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

Autor:	Andres Algaba, Kris Boudt, Samuel Borms, Brecht Verbeken
Přispěvatelé:	Business technology and Operations, Business, Faculty of Economic and Social Sciences and Solvay Business School
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Business and Economics sentiment index C51 sentometrics mixed-frequency ddc:330 state space dynamic factor model nowcasting Business and International Management C53 C32 C55
Zdroj:	INTERNATIONAL JOURNAL OF FORECASTING
ISSN:	0169-2070 1872-8200
DOI:	10.1016/j.ijforecast.2021.11.005
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::616c0e521282b0dae9ce9604c86edab5 https://doi.org/10.1016/j.ijforecast.2021.11.005 Zobrazit plný text záznamu Full Text from ScienceDirect