IDEAS home Printed from https://ideas.repec.org/a/vrs/eaiada/v22y2018i2p74-88n6.html
   My bibliography  Save this article

Clustering Macroeconomic Time Series

Author

Listed:
  • Augustyński Iwo

    (Wrocław University of Economics, Wrocław, Poland)

  • Laskoś-Grabowski Paweł

    (University of Wrocław, Institute of Theoretical Physics, Wrocław, Poland)

Abstract

The data mining technique of time series clustering is well established. However, even when recognized as an unsupervised learning method, it does require making several design decisions that are nontrivially influenced by the nature of the data involved. By extensively testing various possibilities, we arrive at a choice of a dissimilarity measure (compression-based dissimilarity measure, or CDM) which is particularly suitable for clustering macroeconomic variables. We check that the results are stable in time and reflect large-scale phenomena, such as crises. We also successfully apply our findings to the analysis of national economies, specifically to identifying their structural relations.

Suggested Citation

  • Augustyński Iwo & Laskoś-Grabowski Paweł, 2018. "Clustering Macroeconomic Time Series," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(2), pages 74-88, June.
    • Handle: RePEc:vrs:eaiada:v:22:y:2018:i:2:p:74-88:n:6
    DOI: 10.15611/eada.2018.2.06
    as

    Download full text from publisher

    File URL: https://doi.org/10.15611/eada.2018.2.06
    Download Restriction: no
    File URL: https://libkey.io/10.15611/eada.2018.2.06?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
    ---><---

    References listed on IDEAS

    as
    1. Papageorgiou, Theofanis & Michaelides, Panayotis G. & Milios, John G., 2010. "Business cycles synchronization and clustering in Europe (1960-2009)," Journal of Economics and Business, Elsevier, vol. 62(5), pages 419-470, September.
    2. Jakob De Haan & Robert Inklaar & Richard Jong‐A‐Pin, 2008. "Will Business Cycles In The Euro Area Converge? A Critical Survey Of Empirical Research," Journal of Economic Surveys, Wiley Blackwell, vol. 22(2), pages 234-273, April.
    3. Ansgar Belke & Clemens Domnick & Daniel Gros, 2017. "Business Cycle Synchronization in the EMU: Core vs. Periphery," Open Economies Review, Springer, vol. 28(5), pages 863-892, November.
    4. Ahlborn, Markus & Wortmann, Marcus, 2018. "The core‒periphery pattern of European business cycles: A fuzzy clustering approach," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 12-27.
    5. James D. Hamilton, 2017. "Why You Should Never Use the Hodrick-Prescott Filter," NBER Working Papers 23429, National Bureau of Economic Research, Inc.
    6. Christophe Croux & Mario Forni & Lucrezia Reichlin, 2001. "A Measure Of Comovement For Economic Variables: Theory And Empirics," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 232-241, May.
    7. Martin Gächter & Aleksandra Riedl & Doris Ritzberger-Grünwald, 2012. "Business Cycle Synchronization in the Euro Area and the Impact of the Financial Crisis," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 33-60.
    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. Agnieszka Gehringer & Jörg König, 2021. "Recent Patterns of Economic Alignment in the European (Monetary) Union," JRFM, MDPI, vol. 14(8), pages 1-23, August.
    2. Ansgar Belke & Clemens Domnick & Daniel Gros, 2017. "Business Cycle Synchronization in the EMU: Core vs. Periphery," Open Economies Review, Springer, vol. 28(5), pages 863-892, November.
    3. Sabrina Bunyan & David Duffy & George Filis & Ishmael Tingbani, 2018. "Bilateral business cycle synchronisation in the EMU: What is the role of fiscal policy and government size?," Working Papers 2018.02, International Network for Economic Research - INFER.
    4. Mattia Guerini & Duc Thi Luu & Mauro Napoletano, 2023. "Synchronization patterns in the European Union," Applied Economics, Taylor & Francis Journals, vol. 55(18), pages 2038-2059, April.
    5. repec:hal:spmain:info:hdl:2441/5q8fnecj1u87ka099dc571bhi2 is not listed on IDEAS
    6. Stavros Degiannakis & David Duffy & George Filis, 2014. "Business Cycle Synchronization in EU: A Time-Varying Approach," Scottish Journal of Political Economy, Scottish Economic Society, vol. 61(4), pages 348-370, September.
    7. Sabrina Bunyan & David Duffy & George Filis & Ishmael Tingbani, 2020. "Fiscal policy, government size and EMU business cycle synchronization," Scottish Journal of Political Economy, Scottish Economic Society, vol. 67(2), pages 201-222, May.
    8. Jakob de Haan & Jan P.A.M. Jacobs & Renske Zijm, 2022. "Coherence of Output Gaps in the Euro Area: The Impact of the Covid-19 Shock," CESifo Working Paper Series 9654, CESifo.
    9. Rémi Odry & Roman Mestre, 2021. "Monetary Policy and Business Cycle Synchronization in Europe," Working Papers hal-04159759, HAL.
    10. Uctum Merih & Uctum Remzi & Vijverberg Chu-Ping C., 2021. "The European growth synchronization through crises and structural changes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(1), pages 1-17, February.
    11. Masato Nakao & Toichiro Asada, 2022. "Purchase of government bonds by a supranational central bank: its impact on business cycles," Evolutionary and Institutional Economics Review, Springer, vol. 19(1), pages 395-424, April.
    12. Kapounek, Svatopluk & Kučerová, Zuzana, 2019. "Historical decoupling in the EU: Evidence from time-frequency analysis," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 265-280.
    13. Augustyński, Iwo & Laskoś-Grabowski, Paweł, 2017. "Clustering Macroeconomic Time Series," EconStor Preprints 171380, ZBW - Leibniz Information Centre for Economics.
    14. Degiannakis, Stavros & Duffy, David & Filis, George, 2013. "Time-varying Business Cycles Synchronisation in Europe," MPRA Paper 52925, University Library of Munich, Germany.
    15. Lubos Hanus & Lukas Vacha, 2015. "Business cycle synchronization of the Visegrad Four and the European Union," Working Papers IES 2015/19, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jul 2015.
    16. Mariarosaria Comunale, 2017. "Synchronicity of real and financial cycles and structural characteristics in EU countries," CEIS Research Paper 414, Tor Vergata University, CEIS, revised 25 Sep 2017.
    17. Gächter, Simon & Riedl, Alesandra & Ritzberger-Grünwald, Doris, 2013. "Business cycle convergence or decoupling? Economic adjustment in CESEE during the crisis," BOFIT Discussion Papers 3/2013, Bank of Finland Institute for Emerging Economies (BOFIT).
    18. Bertrand Candelon & Jan Piplack & Stefan Straetmans, 2009. "Multivariate Business Cycle Synchronization in Small Samples," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 715-737, October.
    19. Gießler Stefan & Heinisch Katja & Holtemöller Oliver, 2021. "(Since When) Are East and West German Business Cycles Synchronised?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(1), pages 1-28, February.
    20. Sylvia Gottschalk, 2023. "From Black Wednesday to Brexit: Macroeconomic shocks and correlations of equity returns in France, Germany, Italy, Spain, and the United Kingdom," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2843-2873, July.
    21. Ahlborn, Markus & Wortmann, Marcus, 2018. "The core‒periphery pattern of European business cycles: A fuzzy clustering approach," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 12-27.

    More about this item

    Keywords

    time series clustering; similarity; cluster analysis; GDP;
    All these keywords.

    JEL classification:

    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:vrs:eaiada:v:22:y:2018:i:2:p:74-88:n:6. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.