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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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    File URL: http://repec.wesleyan.edu/pdf/jjacobsen/2014003_jacobsen.pdf
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    References listed on IDEAS

    as
    1. James H. Stock, 2010. "The Other Transformation in Econometric Practice: Robust Tools for Inference," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 83-94, Spring.
    2. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    3. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
    4. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
    5. repec:ags:stataj:117524 is not listed on IDEAS
    6. Matthias Schonlau, 2005. "Boosted regression (boosting): An introductory tutorial and a Stata plugin," Stata Journal, StataCorp LP, vol. 5(3), pages 330-354, September.
    7. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    data mining; ensemble modeling;

    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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