IDEAS home Printed from https://ideas.repec.org/p/zbw/vfsc15/113111.html
   My bibliography  Save this paper

Tests Of Non-Causality In A Frequency Band

Author

Listed:
  • Schreiber, Sven
  • Breitung, Jörg

Abstract

We extend the frequency-specific Granger-causality test of Breitung and Candelon (2006) to a more general null hypothesis that allows non-causality at unknown frequencies within an interval, instead of having to prespecify a single frequency. This setup corresponds better to most hypotheses that are typically analyzed in applied research and is easy to implement. We also discuss a test approach that departs from strict non-causality, given the impossibility of (non-trivial) non-causality over a continuum of frequencies. 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.

Suggested Citation

  • Schreiber, Sven & Breitung, Jörg, 2015. "Tests Of Non-Causality In A Frequency Band," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113111, Verein für Socialpolitik / German Economic Association.
    • Handle: RePEc:zbw:vfsc15:113111
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/113111/1/VfS_2015_pid_903.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    2. Katrin Assenmacher-Wesche & Stefan Gerlach, 2007. "Money at Low Frequencies," Journal of the European Economic Association, MIT Press, vol. 5(2-3), pages 534-542, 04-05.
    3. 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.
    4. Lemmens, Aurélie & Croux, Christophe & Dekimpe, Marnik G., 2008. "Measuring and testing Granger causality over the spectrum: An application to European production expectation surveys," International Journal of Forecasting, Elsevier, vol. 24(3), pages 414-431.
    5. 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.
    6. Shiller, Robert J, 1979. "The Volatility of Long-Term Interest Rates and Expectations Models of the Term Structure," Journal of Political Economy, University of Chicago Press, vol. 87(6), pages 1190-1219, December.
    7. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Breitung, Jörg & Schreiber, Sven, 2018. "Assessing causality and delay within a frequency band," Econometrics and Statistics, Elsevier, vol. 6(C), pages 57-73.
    2. Tiwari, Aviral Kumar, 2012. "An empirical investigation of causality between producers' price and consumers' price indices in Australia in frequency domain," Economic Modelling, Elsevier, vol. 29(5), pages 1571-1578.
    3. repec:ipg:wpaper:2014-441 is not listed on IDEAS
    4. Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah & Sjö, Bo, 2016. "On the time scale behavior of equity-commodity links: Implications for portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 30-46.
    5. Ritabrata Bose & Ashima Goyal, 2020. "Disaggregated Indian industrial cycles: A Spectral analysis," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2020-033, Indira Gandhi Institute of Development Research, Mumbai, India.
    6. Tiwari, Aviral Kumar & Jena, Sangram Keshari & Mitra, Amarnath & Yoon, Seong-Min, 2018. "Impact of oil price risk on sectoral equity markets: Implications on portfolio management," Energy Economics, Elsevier, vol. 72(C), pages 120-134.
    7. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    8. Wang, Lu & Ma, Feng & Niu, Tianjiao & Liang, Chao, 2021. "The importance of extreme shock: Examining the effect of investor sentiment on the crude oil futures market," Energy Economics, Elsevier, vol. 99(C).
    9. Tsangyao Chang & Omid Ranjbar & Charl Jooste, 2017. "Stock Market Interactions between the BRICS and the United States: Evidence from Asymmetric Granger Causality Tests in the Frequency Domain," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 21(2), pages 297-320, Spring.
    10. Claudiu Tiberiu Albulescu & Cornel Oros & Aviral Kumar Tiwari, 2017. "Oil price–inflation pass-through in Romania during the inflation targeting regime," Applied Economics, Taylor & Francis Journals, vol. 49(15), pages 1527-1542, March.
    11. Joseph, Anto & Sisodia, Garima & Tiwari, Aviral Kumar, 2014. "A frequency domain causality investigation between futures and spot prices of Indian commodity markets," Economic Modelling, Elsevier, vol. 40(C), pages 250-258.
    12. 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.
    13. Aviral Kumar Tiwari, 2012. "Tax Burden and GDP: Evidence from Frequency Doman Approach for the USA," Economics Bulletin, AccessEcon, vol. 32(1), pages 147-159.
    14. Assenmacher-Wesche, Katrin & Gerlach, Stefan & Sekine, Toshitaka, 2008. "Monetary factors and inflation in Japan," Journal of the Japanese and International Economies, Elsevier, vol. 22(3), pages 343-363, September.
    15. Bozoklu, Seref & Yilanci, Veli, 2013. "Energy consumption and economic growth for selected OECD countries: Further evidence from the Granger causality test in the frequency domain," Energy Policy, Elsevier, vol. 63(C), pages 877-881.
    16. Bekiros, Stelios & Boubaker, Sabri & Nguyen, Duc Khuong & Uddin, Gazi Salah, 2017. "Black swan events and safe havens: The role of gold in globally integrated emerging markets," Journal of International Money and Finance, Elsevier, vol. 73(PB), pages 317-334.
    17. Sharma, Gagan Deep & Tiwari, Aviral Kumar & Talan, Gaurav & Jain, Mansi, 2021. "Revisiting the sustainable versus conventional investment dilemma in COVID-19 times," Energy Policy, Elsevier, vol. 156(C).
    18. Bouoiyour, Jamal & Selmi, Refk & Tiwari, Aviral, 2014. "Is Bitcoin business income or speculative bubble? Unconditional vs. conditional frequency domain analysis," MPRA Paper 59595, University Library of Munich, Germany.
    19. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    20. repec:zbw:bofitp:2019_023 is not listed on IDEAS
    21. 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.
    22. Schreiber, Sven, 2013. "(When) does money growth help to predict Euro-area inflation at low frequencies?," Discussion Papers 2013/10, Free University Berlin, School of Business & Economics.

    More about this item

    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
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:vfsc15:113111. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vfsocea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.