The FOMC versus the Staff: Do Policymakers Add Value in Their Tales?

Ilias Filippou, James Mitchell and My T. Nguyen

No 23-20, Working Papers from Federal Reserve Bank of Cleveland

Abstract: Using close to 40 years of textual data from FOMC transcripts and the Federal Reserve staff's Greenbook/Tealbook, we extend Romer and Romer (2008) to test if the FOMC adds information relative to its staff forecasts not via its own quantitative forecasts but via its words. We use methods from natural language processing to extract from both types of document text-based forecasts that capture attentiveness to and sentiment about the macroeconomy. We test whether these text-based forecasts provide value-added in explaining the distribution of outcomes for GDP growth, the unemployment rate, and inflation. We find that FOMC tales about macroeconomic risks do add value in the tails, especially for GDP growth and the unemployment rate. For inflation, we find value-added in both FOMC point forecasts and narrative, once we extract from the text a broader set of measures of macroeconomic sentiment and risk attentiveness.

Keywords: monetary policy; sentiment; uncertainty; risk; forecast evaluation; FOMC meetings; textual analysis; machine learning; quantile regression (search for similar items in EconPapers)
JEL-codes: E17 E31 E52 E58 (search for similar items in EconPapers)
Pages: 74
Date: 2023-08-30
New Economics Papers: this item is included in nep-ban, nep-big, nep-cba, nep-cmp, nep-hpe and nep-mon
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DOI: 10.26509/frbc-wp-202320

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