Predicting Economic Growth: Which Variables Matter

Atin Basuchoudhary, James Bang and Tinni Sen
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Tinni Sen: Virginia Military Institute

Chapter Chapter 5 in Machine-learning Techniques in Economics, 2017, pp 37-56 from Springer

Abstract: 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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spbchp:978-3-319-69014-8_5

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DOI: 10.1007/978-3-319-69014-8_5

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