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Does Sunspot Numbers Cause Global Temperatures? A Reconsideration Using a Non-Parametric Causality Test

Author

Listed:
  • Hossein Hassani

    (The Statistical Research Centre, Bournemouth University, UK)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Xu Huang

    (The Statistical Research Centre, Bournemouth University, UK)

  • Mansi Ghodsi

    (The Statistical Research Centre, Bournemouth University, UK)

Abstract

This paper applies several causality tests to analyze whether sunspot numbers (used as an approximate proxy for the solar activity) cause global temperatures, using monthly data covering the time period 1880:1-2013:9. Both parametric and non-parametric causality tests are performed, which concludes standard time domain Granger causality test, the frequency domain causality test and the Singular Spectrum Analysis (SSA)-based causality test. Standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers does not cause global temperatures for both full and sub-samples (identified based on tests of structural breaks), the frequency domain causality test detects predictability for both the full-sample and the last sub-sample at short (2 to 2.6 months) and long (10.3 months and above) cycle lengths respectively. Our results highlight the importance of analyzing causality using the frequency domain test, which, unlike the time domain Granger causality test, allows us to decompose causality by different time horizons, and hence, could detect predictability at certain cycle lengths even when the time domain causality test might fail to pick up any causality. We also performed SSA-based causality test on both the monthly data of the time period 1936:3-1986:11 and 1986:12-2013:9. Significant causality relationships are detected for the time period 1936:3-1986:11 and the time range of 1986:12-2013:9. What is more, we also confirm causality relationship between global temperatures and sunspot numbers in the first subsample. SSA-based causality test shows powerful sensitiveness of detecting causality relationship that previous methods could not detect. Generally speaking, the non-parametric SSA-based causality test outperformed both time domain and frequency domain causality tests. Further, given the wide-spread discussion in the literature, that results for the full-sample causality, irrespective of whether it is in time or frequency domains, cannot be relied upon when there are structural breaks present, and one needs to draw inference regarding causality from the sub-samples, SSA-based causality test provides the most accurate results for each subsample and it can also show clear support of predictability on forecasting between tested variables.

Suggested Citation

  • Hossein Hassani & Rangan Gupta & Xu Huang & Mansi Ghodsi, 2014. "Does Sunspot Numbers Cause Global Temperatures? A Reconsideration Using a Non-Parametric Causality Test," Working Papers 201427, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201427
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    Cited by:

    1. Ren, Weijie & Li, Baisong & Han, Min, 2020. "A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    2. Huang, Xu & Hassani, Hossein & Ghodsi, Mansi & Mukherjee, Zinnia & Gupta, Rangan, 2017. "Do trend extraction approaches affect causality detection in climate change studies?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 604-624.
    3. Hossein Hassani & Xu Huang & Mansi Ghodsi, 2018. "Big Data and Causality," Annals of Data Science, Springer, vol. 5(2), pages 133-156, June.
    4. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.
    5. Kristoufek, Ladislav, 2017. "Has global warming modified the relationship between sunspot numbers and global temperatures?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 351-358.
    6. Umberto Triacca, 2025. "Testing for galactic cosmic ray warming hypothesis using the notion of block‐exogeneity," Environmetrics, John Wiley & Sons, Ltd., vol. 36(1), January.
    7. Huang, Xu & Maçaira, Paula Medina & Hassani, Hossein & Cyrino Oliveira, Fernando Luiz & Dhesi, Gurjeet, 2019. "Hydrological natural inflow and climate variables: Time and frequency causality analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 480-495.

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    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

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