Global approximations to cost and production functions using artificial neural networks

Tsionas, EG; Michaelides, PG; Vouldis, AT

dc.contributor.author	Tsionas, EG	en
dc.contributor.author	Michaelides, PG	en
dc.contributor.author	Vouldis, AT	en
dc.date.accessioned	2014-03-01T01:30:49Z
dc.date.available	2014-03-01T01:30:49Z
dc.date.issued	2009	en
dc.identifier.issn	1875-6883	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19636
dc.subject	Econometrics	en
dc.subject	Neural networks	en
dc.subject	Production and cost functions	en
dc.subject	RTS	en
dc.subject	TFP	en
dc.subject.other	Arbitrary costs	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Artificial neural networks	en
dc.subject.other	Econometrics	en
dc.subject.other	Global approximation	en
dc.subject.other	Machine learning research	en
dc.subject.other	Production function	en
dc.subject.other	Returns to scale	en
dc.subject.other	RTS	en
dc.subject.other	TFP	en
dc.subject.other	Total factor productivity	en
dc.subject.other	Backpropagation	en
dc.subject.other	Cost accounting	en
dc.subject.other	Costs	en
dc.subject.other	Economics	en
dc.subject.other	Neural networks	en
dc.subject.other	Cost functions	en
dc.title	Global approximations to cost and production functions using artificial neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.2991/ijcis.2009.2.2.4	en
heal.identifier.secondary	http://dx.doi.org/10.2991/ijcis.2009.2.2.4	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	The estimation of cost and production functions in economics relies on standard specifications which are less than satisfactory in numerous situations. However, instead of fitting the data with a pre-specified model, Artificial Neural Networks (ANNs) let the data itself serve as evidence to support the model's estimation of the underlying process. In this context, the proposed approach combines the strengths of economics, statistics and machine learning research and the paper proposes a global approximation to arbitrary cost and production functions, respectively, given by ANNs. Suggestions on implementation are proposed and empirical application relies on standard techniques. All relevant measures such as Returns to Scale (RTS) and Total Factor Productivity (TFP) may be computed routinely. Copyright: the authors.	en
heal.publisher	ATLANTIS PRESS	en
heal.journalName	International Journal of Computational Intelligence Systems	en
dc.identifier.doi	10.2991/ijcis.2009.2.2.4	en
dc.identifier.isi	ISI:000272257200005	en
dc.identifier.volume	2	en
dc.identifier.issue	2	en
dc.identifier.spage	132	en
dc.identifier.epage	139	en