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A machine learning approach to identifying different types of uncertainty

Author

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  • Saltzman, Bennett
  • Yung, Julieta

Abstract

We implement natural language processing techniques to extract uncertainty measures from Federal Reserve Beige Books between 1970 and 2018. Business and economic related uncertainty is associated with future weakness in output, higher unemployment, and elevated term premia. On the other hand, political and government uncertainty, while high during recent times, has no statistically significant impact on the economy.

Suggested Citation

  • Saltzman, Bennett & Yung, Julieta, 2018. "A machine learning approach to identifying different types of uncertainty," Economics Letters, Elsevier, vol. 171(C), pages 58-62.
  • Handle: RePEc:eee:ecolet:v:171:y:2018:i:c:p:58-62
    DOI: 10.1016/j.econlet.2018.07.003
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    References listed on IDEAS

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    More about this item

    Keywords

    Natural language processing; VAR; Federal Reserve Beige Books;

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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