Updated: 25 May 2023
Next update: 22 June 2023
|January 2023||February 2023||March 2023||April 2023|
|Money holding sector||5.1||4.3||3.7||2.4|
|Other financial corporations||-6.1||-3.3||-3.8||2.1|
More figures from this statistics
About the statistics
The main focus of the monetary aggregate statistics is the twelve-month growth of the money-holding sectors’ stock of money and other highly liquid financial assets. Calculations of transactions and growth are adjusted for changes caused by exchange rate fluctuations and non-transactional breaks.
The money-issuing sector consists of Norges Bank (The central bank of Norway), banks and mortgage companies (MFIs). In the monetary base M0 the money-issuing sector consists of Norges Bank.
The money-neutral sector consists of the central government, state lending institutions etc. and foreign sectors.
The money-holding sector consists of all sectors not included in the money-issuing or money-neutral sector, i.e. the general public (municipalities, non-financial corporations, nonprofit organisations and households) and financial corporations that are not MFIs.
The monetary base M0 is defined as the sum of Norwegian notes and coins in circulation and the MFIs’ and the money-holding sector's deposits in Norges Bank.
Narrow money M1 is defined as the money-holding sector's stock of Norwegian banknotes and coins (currency in circulation) and their transaction deposits in Norwegian kroner and foreign currency. Transaction deposits compromise deposits from which, regardless of purpose, payments and withdrawals may be made directly, without additional costs beyond regular transaction fees (overnight deposits).
Intermediate money M2 is defined as the sum of M1 and the money-holding sector's deposits in Norwegian kroner and foreign currency with period of notice up to three months or agreed maturity of up to two years.
Broad money M3 is defined as the sum of M2 and marketable instruments issued by the MFI sector. This includes repurchase agreements and debt securities/bonds with an original maturity of up to two years.
The classification of most financial instruments and sectoral structure used in the monetary aggregates follows the principles of the Monetary and Financial Statistics Manual (MFSM) and the Manual on MFI Balance Sheet Statistics (see coherence with other statistics section).
The monetary aggregates have two different breakdowns; holding sector and money supply object. Holding sector is a breakdown of the money-holding sector, by other financial corporations, non-financial corporations, municipalities and households. Money supply objects are currency in circulation, transaction deposits, other deposits, debt securities and repurchase agreements.
Name: Monetary aggregates
Topic: Banking and financial markets
Division for Financial Markets Statistics
Monthly. Monetary aggregates are published within one month after the reference period.
Reporting to the International Monetary Fund (IMF) via the Special Data Dissemination Standard (SDDS) and the Bank for International Settlements (BIS).
Collected microdata and published data are stored in databases at Statistics Norway.
Monetary aggregates are used as a basis for monetary policy. The statistics were established by Norges Bank in 1971 and adjusted for IMF standards in the publication of figures for November 2000. Following a review of content and international standards in 2014, the monetary aggregates now include the broad monetary aggregate M3.
Norway takes part in the Special Data Dissemination Standard (SDDS) of the International Monetary Fund (IMF). This standard requires Norway to publish a detailed breakdown of the monetary aggregates.
As of 1 January 2007, most of the work related to collection and release of financial markets statistics were transferred to Statistics Norway from Norges Bank. This included the monetary aggregates statistics.
The main users are monetary policy authorities, i.e. Norges Bank, the Financial Supervisory Authority and the Ministry of Finance. Other users are financial market operators and research institutions as well as students.
No external users have access to the statistics and analyses before they are published and accessible simultaneously for all users on ssb.no at 8 am. Prior to this, a minimum of three months' advance notice is given in the Statistics Release Calendar. For more information, see Principles for equal treatment of users in releasing statistics and analyses.
The statistics are based on the guidelines of the standards System of National Accounts 2008 (SNA 2008) and European System of National Accounts 2010 (ESA 2010), as well as guidelines for money supply statistics in IMF's Monetary and Financial Statistics Manual and Compilation Guide (2016) and the European Central Bank’s Manual on MFI Balance Sheet Statistics (2019).
The data sources of the monetary aggregates are the same as for the Financial corporations, balance sheet statistics. Data on debt securities are collected from the Norwegian Central Securities Depository (VPS); the same source used in the Securities statistics.
Derived statistics, without direct Council Directives or Council Regulations from the EU.
The statistics consist of the monetary aggregates M1, M2 and M3. See the section on definitions for further information. The money-issuing sector consists of Norges Bank, banks and mortgage companies (MFIs). In the monetary base M0, the money-issuing sector consists of Norges Bank.
Data for the money-issuing sector are derived from the accounting statistics of ORBOF (Reporting of banks, mortgage companies, state lending institutions and finance companies’ accounts to the public authorities(norwegian only)). Data for issued debt securities are derived from statistics for securities registered with the Norwegian Central Securities Depository (VPS). Exchange rate adjustments are calculated using the official exchange rates from Norges Bank and information on the currency composition of the money-holding sector’s deposits in banks from the BIS survey (report 13). Shares of deposits in foreign currency to calculate valuation changes are updated quarterly.
The statistics have a full census.
From 2007, collecting accounting data for banks and financial corporations has been the responsibility of Statistics Norway. Editing of data are undertaken by Statistics Norway and the Financial Supervisory Authority. The editing policy is to publish corrections of the previous month’s data together with the current month’s data. With every release, the latest 25 periods of stock data and 13 periods of transaction and growth data are updated. Statistics Norway is fully prepared to edit in a timely manner, with appropriate notification to users and the media, should it be deemed necessary by the magnitude of a past error, or, owing to other exceptional circumstances. Some of the reported data may contain preliminary data that are subsequently corrected. Statistics Norway monitors and analyses the data, and editing are conducted regularly.
