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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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    References listed on IDEAS

    as
    1. Rangan Gupta & Luis A. Gil-Alana & Olaoluwa S. Yaya, 2015. "Do sunspot numbers cause global temperatures? Evidence from a frequency domain causality test," Applied Economics, Taylor & Francis Journals, vol. 47(8), pages 798-808, February.
    2. 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.
    3. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    4. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    5. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    6. Holmes, James M & Hutton, Patricia A, 1990. "On the Causal Relationship between Government Expenditures and National Income," The Review of Economics and Statistics, MIT Press, vol. 72(1), pages 87-95, February.
    7. Gil-Alana, Luis A. & Yaya, OlaOluwa S. & Shittu, Olanrewaju I., 2014. "Global temperatures and sunspot numbers. Are they related?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 42-50.
    8. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
    9. Hossein Hassani & Saeed Heravi & Anatoly Zhigljavsky, 2013. "Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 395-408, August.
    10. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    11. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    12. Hossein Hassani & Abdol S. Soofi & Anatoly Zhigljavsky, 2013. "Predicting inflation dynamics with singular spectrum analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 743-760, June.
    13. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    14. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
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    Cited by:

    1. 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.
    2. 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).
    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. 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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    More about this item

    Keywords

    Causality; SSA; frequency domain; global temperatures predictability; sunspot numbers;
    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

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