Neural Network Principles To Classify Economic Data
AbstractThe increased globalization makes every country more and more responsible for its actions that are meant to support the price stability and the fiscal position sustainability in an unpredictable world. Decisions makers can provide the right solutions to overcome the latest global economic crisis by using methods of classifying the continuously growing amounts of digital economic data. The principles of neural networks are applied in order to classify a set of countries according to their statistical data for economic indicators provided by the European Committee. The results and performance of this classification technique is discussed in the final section of the paper.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Lucian Blaga University of Sibiu, Faculty of Economic Sciences in its journal Revista Economica.
Volume (Year): 63.4-5 (2012)
Issue (Month): 4-5 ()
Contact details of provider:
Postal: Lucian Blaga University of Sibiu, Faculty of Economic Sciences Dumbravii Avenue, No.17, postal code 550324, Sibiu, Romania
Phone: 004 0269 210375
Fax: 004 0269 210375
Web page: http://economice.ulbsibiu.ro/
More information through EDIRC
neural networks; supervised learning; data classification; economic prosperity;
Find related papers by JEL classification:
- A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Eduard Alexandru Stoica).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.