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Confidence, animal spirits, and the macroeconomy in China: Based on mixed-frequency data models

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  • Wanbo Lu
  • Qibo Liu
  • Haofang Li

Abstract

This paper employs the mixed-frequency Granger causality test, reverse unconstrained mixed-frequency data sampling models, and Chinese data from January 2006 to June 2024 to test the nexus between consumer confidence and the macroeconomy. The results show that changes in the real estate market, GDP, and urban unemployment rate are Granger causes of consumer confidence. In reverse, consumer confidence is a Granger cause of the CPI. Second, GDP and the real estate market (CPI and urban unemployment rate) have a significant positive (negative) impact on consumer confidence, while the conditions of industrial production, interest rate, and stock market do not. Third, the “animal spirits” extracted from consumer confidence cannot lead to noticeable fluctuations in China’s macroeconomy. This suggests that the “animal spirits” will not dominate economic growth, even though they affect the macroeconomy slightly and inevitably. The results are robust after replacing the dependent variable and considering the influence of the global financial crisis and the COVID-19 pandemic.

Suggested Citation

  • Wanbo Lu & Qibo Liu & Haofang Li, 2025. "Confidence, animal spirits, and the macroeconomy in China: Based on mixed-frequency data models," PLOS ONE, Public Library of Science, vol. 20(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0332909
    DOI: 10.1371/journal.pone.0332909
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    References listed on IDEAS

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    1. Giannone, Domenico & Reichlin, Lucrezia & Simonelli, Saverio, 2009. "Nowcasting Euro Area Economic Activity in Real Time: The Role of Confidence Indicators," National Institute Economic Review, National Institute of Economic and Social Research, vol. 210, pages 90-97, October.
    2. 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.
    3. Guo, Yumei & He, Shan, 2020. "Does confidence matter for economic growth? An analysis from the perspective of policy effectiveness," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 1-19.
    4. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
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