Money Supply
Money Supply is the total amount of money that is in circulation in a country. It includes currency, demand deposits, and other liquid assets that can be easily converted into cash. Money supply is an important economic indicator that the Federal Reserve uses to implement its monetary policy.
Money supply can be divided into five categories: M0, M1, M2, M3 and M4.
- M0: The total of all physical currency, plus accounts at the central bank that can be exchanged for physical currency.
- M1: The total of all physical currency part of bank reserves + the amount in demand accounts (“checking” or “current” accounts).
- M2: M1 + most savings accounts, money market accounts, retail money market mutual funds, and small denomination time deposits.
- M3: M2 + large time deposits, institutional money market funds, short-term repurchase agreements, and other larger liquid assets.
- M4: M3 + all other financial assets.
Data comes from the Global Macro Database (GMDB), further information about the variable can be found within https://www.globalmacrodata.com/documentation.html
The aggregates are annual and expressed in millions of national currency, so levels are not comparable across countries with different currencies, but growth rates are. Not every country publishes every aggregate; the ones it does not are NaN.
Also known as: M1, M2, M3, monetary aggregate.
Money Supply in Python or via the MCP
Install the Finance Toolkit with:
pip install financetoolkit -U
Then call get_money_supply as shown below.
from financetoolkit import Economics
economics = Economics(start_date='2010-01-01', end_date='2020-12-31')
money_supply = economics.get_money_supply(
countries=['Netherlands', 'Germany', 'United States'],
measure='M2'
)
Or, after setting up the Finance Toolkit MCP, ask:
How large was the M2 money supply of the Netherlands, Germany and the United States, in national currency, from 2010 to 2020?
Which returns:
| Netherlands | Germany | United States | |
|---|---|---|---|
| 2010 | 701718 | 1.9878e+06 | 8.4731e+06 |
| 2011 | 727265 | 2.1053e+06 | 8.8445e+06 |
| 2012 | 746482 | 2.2556e+06 | 9.7533e+06 |
| 2013 | 741372 | 2.3144e+06 | 1.05023e+07 |
| 2014 | 743043 | 2.4272e+06 | 1.11275e+07 |
| 2015 | 822382 | 2.6518e+06 | 1.18062e+07 |
| 2016 | 841302 | 2.8022e+06 | 1.25222e+07 |
| 2017 | 851237 | 2.9236e+06 | 1.33189e+07 |
| 2018 | 846513 | 3.0562e+06 | 1.39024e+07 |
| 2019 | 889033 | 3.1968e+06 | 1.44602e+07 |
| 2020 | 974276 | 3.4582e+06 | 1.5425e+07 |
Parameters
get_money_supply accepts the following parameters.
| Parameter | Type | Description | Example |
|---|---|---|---|
countries | list[str] | str | None | The countries to include in the data. Defaults to None. | ["United States", "Germany"] |
measure | str | None | Which single aggregate to return, one of ‘M0’, ‘M1’, ‘M2’, ‘M3’ or ‘M4’. Defaults to None, which returns all five with the aggregate as the first level of the column index. | "M2" |
rolling | int | The rolling window size to use for smoothing the data (simple moving average). Defaults to None. | 4 |
trailing | int | The trailing window size to use for summing the data over trailing periods (e.g. a trailing-4-quarter sum). Defaults to None. | 4 |
growth | bool | Whether to return the growth data or the actual data. Defaults to False. | True |
lag | int | The number of periods to lag the growth data. Defaults to 1. | 4 |
standardize | bool | Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False. | True |
rounding | int | None | The number of decimals to round the results to. Defaults to None. | 2 |
gmdb_forecasts | bool | Whether to include the years the Global Macro Database projects, from the IMF’s World Economic Outlook, up to five years ahead. Only applies to data from the Global Macro Database. Defaults to False, which ends a series with its last observation. | True |