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Nonlinear relationship between permanent and transitory components of monetary aggregates and the economy

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  • Richard G. Anderson
  • Marcelle Chauvet
  • Barry E. Jones

Abstract

This paper uses several methods to study the interrelationship among Divisia monetary aggregates, prices, and income, allowing for nonstationary, nonlinearities, asymmetries, and time-varying relationships among the series. We propose a multivariate regime switching unobserved components model to obtain transitory and permanent components for each series, allowing for potential recurrent and structural changes in their dynamics. Each component follows distinct two-state Markov processes representing low or high phases. Since the lead-lag relationship between the phases can vary over time, rather than preimposing a structure to their linkages, the proposed flexible framework enables us to study their specific lead-lag relationship over each one of their cycles and over each U.S. recession in the last 40 years. The decomposition of the series into permanent and transitory components reveals striking results. First, we find a strong nonlinear association between the components of money and prices ? all low phases of the transitory component of prices were preceded by tight transitory and permanent money phases. We also find that most recessions were preceded by tight money phases (its cyclical and permanent components) and high transitory price phases (with the exception of the 2001 and 2009-2010 recessions). In addition, all recessions were associated with a decrease in transitory and permanent income.>

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  • Richard G. Anderson & Marcelle Chauvet & Barry E. Jones, 2013. "Nonlinear relationship between permanent and transitory components of monetary aggregates and the economy," Working Papers 2013-018, Federal Reserve Bank of St. Louis.
  • Handle: RePEc:fip:fedlwp:2013-018
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    Cited by:

    1. Michael D. Bordo & John V. Duca, 2023. "Broad Divisia Money and the Recovery of U.S. Nominal GDP from the COVID-19 Recession," Working Papers 319, Princeton University, Department of Economics, Center for Economic Policy Studies..
    2. Chun Deng & Jie-Fang Dong, 2016. "Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model," Sustainability, MDPI, vol. 8(9), pages 1-16, August.
    3. Michael T. Belongia & Peter N. Ireland, 2013. "Instability: Monetary and Real," Boston College Working Papers in Economics 830, Boston College Department of Economics.
    4. Michael T. Belongia & Peter N. Ireland, 2016. "Money and Output: Friedman and Schwartz Revisited," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(6), pages 1223-1266, September.
    5. Michael D. Bordo & John V. Duca, 2023. "Money Matters: Broad Divisia Money and the Recovery of Nominal GDP from the COVID-19 Recession," Working Papers 2306, Federal Reserve Bank of Dallas.
    6. Sijia Li & Lihua Wu, 2024. "Can regional integration promote industrial green transformation? Empirical evidence from Yangtze River Delta Urban Agglomeration," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 14(1), pages 117-134, March.
    7. Fleissig, Adrian R. & Jones, Barry E., 2015. "The impact of commercial sweeping on the demand for monetary assets during the Great Recession," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 412-422.
    8. Allan H. Meltzer, 2014. "Current Lessons from the Past: How the Fed Repeats Its History," Cato Journal, Cato Journal, Cato Institute, vol. 34(3), pages 519-539, Fall.
    9. Apostolos Serletis & Khandokar Istiak, 2016. "Are the Responses of the U.S. Economy Asymmetric to Positive and Negative Money Supply Shocks?," Open Economies Review, Springer, vol. 27(2), pages 303-316, April.
    10. Lu, Yao & Zhao, Zhihui & Tian, Yuan & Zhan, Minghua, 2024. "How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    11. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    12. Xu, Bin & Lin, Boqiang, 2015. "Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach," Energy, Elsevier, vol. 83(C), pages 486-495.

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    Keywords

    Debt; Inflation (Finance); Banks and banking; Central;
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