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Assessing Causality and Delay within a Frequency Band

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  • Jörg Breitung
  • Sven Schreiber

Abstract

We extend the frequency-specific Granger-causality test of Breitung et al. (2006) to a more general null hypothesis that allows causality testing at unknown frequencies within a prespecified range of frequencies. This setup corresponds better to empirical situations encountered in applied research and it is easily implemented in vector autoregressive models. We also provide tools for estimating the phase shift/delay at some prespecified frequency or frequency band. In an empirical application dealing with the dynamics of US temperatures and CO2 emissions we find that emissions cause temperature changes only at very low frequencies with more than 30 years of oscillation. Furthermore we analyze the indicator properties of new orders for German industrial production by assessing the delay at the frequencies of interest.

Suggested Citation

  • Jörg Breitung & Sven Schreiber, 2016. "Assessing Causality and Delay within a Frequency Band," IMK Working Paper 165-2016, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    • Handle: RePEc:imk:wpaper:165-2016
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    References listed on IDEAS

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    11. 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.
    12. Assenmacher-Wesche, Katrin & Gerlach, Stefan, 2008. "Money growth, output gaps and inflation at low and high frequency: Spectral estimates for Switzerland," Journal of Economic Dynamics and Control, Elsevier, vol. 32(2), pages 411-435, February.
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    Cited by:

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    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. Theologos Dergiades & Panos K. Pouliasis, 2023. "Should stock returns predictability be ‘hooked on’ long‐horizon regressions?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 718-732, January.
    4. Strohsal, Till & Proaño, Christian R. & Wolters, Jürgen, 2019. "Characterizing the financial cycle: Evidence from a frequency domain analysis," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 568-591.
    5. Jair N. Ojeda-Joya & Oscar Jaulin-Mendez & Juan C. Bustos-Peláez, 2019. "The Interdependence Between Commodity-Price and GDP Cycles: A Frequency-Domain Approach," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 47(3), pages 275-292, September.
    6. Daouda Coulibaly & Fulgence Zran Goueu, 2019. "An Empirical Analysis of the Link between Economic Growth and Exports in Côte d’Ivoire," International Business Research, Canadian Center of Science and Education, vol. 12(9), pages 94-104, September.
    7. 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.
    8. Bernd Süssmuth, 2022. "The mutual predictability of Bitcoin and web search dynamics," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 435-454, April.
    9. Strohsal, Till & Proaño Acosta, Christian & Wolters, Jürgen, 2015. "Characterizing the financial cycle: Evidence from a frequency domain analysis," SFB 649 Discussion Papers 2015-021, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. 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).
    11. Süssmuth, Bernd, 2019. "Bitcoin and Web Search Query Dynamics: Is the price driving the hype or is the hype driving the price?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203566, Verein für Socialpolitik / German Economic Association.

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    More about this item

    Keywords

    Granger causality; frequency domain; filter gain;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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