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Does Monetary Policy cause Randomness or Chaos? A Case Study from the European Central Bank

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  • Sanderson, Rohnn

Abstract

Using the HICP (Harmonized Index of Consumer Prices) the author tests the series for the makeup of its dynamic components both before and after the start of stage three of the European Central Bank’s (ECB) monetary policy directive. While it appears ECB is meeting its stated objective, it is perhaps more important to address the composition of the lag and volatility of monetary policy to see how a policy change alters the fundamental dynamic structure of an economic system. The HICP data provides a good natural experiment for assessing structural change. This is important because while a policy may achieve its goal(s), in doing so it may alter the fundamental nature of how that system behaves, potentially causing the system to be more volatile or more sensitive to exogenous shocks in the future. Changes to the fundamental nature of a dynamic system can mean that future policies, that are similar to the present policies, could have very different impacts on that very same system in terms of both long run and short run effects. The paper finds that while the ECB may be meeting its stated objectives, it may be potentially increasing the degree and severity of future short run deflationary/inflationary cycles from similar policies in the future due to the type of random and deterministic components in the system. More data and further study is needed to determine the long-term affects of monetary policy in economic systems as many economic cycles are indeed very long.

Suggested Citation

  • Sanderson, Rohnn, 2013. "Does Monetary Policy cause Randomness or Chaos? A Case Study from the European Central Bank," MPRA Paper 52537, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:52537
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    File URL: https://mpra.ub.uni-muenchen.de/52537/1/BBS_en_2013_04_Sanderson.pdf
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    References listed on IDEAS

    as
    1. Baumol, William J & Benhabib, Jess, 1989. "Chaos: Significance, Mechanism, and Economic Applications," Journal of Economic Perspectives, American Economic Association, vol. 3(1), pages 77-105, Winter.
    2. Rohnn Sanderson, 2011. "Compartmentalising Gold Prices," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Eastern Macedonia and Thrace Institute of Technology (EMATTECH), Kavala, Greece, vol. 4(2), pages 99-124, August.
    3. Hsieh, David A, 1991. " Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
    4. Fama, Eugene F & French, Kenneth R, 1988. "Permanent and Temporary Components of Stock Prices," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 246-273, April.
    5. Milton Friedman, 1961. "The Lag in Effect of Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 69, pages 447-447.
    6. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    7. Howitt, Peter & Clower, Robert, 2000. "The emergence of economic organization," Journal of Economic Behavior & Organization, Elsevier, vol. 41(1), pages 55-84, January.
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    More about this item

    Keywords

    dynamic systems; Hurst exponent; chaos; long-term memory; monetary policy;

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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