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

Author

Listed:
  • Joyce P Jacobsen

    (Economics Department, Public Affairs Center, Wesleyan University, 238 Church Street, Middletown, CT 06459, USA.)

  • Laurence M Levin

    (VISA Inc., 800 Metro Center Boulevard, Foster City, CA 94404)

  • Zachary Tausanovitch

    (Network for Teaching Entrepreneurship, 120 Wall Street, 18th floor, NY 10005, New York)

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 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, 2016. "Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists’ Predictions," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 42(3), pages 387-398, June.
    • Handle: RePEc:pal:easeco:v:42:y:2016:i:3:p:387-398
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    References listed on IDEAS

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    More about this item

    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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