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

Author

Listed:
  • 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.)

  • Kris Boudt

    (Solvay Business School, Vrije Universiteit Brussel; Department of Economics, Ghent University; School of Business and Economics, Vrije Universiteit Amsterdam)

  • Brecht Verbeken

    (Faculty of Social Sciences and Solvay Business School.)

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.

Suggested Citation

  • Andres Algaba & Samuel Borms & Kris Boudt & Brecht Verbeken, 2021. "Daily news sentiment and monthly surveys: A mixed–frequency dynamic factor model for nowcasting consumer confidence," Working Paper Research 396, National Bank of Belgium.
    • Handle: RePEc:nbb:reswpp:202102-396
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    File URL: https://www.nbb.be/fr/articles/daily-news-sentiment-and-monthly-surveys-mixed-frequency-dynamic-factor-model-nowcasting
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    References listed on IDEAS

    as
    1. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
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    Cited by:

    1. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    2. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
    3. Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2024. "Forecasting GDP in Europe with textual data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 338-355, March.
    4. Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021. "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series 2616, European Central Bank.
    5. An, Yimeng & Dang, Yaoguo & Wang, Junjie & Zhou, Huimin & Mai, Son T., 2024. "Mixed-frequency data Sampling Grey system Model: Forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators," Applied Energy, Elsevier, vol. 370(C).
    6. Guobin Fang & Xuehua Zhou, 2024. "Web Semantic Analysis of Investor Sentiment, Short Trading, and Stock Market Volatility," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-35, January.
    7. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2023. "Testing big data in a big crisis: Nowcasting under Covid-19," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1548-1563.

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    More about this item

    Keywords

    dynamic factor model; mixed-frequency; nowcasting; sentiment index; Sentometrics; state space;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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