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Some Theoretical and Simulation Results on the Frequency Domain Causality Test

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  • Hiroshi Yamada
  • Wei Yanfeng

Abstract

Breitung and Candelon (2006) in Journal of Econometrics proposed a simple statistical testing procedure for the noncausality hypothesis at a given frequency. In their paper, however, they reported some theoretical results indicating that their test severely suffers from quite low power when the noncausality hypothesis is tested at a frequency close to 0 or pi. This paper examines whether or not these results indicate their procedure is useless at such frequencies.

Suggested Citation

  • Hiroshi Yamada & Wei Yanfeng, 2014. "Some Theoretical and Simulation Results on the Frequency Domain Causality Test," Econometric Reviews, Taylor & Francis Journals, vol. 33(8), pages 936-947, November.
    • Handle: RePEc:taf:emetrv:v:33:y:2014:i:8:p:936-947
    DOI: 10.1080/07474938.2013.808488
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    Citations

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    Cited by:

    1. Sangram Keshari Jena & Aviral Kumar Tiwari & Amarnath Mitra, 2019. "Put–Call Ratio Volume vs. Open Interest in Predicting Market Return: A Frequency Domain Rolling Causality Analysis," Economies, MDPI, vol. 7(1), pages 1-10, March.
    2. Candelon, Bertrand & Hasse, Jean-Baptiste, 2023. "Testing for causality between climate policies and carbon emissions reduction," Finance Research Letters, Elsevier, vol. 55(PA).
    3. Breitung, Jörg & Schreiber, Sven, 2018. "Assessing causality and delay within a frequency band," Econometrics and Statistics, Elsevier, vol. 6(C), pages 57-73.
    4. Alexandra M. Espinosa & Ignacio Díaz-Emparanza, 2023. "Assessing the Spanish immigration policy with frequency-wise causality in Hosoya’s sense," Empirical Economics, Springer, vol. 65(1), pages 111-147, July.
    5. repec:zbw:bofitp:2019_023 is not listed on IDEAS
    6. Barunik, Jozef & Krehlik, Tomas, 2016. "Measuring the frequency dynamics of financial and macroeconomic connectedness," FinMaP-Working Papers 54, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    7. Funke, Michael & Li, Xiang & Tsang, Andrew, 2019. "Monetary policy shocks and peer-to-peer lending in China," BOFIT Discussion Papers 23/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
    8. Matteo Farnè & Angela Montanari, 2022. "A Bootstrap Method to Test Granger-Causality in the Frequency Domain," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 935-966, March.
    9. Yang Hu & Yanran Hong & Kai Feng & Jikai Wang, 2023. "Evaluating the Importance of Monetary Policy Uncertainty: The Long- and Short-Term Effects and Responses," Evaluation Review, , vol. 47(2), pages 264-286, April.
    10. Zhu, Fangfei & Zhu, Yabei & Jin, Xuejun & Luo, Xingguo, 2018. "Do spillover effects between crude oil and natural gas markets disappear? Evidence from option markets," Finance Research Letters, Elsevier, vol. 24(C), pages 25-33.
    11. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    12. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2017. "The dynamic linkages between crude oil and natural gas markets," Energy Economics, Elsevier, vol. 62(C), pages 155-170.
    13. Wei, Yanfeng & Zhang, Liguo & Guo, Xiaoying & Yang, Ting, 2021. "A theoretical and simulation analysis on the power of the frequency domain causality test," Statistics & Probability Letters, Elsevier, vol. 170(C).
    14. Matteo Farn'e & Angela Montanari, 2018. "A bootstrap test to detect prominent Granger-causalities across frequencies," Papers 1803.00374, arXiv.org, revised Oct 2018.

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