IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v467y2017icp517-526.html
   My bibliography  Save this article

Transfer mutual information: A new method for measuring information transfer to the interactions of time series

Author

Listed:
  • Zhao, Xiaojun
  • Shang, Pengjian
  • Lin, Aijing

Abstract

In this paper, we propose a new method to measure the influence of a third variable on the interactions of two variables. The method called transfer mutual information (TMI) is defined by the difference between the mutual information and the partial mutual information. It is established on the assumption that if the presence or the absence of one variable does make change to the interactions of another two variables, then quantifying this change is supposed to be the influence from this variable to those two variables. Moreover, a normalized TMI and other derivatives of the TMI are introduced as well. The empirical analysis including the simulations as well as real-world applications is investigated to examine this measure and to reveal more information among variables.

Suggested Citation

  • Zhao, Xiaojun & Shang, Pengjian & Lin, Aijing, 2017. "Transfer mutual information: A new method for measuring information transfer to the interactions of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 517-526.
    • Handle: RePEc:eee:phsmap:v:467:y:2017:i:c:p:517-526
    DOI: 10.1016/j.physa.2016.10.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116307142
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2016.10.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. A. W. Phillips, 1958. "The Relation Between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861–1957," Economica, London School of Economics and Political Science, vol. 25(100), pages 283-299, November.
    2. 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.
    3. Dror Y. Kenett & Xuqing Huang & Irena Vodenska & Shlomo Havlin & H. Eugene Stanley, 2015. "Partial correlation analysis: applications for financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 569-578, April.
    4. Leonidas Sandoval Junior & Asher Mullokandov & Dror Y. Kenett, 2015. "Dependency Relations among International Stock Market Indices," JRFM, MDPI, vol. 8(2), pages 1-39, May.
    5. Dror Y Kenett & Michele Tumminello & Asaf Madi & Gitit Gur-Gershgoren & Rosario N Mantegna & Eshel Ben-Jacob, 2010. "Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-14, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dong, Keqiang & Long, Linan & Zhang, Hong & Gao, You, 2018. "The mutual information based minimum spanning tree to detect and evaluate dependencies between aero-engine gas path system variables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 248-253.
    2. Xu, Chao & Zhao, Xiaojun & Wang, Yanwen, 2022. "Causal decomposition on multiple time scales: Evidence from stock price-volume time series," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

    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. Nie, Chun-Xiao, 2020. "Correlation dynamics in the cryptocurrency market based on dimensionality reduction analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    2. Chen, Yanhua & Li, Youwei & Pantelous, Athanasios A. & Stanley, H. Eugene, 2022. "Short-run disequilibrium adjustment and long-run equilibrium in the international stock markets: A network-based approach," International Review of Financial Analysis, Elsevier, vol. 79(C).
    3. Basnarkov, Lasko & Stojkoski, Viktor & Utkovski, Zoran & Kocarev, Ljupco, 2020. "Lead–lag relationships in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    4. Naeem, Muhammad Abubakr & Chatziantoniou, Ioannis & Gabauer, David & Karim, Sitara, 2024. "Measuring the G20 stock market return transmission mechanism: Evidence from the R2 connectedness approach," International Review of Financial Analysis, Elsevier, vol. 91(C).
    5. Yong Kheng Goh & Haslifah M Hasim & Chris G Antonopoulos, 2018. "Inference of financial networks using the normalised mutual information rate," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-21, February.
    6. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    7. Yonatan Berman & Eshel Ben-Jacob & Xin Zhang & Yoash Shapira, 2016. "Analyzing the Long Term Cohesive Effect of Sector Specific Driving Forces," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-16, March.
    8. Lucas, Robert E, Jr, 1996. "Nobel Lecture: Monetary Neutrality," Journal of Political Economy, University of Chicago Press, vol. 104(4), pages 661-682, August.
    9. M. Mouchart & R. Orsi & G. Wunsch, 2020. "Causality in Econometric Modeling. From Theory to Structural Causal Modeling," Working Papers wp1143, Dipartimento Scienze Economiche, Universita' di Bologna.
    10. Robert G. King & Mark W. Watson, 1994. "The Post-War U.S. Phillips Curve: A Revisionist Econometric History," Working Papers 1994-2, Princeton University. Economics Department..
    11. Christopher L. Gilbert & Duo Qin, 2005. "The First Fifty Years of Modern Econometrics," Working Papers 544, Queen Mary University of London, School of Economics and Finance.
    12. Jung, Sean S. & Chang, Woojin, 2016. "Clustering stocks using partial correlation coefficients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 410-420.
    13. Chuang, Hongwei, 2016. "Brokers’ financial network and stock return," The North American Journal of Economics and Finance, Elsevier, vol. 36(C), pages 172-183.
    14. Rehman, Mobeen Ur & Shahzad, Syed Jawad Hussain & Ahmad, Nasir & Vo, Xuan Vinh, 2022. "Dependence dynamics of US REITs," International Review of Financial Analysis, Elsevier, vol. 81(C).
    15. Zungun Deniz & Ayvaz Guven Emine Turkan, 2015. "The Footprints Of Stagflation In Turkish Economy," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 46-60, December.
    16. Andre Amaral & Taysir E. Dyhoum & Hussein A. Abdou & Hassan M. Aljohani, 2022. "Modeling for the Relationship between Monetary Policy and GDP in the USA Using Statistical Methods," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    17. Feng Zhu, 2005. "The fragility of the Phillips curve: A bumpy ride in the frequency domain," BIS Working Papers 183, Bank for International Settlements.
    18. Tang, Chor Foon & Lean, Hooi Hooi, 2009. "New evidence from the misery index in the crime function," Economics Letters, Elsevier, vol. 102(2), pages 112-115, February.
    19. Richard Ashley & Randal J. Verbrugge, 2019. "The Intermittent Phillips Curve: Finding a Stable (But Persistence-Dependent) Phillips Curve Model Specification," Working Papers 19-09R2, Federal Reserve Bank of Cleveland, revised 14 Feb 2023.
    20. Haotian Cai & Anatoly B. Schmidt, 0. "Comparing mean–variance portfolios and equal-weight portfolios for major US equity indexes," Journal of Asset Management, Palgrave Macmillan, vol. 0, pages 1-7.

    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:eee:phsmap:v:467:y:2017:i:c:p:517-526. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.