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Do Trend Extraction Approaches Affect Causality Detection in Climate Change Studies?

Author

Listed:
  • Xu Huang

    (The Statistical Research Centre, Bournemouth University, UK)

  • Hossein Hassani

    (The Statistical Research Centre, Bournemouth University, UK)

  • Mansi Ghodsi

    (The Statistical Research Centre, Bournemouth University, UK)

  • Zinnia Mukherjee

    (Author-Name: Department of Economics, Simmons College, U.S.A)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria)

Abstract

Various scientific studies have investigated the causal link between solar activity (SS) and the earth’s temperature (GT). Results from literature indicate that both the detected structural breaks and existing trend have significant effects on the causality detection outcomes. In this paper, we make a contribution to this literature by evaluating and comparing seven trend extraction methods covering various aspects of trend extraction studies to date. In addition, we extend previous work by using Convergent Cross Mapping (CCM) - an advanced nonparametric causality detection technique to provide evidence on the effect of existing trend in global temperature on the causality detection outcome. This paper illustrates the use of a method to find the most reliable trend extraction approach for data preprocessing, as well as provides detailed analyses of the causality detection of each component by this approach to achieve a better understanding of the causal link between SS and GT. Furthermore, the corresponding CCM results indicate increasing significance of causal effect from SS to GT since 1880 to recent years, which provide solid evidences that may contribute on explaining the escalating global tendency of warming up recent decades.

Suggested Citation

  • Xu Huang & Hossein Hassani & Mansi Ghodsi & Zinnia Mukherjee & Rangan Gupta, 2016. "Do Trend Extraction Approaches Affect Causality Detection in Climate Change Studies?," Working Papers 201660, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201660
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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. Hossein Hassani & Jan Coreman & Saeed Heravi & Joshy Easaw, 2018. "Forecasting Inflation Rate: Professional Against Academic, Which One is More Accurate," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(3), pages 631-646, September.
    3. Silva, Emmanuel Sirimal & Ghodsi, Zara & Ghodsi, Mansi & Heravi, Saeed & Hassani, Hossein, 2017. "Cross country relations in European tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 151-168.
    4. 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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