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Using Wavelets to decompose time-frequency economic relations

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Abstract

Economic agents simultaneously operate at different horizons. Many economic processes are the result of the actions of several agents with different term objectives. Therefore, economic time-series is a combination of components operating on different frequencies. Several questions about the data are connected to the understanding of the time-series behavior at different frequencies. While Fourier analysis is not appropriate to study the cyclical nature of economic time-series, because these are rarely stationary, wavelet analysis performs the estimation of the spectral characteristics of a time-series as a function of time. In spite of all its advantages, wavelets are hardly ever used in economics. The purpose of this paper is to show that cross wavelet analysis can be used to directly study the interactions different time-series in the time-frequency domain. We use wavelets to analyze the impact of interest rate price changes on some macroeconomic variables: Industrial Production, Inflation and the monetary aggregates M1 and M2. Specifically, three tools are utilized: the wavelet power spectrum, wavelet coherency and wavelet phase-difference. These instruments illustrate how the use of wavelets may help to unravel economic time-frequency relations that would otherwise remain hidden.

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  • 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.
    • Handle: RePEc:nip:nipewp:17/2007
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    Cited by:

    1. Tsangyao Chang & Xiao-lin Li & Stephen M. Miller & Mehmet Balcilar & Rangan Gupta, 2013. "The Co-Movement and Causality between the U.S. Real Estate and Stock Markets in the Time and Frequency Domains," Working papers 2013-34, University of Connecticut, Department of Economics.
    2. Ladislav Kristoufek, 2013. "Fractal Markets Hypothesis and the Global Financial Crisis: Wavelet Power Evidence," Papers 1310.1446, arXiv.org.
    3. Luís Francisco Aguiar & Pedro C. Magalhães & Maria Joana Soares, 2010. "On Waves in War and Elections Wavelet Analysis of Political Time-Series," NIPE Working Papers 1/2010, NIPE - Universidade do Minho.
    4. Gazi Salah Uddin & Mohamed Arouri & Aviral Kumar Tiwari, 2014. "Co-movements between Germany and International Stock Markets: Some New Evidence from DCC-GARCH and Wavelet Approaches," Working Papers 2014-143, Department of Research, Ipag Business School.
    5. Buerhan Saiti & Azlan Ali & Naziruddin Abdullah & Sulaiman Sajilan, 2014. "Palm Oil Price, Exchange Rate, and Stock Market: A Wavelet Analysis on the Malaysian Market," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 2(1), pages 13-27.
    6. Rita Sousa & Luís Aguiar-Conraria & Maria Joana Soares, 2014. "Carbon Financial Markets: a time-frequency analysis of CO2 price drivers," NIPE Working Papers 03/2014, NIPE - Universidade do Minho.
    7. Muhammad Shahbaz & Aviral Kumar Tiwari & Mohammad Iqbal Tahir, 2015. "Analyzing time-frequency relationship between oil price and exchange rate in Pakistan through wavelets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 690-704, April.
    8. Gazi Salah Uddin & Aviral Kumar Tiwari, 2013. "Measuring co-movement of oil price and exchange rate differential in Bangladesh," Economics Bulletin, AccessEcon, vol. 33(3), pages 1922-1930.
    9. Tiwari, Aviral Kumar, 2012. "Decomposing Time-Frequency Relationship between Interest Rates and Share Prices in India through Wavelets," MPRA Paper 39693, University Library of Munich, Germany.
    10. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2012. "The yield curve and the macro-economy across time and frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1950-1970.
    11. Jozef BARUNÍK & Lukáš VÁCHA, 2013. "Contagion among Central and Eastern European Stock Markets during the Financial Crisis," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 443-453, November.
    12. Antonis Michis, 2011. "Multiscale Analysis of the Liquidity Effect," Working Papers 2011-5, Central Bank of Cyprus.
    13. Aviral Kumar Tiwari & Arif Billah Dar & Niyati Bhanja, 2014. "Inflation-Industrial Growth Nexus in India – A Revisit Through Continuous Wavelet Transform," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(2), pages 1-11.
    14. Yung-hsiang Ying & Koyin Chang & Ginny ju-ann Yang & Chen-hsun Lee, 2014. "Measuring co-movement of globalization and democratization in the time–frequency space," Economics Bulletin, AccessEcon, vol. 34(1), pages 206-219.
    15. Niyati Bhanja & Arif Billah Dar & Aviral Kumar Tiwari & Olaolu Richard Olayeni, 2012. "Are Stock Prices Hedge Against Inflation? A Revisit over Time and Frequencies in India," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(3), pages 199-213, September.
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    17. Aloui, Chaker & Hkiri, Besma, 2014. "Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis," Economic Modelling, Elsevier, vol. 36(C), pages 421-431.
    18. Luís Aguiar-Conraria & Maria Soares, 2011. "Oil and the macroeconomy: using wavelets to analyze old issues," Empirical Economics, Springer, vol. 40(3), pages 645-655, May.

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    More about this item

    Keywords

    Monetary policy; time-frequency analysis; non-stationary time series; wavelets; cross wavelets; wavelet coherency.;
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