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

B-Tier
Journal: International Journal of Forecasting
Year: 2023
Volume: 39
Issue: 1
Pages: 266-278

Authors (4)

Algaba, Andres (not in RePEc) Borms, Samuel (not in RePEc) Boudt, Kris (Universiteit Gent) Verbeken, Brecht (not in RePEc)

Score contribution per author:

0.503 = (α=2.01 / 4 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

Policymakers, firms, and investors closely monitor traditional survey-based consumer confidence indicators and treat them as an important piece of economic information. To obtain a daily nowcast of monthly consumer confidence, we introduce 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 uses a Toeplitz correlation matrix to account for the serial correlation in the high-frequency sentiment measurement errors. We find significant accuracy gains in nowcasting survey-based Belgian consumer confidence with economic media news sentiment.

Technical Details

RePEc Handle
repec:eee:intfor:v:39:y:2023:i:1:p:266-278
Journal Field
Econometrics
Author Count
4
Added to Database
2026-01-24