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Text as data: a machine learning-based approach to measuring uncertainty

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  • Rickard Nyman
  • Paul Ormerod

Abstract

The Economic Policy Uncertainty index had gained considerable traction with both academics and policy practitioners. Here, we analyse news feed data to construct a simple, general measure of uncertainty in the United States using a highly cited machine learning methodology. Over the period January 1996 through May 2020, we show that the series unequivocally Granger-causes the EPU and there is no Granger-causality in the reverse direction

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  • Rickard Nyman & Paul Ormerod, 2020. "Text as data: a machine learning-based approach to measuring uncertainty," Papers 2006.06457, arXiv.org.
    • Handle: RePEc:arx:papers:2006.06457
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    References listed on IDEAS

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    1. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    2. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
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