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Methodology

In: Machine-learning Techniques in Economics

Author

Listed:
  • Atin Basuchoudhary

    (Virginia Military Institute)

  • James T. Bang

    (St. Ambrose University)

  • Tinni Sen

    (Virginia Military Institute)

Abstract

In this chapter, we sketch the theory behind the estimation algorithms used later in the book. It serves as a primer for readers interested in theory. We then describe the techniques used to validate these algorithms. These validation techniques can help choose the most “predictive” algorithm. We specifically include this section to introduce the reader to the idea that there are multiple decision rules to choose the most predictive algorithm. Choosing the right rule depends on the user’s purpose. There is no “one size fits all” decision rule to choose the most predictive algorithm. We then describe the process through which individual variables can be rank ordered according to their predictive power. This is followed by a description of how the reader might discern the effect of any one of these variables on the target variable, i.e. growth and recessions.

Suggested Citation

  • Atin Basuchoudhary & James T. Bang & Tinni Sen, 2017. "Methodology," SpringerBriefs in Economics, in: Machine-learning Techniques in Economics, chapter 0, pages 19-28, Springer.
    • Handle: RePEc:spr:spbchp:978-3-319-69014-8_3
    DOI: 10.1007/978-3-319-69014-8_3
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    Cited by:

    1. X. B. Lam & Y. S. Kim & A. D. Hoang & C. W. Park, 2009. "Coupled Aerostructural Design Optimization Using the Kriging Model and Integrated Multiobjective Optimization Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 142(3), pages 533-556, September.

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