The Link Between Statistical Learning Theory and Econometrics: Applications in Economics, Finance, and Marketing
Esfandiar Maasoumi () and
Marcelo Medeiros ()
Econometric Reviews, 2010, vol. 29, issue 5-6, 470-475
Statistical Learning refers to statistical aspects of automated extraction of regularities (structure) in datasets. It is a broad area which includes neural networks, regression-trees, nonparametric statistics and sieve approximation, boosting, mixtures of models, computational complexity, computational statistics, and nonlinear models in general. Although Statistical Learning Theory and Econometrics are closely related, much of the development in each of the areas is seemingly proceeding independently. This special issue brings together these two areas, and is intended to stimulate new applications and appreciation in economics, finance, and marketing. This special volume contains ten innovative articles covering a broad range of relevant topics.
Keywords: Bagging; Forecasting; Mixture of models; Model combination; Neural networks; Nonlinear models; Regression trees; Statistical learning; Support vector regression (search for similar items in EconPapers)
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:29:y:2010:i:5-6:p:470-475
Ordering information: This journal article can be ordered from
Access Statistics for this article
Econometric Reviews is currently edited by Dr. Essie Maasoumi
More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().