Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System
AbstractGreece is a low-productivity economy with an ineffective welfare state, relying almost exclusively on low wages and social transfers. Failure to come to terms with this reality hampers both the appropriateness of EU recommendations and the Greek government's capacity to deal with unemployment. Rather than finding a job in a family business or through relationship contacts, young people stay unemployed. Nor can people move back to their village of origin so easily. The underground economy, and the mass of small companies which characterize the Greek economy are booming, on paper. One in three members of the workforce are "self-employed", compared to one in seven in the EU as a whole. (International Viewpoint) An unemployed person in Greece is 2,15 times more likely to suffer poverty than a person in employment. Yet in Greece there are perhaps even more influential factors in determining increased risk of poverty. Thus while unemployment is a crucial factor in the risk of poverty, it is neither the only nor the most significant factor. The paper presents a new technique in the field of unemployment modeling in order to forecast unemployment index. Techniques from the Artificial Neural Networks and from fuzzy logic have been combined to generate a neuro-fuzzy model. The input is a time series. Classical statistics measures are calculated in order to asses the model performance. Further the results are compared with an ARMA and an AR model.
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 InfoPaper provided by University of Crete, Department of Economics in its series Working Papers with number 0823.
Length: 8 pages
Date of creation: 24 Sep 2008
Date of revision:
forecasting; neural network; unemployment;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-09-29 (All new papers)
- NEP-FOR-2008-09-29 (Forecasting)
- NEP-LAB-2008-09-29 (Labour Economics)
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: (Kostis Pigounakis).
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.