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The Link Between Statistical Learning Theory and Econometrics: Applications in Economics, Finance, and Marketing

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  • Esfandiar Maasoumi
  • Marcelo Medeiros

Abstract

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.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 29 (2010)
Issue (Month): 5-6 ()
Pages: 470-475

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Handle: RePEc:taf:emetrv:v:29:y:2010:i:5-6:p:470-475

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Related research

Keywords: Bagging; Forecasting; Mixture of models; Model combination; Neural networks; Nonlinear models; Regression trees; Statistical learning; Support vector regression;

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