Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence

Arbeitspapier

Policymakers, firms, and investors closely monitor traditional survey-based consumer confidence indicators and treat it as an important piece of economic information. We propose a latent factor model for the vector of monthly survey-based consumer confidence and daily sentiment embedded in economic media news articles. The proposed mixed-frequency dynamic factor model framework uses a novel covariance matrix specification. Model estimation and real-time filtering of the latent consumer confidence index are computationally simple. In a Monte Carlo simulation study and an empirical application concerning Belgian consumer confidence, we document the economically significant accuracy gains obtained by including daily news sentiment in the dynamic factor model for nowcasting consumer confidence.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: NBB Working Paper ; No. 396

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation ; Forecasting Models; Simulation Methods ; Large Data Sets: Modeling and Analysis
• Thema: dynamic factor model ; mixed-frequency ; nowcasting ; sentiment index ; sentometrics ; state space

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Algaba, Andres ; Borms, Samuel ; Boudt, Kris ; Verbeken, Brecht
• Ereignis: Veröffentlichung
• (wer): National Bank of Belgium
• (wo): Brussels
• (wann): 2021

Weitere Informationen

• Handle: http://hdl.handle.net/10419/238183
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