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

Andres Algaba (), Samuel Borms, Kris Boudt and Brecht Verbeken
Additional contact information
Andres Algaba: Faculty of Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Pleinlaan 2, 1010 Brussel, Belgium
Samuel Borms: Faculty of Social Sciences and Solvay Business School, Institute of Financial Analysis, University of Neuchâtel, Switzerland.
Brecht Verbeken: Faculty of Social Sciences and Solvay Business School.

No 396, Working Paper Research from National Bank of Belgium

Abstract: 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.

Keywords: dynamic factor model; mixed-frequency; nowcasting; sentiment index; Sentometrics; state space (search for similar items in EconPapers)
JEL-codes: C32 C51 C53 C55 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2021-02
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ore
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Citations: View citations in EconPapers (7)

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Journal Article: Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:nbb:reswpp:202102-396

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