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Money growth and consumer price inflation in the euro area: A wavelet analysis

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  • Mandler, Martin
  • Scharnagl, Michael

Abstract

Our paper studies the relationship between money growth and consumer price inflation in the euro area using wavelet analysis. Wavelet analysis allows to account for variations in the money growth-inflation relationship both across the frequency spectrum and across time. We find evidence of strong comovements between money growth and inflation at low frequencies with money growth as the leading variable. However, our analysis of time variation at medium-to-long-run frequencies indicates a weakening of the relationship after the mid 1990s which also reflects in a deterioration of the leading indicator property and a decline in the cross wavelet gain. In contrast, most of the literature, by failing to account for the effects of time variation, estimated stable long-run relationships between money growth and inflation well into the 2000s.

Suggested Citation

  • Mandler, Martin & Scharnagl, Michael, 2014. "Money growth and consumer price inflation in the euro area: A wavelet analysis," Discussion Papers 33/2014, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdps:332014
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    References listed on IDEAS

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    1. Carstensen, Kai, 2007. "Is core money growth a good and stable inflation predictor in the euro area?," Kiel Working Papers 1318, Kiel Institute for the World Economy.
    2. Thomas J. Sargent & Paolo Surico, 2011. "Two Illustrations of the Quantity Theory of Money: Breakdowns and Revivals," American Economic Review, American Economic Association, vol. 101(1), pages 109-128, February.
    3. Amisano, Gianni & Fagan, Gabriel, 2013. "Money growth and inflation: A regime switching approach," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 118-145.
    4. Ramsey, J.B., 2002. "Wavelets in Economics and Finance: Past and Future," Working Papers 02-02, C.V. Starr Center for Applied Economics, New York University.
    5. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    6. Paul De Grauwe & Magdalena Polan, 2005. "Is Inflation Always and Everywhere a Monetary Phenomenon?," Scandinavian Journal of Economics, Wiley Blackwell, vol. 107(2), pages 239-259, June.
    7. Hofmann, Boris, 2006. "Do monetary indicators (still) predict euro area inflation?," Discussion Paper Series 1: Economic Studies 2006,18, Deutsche Bundesbank.
    8. Pedro Teles & Harald Uhlig & João Valle e Azevedo, 2016. "Is Quantity Theory Still Alive?," Economic Journal, Royal Economic Society, vol. 126(591), pages 442-464, March.
    9. Dewald, William G. & Haug, Alfred A., 2004. "Longer-term effects of monetary growth on real and nominal variables, major industrial countries, 1880-2001," Working Paper Series 382, European Central Bank.
    10. Ramsey James B., 2002. "Wavelets in Economics and Finance: Past and Future," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-29, November.
    11. Patrick Crowley, 2005. "An intuitive guide to wavelets for economists," Econometrics 0503017, University Library of Munich, Germany.
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    Cited by:

