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Predicting Recessions: What We Learn from Widening the Goalposts

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 move our focus from economic growth to trying to predict a related target—economic recessions. We continue to use the “usual suspect” growth variables to check whether these variables are better at predicting recessions. We show how prediction performance of algorithms differs widely depending on the type of prediction criteria. We can, however, identify some of the most salient predictors of recessions. These suggest that fiscal policy may generally be better at combating recessions. Moreover, these predictors have non-linear effects on the likelihood of recessions which suggests that there may be no silver bullet for combating recessions. Last, the sorts of variables that influence economic growth also influence the likelihood of a recession. This suggest that economic growth probably should not be studied separately from recessions.

Suggested Citation

  • Atin Basuchoudhary & James T. Bang & Tinni Sen, 2017. "Predicting Recessions: What We Learn from Widening the Goalposts," SpringerBriefs in Economics, in: Machine-learning Techniques in Economics, chapter 0, pages 57-73, Springer.
    • Handle: RePEc:spr:spbchp:978-3-319-69014-8_6
    DOI: 10.1007/978-3-319-69014-8_6
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