of efficiency. A numerical illustration with real data is provided to illustrate the methodology.
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (259) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
Working Paper: A General Methodology for Bootstrapping in Nonparametric Frontier Models (1998)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:27:y:2000:i:6:p:779-802
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Applied Statistics is currently edited by Robert Aykroyd
More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().