This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Efficiency in Public Sector: A Neural Network Approach

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Francisco J. Delgado

Additional information is available for the following registered author(s):

Abstract

Here artificial neural networks (ANNs) are employed for efficiency purposes. First, the main features of ANNs are presented. Then, common techniques of the efficiency literature are reviewed: parametric (deterministic and stochastic) and non-parametric (Data Envelopment Analysis [DEA] and Free Disposal Hull [FDH]). ANNs are proposed for frontier approximation. Their advantages and drawbacks in the efficiency context are examined. Finally, these various methodologies are applied to refuse collection services using a sample of Spanish (Catalonian) municipalities. The results are compared with Pearson´s correlation and Spearman rank-correlation coefficients

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help file. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.uniovi.es/economia/prof/Economia/FrancisoJoseDelgadoRivero/Delgado%20FJ%20-%20Efficiency%20ANNs%20-%20CEF2004.pdf
File Format: application/pdf
File Function: main text
Download Restriction: no

Publisher Info
Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 81.

Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Length:
Date of creation: 11 Aug 2004
Date of revision:
Handle: RePEc:sce:scecf4:81

Contact details of provider:
Email:
Web page: http://comp-econ.org/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords: Neural Networks Efficiency DEA

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July. [Downloadable!] (restricted)
Full references

Statistics
Access and download statistics

Did you know? You can use convenient plug-ins to search directly IDEAS from your browser.

This page was last updated on 2008-8-5.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.