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    2. Fratianni, Michele & Gallegati, Marco & Giri, Federico, 2022. "The medium-run Phillips curve: A time–frequency investigation for the UK," Journal of Macroeconomics, Elsevier, vol. 73(C).
    3. Luís Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, 2019. "The Phillips Curve at 60: time for time and frequency," NIPE Working Papers 04/2019, NIPE - Universidade do Minho.
    4. Xie, Henglang & Bui, Wency Kher Thinng, 2024. "Impact of globalization and energy consumption on CO2 emissions in China: Implications for energy transition," Finance Research Letters, Elsevier, vol. 67(PB).
    5. Alexander Jung, 2018. "Have money and credit data releases helped markets to predict the interest rate decisions of the European Central Bank?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(1), pages 39-67, February.
    6. Hassan Khodavaisi, 2025. "Output, Money and Interest Rate in the United States: New Evidence Based on Wavelet Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 1571-1601, August.
    7. Yaqi Wu & Chen Zhang & Po Yun & Dandan Zhu & Wei Cao & Zulfiqar Ali Wagan, 2021. "Time–frequency analysis of the interaction mechanism between European carbon and crude oil markets," Energy & Environment, , vol. 32(7), pages 1331-1357, November.
    8. Flor, Michael A. & Klarl, Torben, 2017. "On the cyclicity of regional house prices: New evidence for U.S. metropolitan statistical areas," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 134-156.
    9. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2023. "The Phillips curve at 65: Time for time and frequency," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    10. Jung, Alexander, 2016. "Have monetary data releases helped markets to predict the interest rate decisions of the European Central Bank?," Working Paper Series 1926, European Central Bank.
    11. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2020. "Okun’s Law across time and frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    12. Gallegati, Marco & Giri, Federico & Fratianni, Michele, 2019. "Money growth and inflation: International historical evidence on high inflation episodes for developed countries," Bank of Finland Research Discussion Papers 1/2019, Bank of Finland.
    13. Amine Ben Amar & Jean‐Étienne Carlotti, 2021. "Who drives the dance? Further insights from a time‐frequency wavelet analysis of the interrelationship between stock markets and uncertainty," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1623-1636, January.
    14. Rünstler, Gerhard & Balfoussia, Hiona & Burlon, Lorenzo & Buss, Ginters & Comunale, Mariarosaria & De Backer, Bruno & Dewachter, Hans & Guarda, Paolo & Haavio, Markus & Hindrayanto, Irma & Iskrev, Nik, 2018. "Real and financial cycles in EU countries - Stylised facts and modelling implications," Occasional Paper Series 205, European Central Bank.
    15. Aguiar-Conraria, Luis & Martins, Manuel M.F. & Soares, Maria Joana, 2018. "Estimating the Taylor rule in the time-frequency domain," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 122-137.
    16. Ramin Khochiani & Younes Nademi, 2020. "Energy consumption, CO2 emissions, and economic growth in the United States, China, and India: A wavelet coherence approach," Energy & Environment, , vol. 31(5), pages 886-902, August.
    17. Scharnagl, Michael & Mandler, Martin, 2015. "The relationship of simple sum and Divisia monetary aggregates with real GDP and inflation: a wavelet analysis for the US," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112879, Verein für Socialpolitik / German Economic Association.
    18. Funashima Yoshito, 2021. "Time–Frequency Regression," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 21-32, January.
    19. Ha, Le Thanh, 2024. "Dynamic spill-over influences of FinTech innovation development on renewable energy volatility during the time of war in pandemic: A novel insight from a wavelet model," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 515-529.
    20. Luís Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, 2019. "The Phillips Curve at 60: time for time and frequency," CEF.UP Working Papers 1902, Universidade do Porto, Faculdade de Economia do Porto.
    21. Antony, Jürgen & Klarl, Torben, 2020. "Estimating the income inequality-health relationship for the United States between 1941 and 2015: Will the relevant frequencies please stand up?," The Journal of the Economics of Ageing, Elsevier, vol. 17(C).
    22. Andreani, Michele & Giri, Federico, 2024. "Mortgages, house prices, and business cycle dynamic: A medium-run exploration using the continuous wavelet transform," International Review of Economics & Finance, Elsevier, vol. 94(C).
    23. Bašta, Milan & Molnár, Peter, 2018. "Oil market volatility and stock market volatility," Finance Research Letters, Elsevier, vol. 26(C), pages 204-214.
    24. Patrick M. Crowley & Andrew Hughes Hallett, 2021. "The Evolution of US and UK Real GDP Components in the Time-Frequency Domain: A Continuous Wavelet Analysis," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(3), pages 233-261, December.
    25. Marcin Koltuniak, 2016. "Examination of the directions of spillover effects between the real estate and stock prices in Poland using wavelet analysis," Bank i Kredyt, Narodowy Bank Polski, vol. 47(3), pages 251-266.

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    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General

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