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

Author

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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.

Suggested Citation

  • Esfandiar Maasoumi & Marcelo Medeiros, 2010. "The Link Between Statistical Learning Theory and Econometrics: Applications in Economics, Finance, and Marketing," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 470-475.
    • Handle: RePEc:taf:emetrv:v:29:y:2010:i:5-6:p:470-475
    DOI: 10.1080/07474938.2010.481544
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    Cited by:

    1. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.

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