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Can Consumer Sentiment and Its Components Forecast Australian GDP and Consumption?

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  • Chew Lian Chua
  • Sarantis Tsiaplias

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

Abstract

This paper examines whether the disaggregation of consumer sentiment data into its sub-components improves the real-time capacity to forecast GDP and consumption. A Bayesian error correction approach augmented with the consumer sentiment index and permutations of the consumer sentiment sub-indexes is used to evaluate forecasting power. The forecasts are benchmarked against both composite forecasts and forecasts from standard error correction models. Using Australian data, we find that consumer sentiment data increases the accuracy of GDP and consumption forecasts, with certain components of consumer sentiment consistently providing better forecasts than aggregate consumer sentiment data.

Suggested Citation

  • Chew Lian Chua & Sarantis Tsiaplias, 2008. "Can Consumer Sentiment and Its Components Forecast Australian GDP and Consumption?," Melbourne Institute Working Paper Series wp2008n03, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    • Handle: RePEc:iae:iaewps:wp2008n03
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    Cited by:

    1. Javier Rojo-Suárez & Ana Belén Alonso-Conde, 2020. "Impact of consumer confidence on the expected returns of the Tokyo Stock Exchange: A comparative analysis of consumption and production-based asset pricing models," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.
    2. Atsuo Utaka, 2014. "Consumer Confidence and the Japanese Economy -Comparison of Pre- and Post-Bubble Period-," Economics Bulletin, AccessEcon, vol. 34(2), pages 1165-1173.
    3. Lenka Mynaříková & Vít Pošta, 2023. "The Effect of Consumer Confidence and Subjective Well-being on Consumers’ Spending Behavior," Journal of Happiness Studies, Springer, vol. 24(2), pages 429-453, February.
    4. John Khumalo, 2014. "Consumer Spending and Consumer Confidence in South Africa: Cointegration Analysis," Journal of Economics and Behavioral Studies, AMH International, vol. 6(2), pages 95-104.
    5. Guay C. Lim & Chew Lian Chua & Edda Claus & Sarantis Tsiaplias, 2009. "Review of the Australian Economy 2008–09: Recessions, Retrenchments and Risks," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 42(1), pages 1-11, March.
    6. Botha, Ferdi & Nguyen, Viet H., 2022. "Opposite nonlinear effects of unemployment and sentiment on male and female suicide rates: Evidence from Australia," Social Science & Medicine, Elsevier, vol. 292(C).
    7. Bahram Adrangi & Joseph Macri, 2011. "Consumer Confidence and Aggregate Consumption Expenditures in the United States," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 1-18, February.
    8. Chew Lian Chua & Sarantis Tsiaplias & Ruining Zhou, 2024. "Constructing a high‐frequency World Economic Gauge using a mixed‐frequency dynamic factor model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2212-2227, September.
    9. Nguyen, Viet Hoang & Claus, Edda, 2013. "Good news, bad news, consumer sentiment and consumption behavior," Journal of Economic Psychology, Elsevier, vol. 39(C), pages 426-438.

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    More about this item

    Keywords

    Bayesian; Composite forecast; Consumer sentiment; Cointegration.;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • 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
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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