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Detecting signatures of stochastic self-organization in US money and velocity measures

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  • Serletis, Apostolos
  • Uritskaya, Olga Y.

Abstract

In this paper, we continue the research by Serletis [Random walks, breaking trend functions, and the chaotic structure of the velocity of money, J. Bus. Econ. Stat. 13 (1995) 453–458] and Serletis and Shintani [Chaotic monetary dynamics with confidence, J. Macroeconomics 28 (2006) 228–252] by applying the method of detrended fluctuation analysis (DFA)—introduced by Peng et al. [Mosaic organization of DNA nucleotides, Phys. Rev. E 49 (1994) 1685–1689] and adapted to the analysis of long-range correlations in economic data by Uritskaya [Forecasting of magnitude and duration of currency crises based on analysis of distortions of fractal scaling in exchange rate fluctuations, Noise and fluctuations in econophysics and finance, Proc. SPIE 5848 (2005) 17–26; Fractal methods for modeling and forecasting of currency crises, in: Proceedings of the fourth International Conference on Modeling and Analysis of Safety and Risk in Complex Systems, SPbSU Press, St.Petersburg, 2005, pp. 210–215]—to investigate the dynamical structure of United States money and velocity measures. We use monthly data over the time period from 1959:1 to 2006:2, at each of the four levels of monetary aggregation, M1, M2, M3, and MZM, making comparisons among simple-sum, Divisia, and currency equivalent (CE) methods of aggregation. The results suggest that the sum and Divisia monetary aggregates are more appropriate for measuring long-term tendencies in money supply dynamics while the CE aggregates are more sensitive measures of short-term processes in the economy.

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  • Serletis, Apostolos & Uritskaya, Olga Y., 2007. "Detecting signatures of stochastic self-organization in US money and velocity measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 281-291.
    • Handle: RePEc:eee:phsmap:v:385:y:2007:i:1:p:281-291
    DOI: 10.1016/j.physa.2007.06.039
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    1. Serletis, Apostolos & Shintani, Mototsugu, 2006. "Chaotic monetary dynamics with confidence," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 228-252, March.
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    Cited by:

    1. Choi-Meng Leong & Chin-Hong Puah & Shazali Abu Mansor & Evan Lau, 2010. "Testing the Effectiveness of Monetary Policy in Malaysia Using Alternative Monetary Aggregation," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(3), pages 321-338, August.
    2. Xian HUANG & Shilong XIA, 2015. "Currency - Equivalent Vs . Divisia Monetary Aggregates: Theoretical Evaluation And Empirical Evidence From The United States And China," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 60-80, September.
    3. Serinaldi, Francesco, 2010. "Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2770-2781.
    4. Apostolos Serletis & Sajjadur Rahman, 2009. "The Output Effects of Money Growth Uncertainty: Evidence from a Multivariate GARCH-in-Mean VAR," Open Economies Review, Springer, vol. 20(5), pages 607-630, November.
    5. Sunil Paul & M. Ramachandran, 2013. "Do Currency Equivalent Monetary Aggregates Have an Edge over Their Simple Sum Counterparts?," South Asian Journal of Macroeconomics and Public Finance, , vol. 2(2), pages 107-143, December.

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