Arbeitspapier

Macroeconomic uncertainty through the lens of professional forecasters

We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of different economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a realtime subjective measure of uncertainty in a simple framework. We jointly model and estimate macroeconomic (common) and indicator-specific uncertainties of four indicators, using a factor stochastic volatility model. Our macroeconomic uncertainty has three major spikes, aligned with the 1973-75, 1980, and 2007-09 recessions, while other recessions were characterized by increases in indicator-specific uncertainties. We also demonstrate for the first time in the literature that the selection of data vintages substantially affects the relative size of jumps in estimated uncertainty series. Finally, our macroeconomic uncertainty has a persistent negative impact on real economic activity, rather than producing "wait-and-see" dynamics.

Sprache
Englisch

Erschienen in
Series: Bank of Canada Staff Working Paper ; No. 2016-5

Klassifikation
Wirtschaft
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
General Aggregative Models: Forecasting and Simulation: Models and Applications
Business Fluctuations; Cycles
Thema
Business fluctuations and cycles
Econometric and statistical methods

Ereignis
Geistige Schöpfung
(wer)
Jo, Soojin
Sekkel, Rodrigo
Ereignis
Veröffentlichung
(wer)
Bank of Canada
(wo)
Ottawa
(wann)
2016

DOI
doi:10.34989/swp-2016-5
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Jo, Soojin
  • Sekkel, Rodrigo
  • Bank of Canada

Entstanden

  • 2016

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