Abstract
Leading central banks did not anticipate the surge in inflation in 2021 and 2022. In our paper we assess whether changes in the velocity of money and monetary growth (broadly defined) explain long term inflation patterns in the US. We use a hundred-year sample to study the long term and the cyclical behaviour of money velocity. We find that changes in the velocity of money are short lived and revert to its mean. We also characterise the periods where changes in money velocity have kept closer to its mean as those of monetary equilibrium. We use a regime switching model to test for the impact of changes in the amount of money (broadly defined) and in money velocity in inflation over the medium and long term. Our model explains both the non-inflationary outcome of the Global Financial Crisis and the surge in inflation in the aftermath of the Covid-19 pandemic.
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Data Availability
Data used and sources detailed in footnote 2.
Notes
The relationship between money growth, inflation and money velocity has been modelled for the period 1948–2021 due to the availability of comparable data series since 1948. In Sect. 3 we cover a broader time period, from 1919 to 2021, as it is available for money growth and nominal GDP (on the contrary, for CPI inflation we have data since 1948).
By broad money, we mean a monetary aggregate that includes not just cash in circulation but the majority of bank deposits in the US. In this regard, we have made use of the monetary aggregate M3 calculated by Shadow Government Statistics (http://www.shadowstats.com), for 2006-2021, following the same method and data which the US Federal Reserve used to publish M3 (1959-2006), and broad money from Gordon (1986) for 1919-1958. For nominal GDP: Gross Domestic Product, Billions of Dollars, Quarterly (https://fred.stlouisfed.org) after 1959 and Gordon (1986) for 1919-1958.
We include an intercept in the test coherently with the non-zero value of the mean growth of the velocity, \(\mu_{v}\), and select automatically the number of lags of the test by means of a Schwarz information criterion.
Consumer Price Index for All Urban Consumers: All Items in U.S. City Average, seasonally adjusted (https://fred.stlouisfed.org).
We refer to the spectral gain of the filter \(1 - L^{4}\), which is applied when calculating the annual rate of a time series. The spectral gain measures the increase in amplitude of any specific frequency component of a time series. For the filter \(1 - L^{4}\), the spectral gain in the frequency domain is \(2(1 - \cos 4\omega )\) with \(\omega\) a frequency component. This filter eliminates the trend and seasonal variation components, letting the remaining components pass. Within them, above the year, the fluctuations with a period between 5.3 quarters and 16 quarters have a greater weight (i. e. a normalized gain greater than 0.5).
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Acknowledgements
We want to thank Guillermo Sagnier for his excellent research assistance. Financial support of Universidad Francisco de Vitoria to this research is also acknowledged. We thank the referees for their comments, as well those from the attendees and fellow contributors to the ‘2022 Institute of International Monetary Research Conference’—where an earlier version of the paper was presented.
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Universidad Francisco de Vitoria.
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Castañeda, J.E., Cendejas, J.L. Money Growth, Money Velocity and Inflation in the US, 1948–2021. Open Econ Rev (2023). https://doi.org/10.1007/s11079-023-09739-0
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DOI: https://doi.org/10.1007/s11079-023-09739-0