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On Waves in War and Elections Wavelet Analysis of Political Time-Series

  • Luís Francisco Aguiar

    ()

    (Universidade do Minho - NIPE)

  • Pedro C. Magalhães

    ()

    (University of Lisbon, Social Sciences Institute)

  • Maria Joana Soares

    ()

    (Universidade do Minho)

Registered author(s):

    Spectral analysis and ARMA models have been the most established weapons of choice for the detection of cycles in time series data. However, such techniques are only appropriate when periodic components are time invariant. This has led some scholars to disregard the possibility of irregular cycles. We aim to contribute to further reconsideration of the possibility of cycles in political phenomena by introducing wavelet analysis. Its main contribution comes from the ability to estimate the spectral characteristics of a time series as a function of time, thus revealing how its different periodic components change over time, while preserving rigorous standards of statistical inference. We demonstrate the usefulness of wavelet analysis with two applications. The first concerns the possibility of long-cycles in wars, one of the central puzzles in the international relations sub-field. The second concerns election returns in presidential and congressional elections in the United States, where the existence of cyclical patterns has been equally controversial. Contrary to the prevailing wisdom, wavelet analysis allows the detection of transient and coexisting cycles, shedding some light over phenomena that have remained unaddressed so far.

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    File URL: http://www3.eeg.uminho.pt/economia/nipe/docs/2010/NIPE_WP_1_2010.pdf
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    Paper provided by NIPE - Universidade do Minho in its series NIPE Working Papers with number 1/2010.

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    Date of creation: 2010
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    Handle: RePEc:nip:nipewp:1/2010
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    Web page: http://www3.eeg.uminho.pt/economia/nipe/versao_inglesa/index_uk.htmEmail:


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    1. Acemoglu, Daron & Johnson, Simon H. & Robinson, James A., 2003. "The Rise of Europe: Atlantic Trade, Institutioanl Change and Economic Growth," Working papers 4269-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. Michael Gerace, 2002. "US Military Expenditures and Economic Growth: Some Evidence from Spectral Methods," Defence and Peace Economics, Taylor & Francis Journals, vol. 13(1), pages 1-11.
    3. Luís Francisco Aguiar-Conraria & Maria Joana Soares & Nuno Azevedo, 2007. "Using Wavelets to decompose time-frequency economic relations," NIPE Working Papers 17/2007, NIPE - Universidade do Minho.
    4. Patrick M. Crowley & David G. Mayes, 2008. "How fused is the euro area core?: An evaluation of growth cycle co-movement and synchronization using wavelet analysis," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,Centre for International Research on Economic Tendency Surveys, vol. 2008(1), pages 63-95.
    5. Gencay, Ramazan & Selcuk, Faruk & Whitcher, Brandon, 2005. "Multiscale systematic risk," Journal of International Money and Finance, Elsevier, vol. 24(1), pages 55-70, February.
    6. Rosecrance, Richard, 1987. "Long cycle theory and international relations," International Organization, Cambridge University Press, vol. 41(02), pages 283-301, March.
    7. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists ," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, 04.
    8. Enders, Walter & Sandler, Todd, 2000. "Is Transnational Terrorism Becoming More Threatening? A Time-Series Investigation," Staff General Research Papers 1823, Iowa State University, Department of Economics.
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