Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists' Predictions

Joyce Jacobsen (), Laurence Levin and Zachary Tausanovitch
Additional contact information
Laurence Levin: VISA
Zachary Tausanovitch: Network for Teaching Entrepreneurship

No 2014-003, Wesleyan Economics Working Papers from Wesleyan University, Department of Economics

Abstract: Economists’ wariness of data mining may be misplaced, even in cases where economic theory provides a well-specified model for estimation. We discuss how new data mining/ensemble modeling software, for example the program TreeNet, can be used to create predictive models. We then show how for a standard labor economics problem, the estimation of wage equations, TreeNet outperforms standard OLS regression in terms of lower prediction error. Ensemble modeling also resists the tendency to overfit data. We conclude by considering additional types of economic problems that are well-suited to use of data mining techniques.

Keywords: data mining; ensemble modeling (search for similar items in EconPapers)
JEL-codes: C14 C51 J31 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2014-12
New Economics Papers: this item is included in nep-ecm and nep-for
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Related works:
Journal Article: Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists’ Predictions (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:wes:weswpa:2014-003

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