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Predicting Economic Growth: Which Variables Matter

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 delve deeper into our findings in Chap. 4 . We highlight how machine learning algorithms can highlight variables that have little predictive value relative to others. This machine learning technology can therefore help highlight the most salient growth “theory” among many. We also notice that the most predictively salient variables affect economic growth in a way that suggest equilibrium shifts in strategic models rather than smooth neoclassical patterns. Thus, we argue that machine learning approaches can help researchers identify more appropriate theoretical modeling techniques. Last, we suggest that some variables are better policy levers than others.

Suggested Citation

  • Atin Basuchoudhary & James T. Bang & Tinni Sen, 2017. "Predicting Economic Growth: Which Variables Matter," SpringerBriefs in Economics, in: Machine-learning Techniques in Economics, chapter 0, pages 37-56, Springer.
    • Handle: RePEc:spr:spbchp:978-3-319-69014-8_5
    DOI: 10.1007/978-3-319-69014-8_5
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