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Money Growth and Inflation: evidence from a Markov Switching Bayesian VAR

Author

Listed:
  • Gianni Amisano

    (DG Research, European Central Bank and University of Brescia, Italy)

  • Roberta Colavecchio

    (Universitaet Hamburg (University of Hamburg))

Abstract

We contribute to the empirical debate on the role of money in monetary policy by analysing the features of the relationship between money growth and inflation in a Bayesian Markov Switching framework for a set of four countries, the US, the UK, the Euro area and Japan, over an estimation period spanning from 1960 to 2012. We find that the relationship between money growth and inflation appears to be nonlinear, as our estimation results identify multiple inflation regimes displaying clear and diversified features; moreover, as part of the model's information set, money growth plays a determinant role in the allocation of regimes. We show that observing monetary developments does (slightly) improve the signal of entering a high inflation regime but the influence of money on such signal seems to be relevant mainly in the 70s and the early 80s, i.e. in periods featuring exceptionally high rates of inflation. Our evidence confi?rms that the relationship between money and inflation appears to be relatively weak during periods featuring low and stable inflation.

Suggested Citation

  • Gianni Amisano & Roberta Colavecchio, 2013. "Money Growth and Inflation: evidence from a Markov Switching Bayesian VAR," Macroeconomics and Finance Series 201304, University of Hamburg, Department of Socioeconomics.
  • Handle: RePEc:hep:macppr:201304
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    File URL: https://www.wiso.uni-hamburg.de/repec/hepdoc/macppr_4_2013.pdf
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    References listed on IDEAS

    as
    1. De Santis, Roberto A., 2012. "Quantity theory is alive: the role of international portfolio shifts," Working Paper Series 1435, European Central Bank.
    2. Ralf Brüggemann & Helmut Lütkepohl & Massimiliano Marcellino, 2008. "Forecasting euro area variables with German pre-EMU data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 465-481.
    3. Sylvia Kaufmann & Peter Kugler, 2008. "Does Money Matter For Inflation In The Euro Area?," Contemporary Economic Policy, Western Economic Association International, vol. 26(4), pages 590-606, October.
    4. Lucas, Robert E, Jr, 1980. "Two Illustrations of the Quantity Theory of Money," American Economic Review, American Economic Association, vol. 70(5), pages 1005-1014, December.
    5. Andreas Beyer & Jurgen A. Doornik & David F. Hendry, 2000. "Reconstructing Aggregate Euro‐zone Data," Journal of Common Market Studies, Wiley Blackwell, vol. 38(4), pages 613-624, November.
    6. 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.
    7. John Geweke & Gianni Amisano, 2011. "Hierarchical Markov normal mixture models with applications to financial asset returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
    8. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    9. Antonello D'Agostino & Paolo Surico, 2009. "Does Global Liquidity Help to Forecast U.S. Inflation?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(2-3), pages 479-489, March.
    10. 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.
    11. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    12. Emil Stavrev & Helge Berger, 2012. "The information content of money in forecasting euro area inflation," Applied Economics, Taylor & Francis Journals, vol. 44(31), pages 4055-4072, November.
    13. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, April.
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    Cited by:

    1. Colavecchio, Roberta & Amisano, Gianni & Fagan, Gabriel, 2014. "A money-based indicator for deflation risk," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100595, Verein für Socialpolitik / German Economic Association.
    2. Cuneyt Dumrul & Yasemin Dumrul, 2015. "Price-Money Relationship after Infl ation Targeting: Co-integration Test with Structural Breaks for Turkey and Brazil," International Journal of Economics and Financial Issues, Econjournals, vol. 5(3), pages 701-708.
    3. Eltejaei , Ebrahim & Montazeri Shoorekchali , Jalal, 2021. "Investigating the Relationship between Money Growth and Inflation in Turkey: A Nonlinear Causality Approach," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 16(3), pages 305-322, September.
    4. Markku Lanne & Jani Luoto & Henri Nyberg, 2014. "Is the Quantity Theory of Money Useful in Forecasting U.S. Inflation?," CREATES Research Papers 2014-26, Department of Economics and Business Economics, Aarhus University.
    5. Claudio Borio & Marco Jacopo Lombardi & James Yetman & Egon Zakrajsek, 2023. "The two-regime view of inflation," BIS Papers, Bank for International Settlements, number 133.

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    More about this item

    Keywords

    Money growth; infl?ation regimes; Markov Switching model; Bayesian inference;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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