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Does Sunspot Numbers Cause Global Temperatures? Evidence from a Frequency Domain Causality Test

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Luis A. Gil-Alana

    (University of Navarra, Faculty of Economics, Edificio Biblioteca, Entrada Este, E-31080 Pamplona, Spain)

  • OlaOluwa S. Yaya

    (University of Ibadan, Ibadan, Nigeria)

Abstract

This paper applies the causality test in the frequency domain, developed by Breitung and Candelon (2006), to analyze whether sunspot numbers cause global temperatures, using monthly data covering the time period 1880:1-2013:9. While, 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.

Suggested Citation

  • Rangan Gupta & Luis A. Gil-Alana & OlaOluwa S. Yaya, 2013. "Does Sunspot Numbers Cause Global Temperatures? Evidence from a Frequency Domain Causality Test," Working Papers 201382, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201382
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    as
    1. 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.
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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. Hassan, Kamrul & Hoque, Ariful & Gasbarro, Dominic & Wong, Wing-Keung, 2023. "Are Islamic stocks immune from financial crises? Evidence from contagion tests," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 919-948.
    3. José A. Pérez‐Montiel & Carles Manera, 2022. "Is autonomous demand really autonomous in the United States? An asymmetric frequency‐domain Granger causality approach," Metroeconomica, Wiley Blackwell, vol. 73(1), pages 78-92, February.
    4. Hassani, Hossein & Huang, Xu & Gupta, Rangan & Ghodsi, Mansi, 2016. "Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 54-65.
    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. Wang, Hanjie & Feil, Jan-Henning & Yu, Xiaohua, 2021. "Disagreement on sunspots and soybeans futures price," Economic Modelling, Elsevier, vol. 95(C), pages 385-393.
    7. Elie Bouri & Imad Kachacha & Donald Lien & David Roubaud, 2017. "Short- and long-run causality across the implied volatility of crude oil and agricultural commodities," Economics Bulletin, AccessEcon, vol. 37(2).
    8. Burakov, D., 2017. "Do Sunspots Matter for Cycles in Agricultural Lending: a VEC Approach to Russian Wheat Market," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 9(1), March.

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    More about this item

    Keywords

    Causality; 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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