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

Author

Listed:
  • Joyce P. Jacobsen

    (Department of Economics, Wesleyan University)

  • Laurence M. Levin

    (VISA)

  • Zachary Tausanovitch

    (Network for Teaching Entrepreneurship)

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.

Suggested Citation

  • Joyce P. Jacobsen & Laurence M. Levin & Zachary Tausanovitch, 2014. "Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists' Predictions," Wesleyan Economics Working Papers 2014-003, Wesleyan University, Department of Economics.
    • Handle: RePEc:wes:weswpa:2014-003
    as

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    References listed on IDEAS

    as
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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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