Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/4131
DC FieldValueLanguage
dc.contributor.authorZombanakis, George A.-
dc.contributor.authorAndreou, Andreas S.-
dc.date2011en
dc.date.accessioned2014-07-09T07:04:27Z-
dc.date.accessioned2015-12-09T11:30:26Z-
dc.date.available2014-07-09T07:04:27Z-
dc.date.available2015-12-09T11:30:26Z-
dc.date.issued2011-08-
dc.identifier1024-2694en
dc.identifier.citationDefence and Peace Economics, 2011, vol. 22, no. 4, pp. 459-469en_US
dc.identifier.issn14768267-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/4131-
dc.description.abstractThis paper looks into the Greek-Turkish arms race a decade after an earlier contribution to the issue that relied heavily on artificial neural networks. The time period between the two papers contributes to the reliability of the results derived, not just by increasing the number of observations, but also mainly by incorporating the progress made in the realm of artificial intelligence. The focus on the case of both countries unlike the paper mentioned above that dealt with just the Greek side provides ample room for comparative purposes regarding the determinants of defense expenditure on both sides. The results derived in terms of input significance estimation support the findings of the earlier research as indicated above, pointing to the leading role of the demographic preponderance of Turkey over Greece. The paper also points to the fact that 10 years later, Turkey continues to set the arms race rules against its rival by determining the defense expenditure of Greece, whereas the role of the latter in affecting the military spending of Turkey is non-existent.en_US
dc.formatpdfen_US
dc.languageenen
dc.language.isoenen_US
dc.relation.ispartofDefence and Peace Economicsen_US
dc.rights© Taylor & Francisen_US
dc.subjectArms raceen_US
dc.subjectNeural networksen_US
dc.subjectTurkeyen_US
dc.subjectGreeceen_US
dc.titleFinancial versus human resources in the Greek-Turkish arms race 10 years on: A forecasting investigation using artificial neural networksen_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationBank of Greeceen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsSubscriptionen_US
dc.reviewPeer Reviewed-
dc.countryCyprusen_US
dc.countryGreeceen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1080/10242694.2010.539858en_US
dc.dept.handle123456789/134en
dc.relation.issue4en_US
dc.relation.volume22en_US
cut.common.academicyear2011-2012en_US
dc.identifier.spage459en_US
dc.identifier.epage469en_US
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn1476-8267-
crisitem.journal.publisherTaylor & Francis-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0001-7104-2097-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Άρθρα/Articles
CORE Recommender
Show simple item record

SCOPUSTM   
Citations

6
checked on Nov 9, 2023

WEB OF SCIENCETM
Citations

6
Last Week
0
Last month
0
checked on Oct 29, 2023

Page view(s) 50

446
Last Week
1
Last month
6
checked on Dec 22, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.