• Media type: E-Book
  • Title: Forecasting Macroeconomic Tail Risk in Real Time : Do Textual Data Add Value?
  • Contributor: Adämmer, Philipp [Author]; Prüser, Jan [Author]; Schüssler, Rainer Alexander [Author]
  • Published: [S.l.]: SSRN, 2023
  • Extent: 1 Online-Ressource (29 p)
  • Language: English
  • DOI: 10.2139/ssrn.4372186
  • Identifier:
  • Keywords: Quantile Regression ; Textual data ; Topic models ; Quantile Regression Forests ; Gaussian process ; Global-local priors
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments February 27, 2023 erstellt
  • Description: We examine the incremental value of news-based data relative to the FRED-MD economic indicators for quantile predictions (now- and forecasts) of employment, output, inflation and consumer sentiment. Our results suggest that news data contain valuable information not captured by economic indicators, particularly for left-tail forecasts. Methods that capture quantile-specific non-linearities produce superior forecasts relative to methods that feature linear predictive relationships. However, adding news-based data substantially increases the performance of quantile-specific linear models, especially in the left tail. Variable importance analyses reveal that left tail predictions are determined by both economic and textual indicators, with the latter having the most pronounced impact on consumer sentiment
  • Access State: Open Access