The main focus in the statistics is transaction-based changes. In growth calculations that include deposits in foreign currency, the transaction and growth rates are adjusted for changes in exchange rates. Growth calculations are also adjusted for structural breaks. These adjustments will lead to discrepancies between growth calculations based on stocks and growth calculations based on transactions. Revisions in accounting standards and changes in accounting legislation may also lead to breaks in the time series.
The seasonal adjustment of monetary aggregate stocks is carried out using the X12 Arima method. Seasonal components are recalculated with each publication and seasonally adjusted stocks, as well as monthly transactions and growth rates are updated. Only the broad money supply (M3) is seasonally adjusted. For this aggregate, seasonally adjusted series are released by money-holding sector, households, non-financial corporations, municipalities and other financial corporations.
Revisions in international standards and major changes in fiscal legislation can lead to breaks in the time series. The same consequences can result from sectoral shifts. Transactions and growth estimates are adjusted for breaks in time series (see the section on data collection, revision and estimations).
Change in the statistics on banks and mortgage companies in 2018
The adjustment of ORBOF to IFRS has led to a change in the statistics on banks and mortgage companies from January 2018. An important implication for the money supply is that accrued interests and changes in value are included with the underlying financial object. As of January 2018, the stock time series are not comparable with previous periods. Transaction and growth series are corrected for this break.
Change in the monetary aggregate statistics in 2015
The stock series, transactions and growth rates in the money supply changed as a result of the change in the monetary aggregate statistics in 2015. In the new money supply statistics framework, M3 is included as the main aggregate in the money supply. M1 and M2 are continued as subsets.
As of April 2015, new deposit specifications were introduced in the balance sheet reporting. This lead to a break in the stock time series involving deposits. The monetary aggregate statistics time series from the periods prior to April 2015 are therefore not comparable with later periods. Transaction and growth series are corrected for this break. Growth series are thus comparable back to January 2009.
New institutional sector classification in 2012
As from January 2012, the Norwegian institutional sector classification has been revised in line with the international classification. This change implies a break in stock time series between February and March 2012.
The statistics are mainly derived from the financial markets statistics. Errors and inconsistencies in these statistics will also affect the monetary aggregates. In this context, we refer to the sections on sources of error and uncertainty in these statistics.
For the monetary aggregates, the response rate is 100 per cent, and variance and bias are not relevant.
The statistics show preliminary figures. Data may be revised in future publications. At each publication, stock time series are updated with the latest 25 periods. Transactions and growth are updated for 13 periods.
Monthly and quarterly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Such time series are therefore subjected to a process of seasonal adjustment in order to remove the effects of these seasonal fluctuations. Once data have been adjusted for seasonal effects by X-12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.
For more general information on seasonal adjustment: see metadata on methods: seasonal adjustment
On the basis of public holidays and holiday period in July and December the intensity of the supply and demand of money fluctuates through the year. This complicates a direct comparison of money supply figures from one month to the next. To adjust for these relations the money supply seasonally adjusts the actual level for the monetary aggregate M3, so that one can analyze the underlying money supply development.
Seasonally adjusted series
Five seasonally adjusted series are published in the monetary aggregate statistics; M3, M3 households, M3 non-financial corporations, M3 municipal government and M3 other financial corporations. The stock time series for each sector aggregate is seasonally adjusted separately and summed up to the total seasonally adjusted M3.
Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
Calendar adjustment involves adjusting for the effects of working days/trading days and for moving holidays. Working days/trading days are adjustment for both the number of working days/trading days and for that the composition of days can vary from one month to another.
No calendar adjustment of any kind is performed.
Methods for trading/working day adjustment
Correction for moving holidays
National and EU/euro area calendars
Definition of series not requiring calendar adjustment.
Treatment of outliers
Outliers, or extreme values, are abnormal values of the series.
Outliers are detected automatically by the seasonal adjustment tool. The outliers are removed before seasonal adjustment is carried out, and then reintroduced into the seasonally adjusted data.
Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.
Manual model selection after running statistical tests. The choice of ARIMA-model is assessed once a year at the time of release of data for January. The model is constant for at least one year.
The decomposition scheme specifies how the various components – basically trend-cycle, seasonal and irregular – combine to form the original series. The most frequently used decomposition schemes are the multiplicative, additive or log additive.
Multiplicative decomposition is applied.
Choice of seasonal adjustment approach:
Consistency between raw and seasonally adjusted data
In some series, consistency between raw and seasonally adjusted series is imposed.
Do not apply any constraint.
Consistency between aggregate/definition of seasonally adjusted data
In some series, consistency between seasonally adjusted totals and the aggregate is imposed. For some series there is also a special relationship between the different series, e.g. GDP which equals production minus intermediate consumption.
Definitions and relationships also apply for seasonally adjusted figures.
Direct versus indirect approach
Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.
Indirect approach where the seasonal adjustment of components occurs using the same approach and software, and then totals are derived by aggregation of the seasonally adjusted components.
Horizon for estimating the model and the correction factors
When performing seasonal adjustment of a time series, it is possible to choose the period to be used in estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the series.
Only part of the time series is used to estimate the correction factors and the model.
General revision policy
Seasonally adjusted data may change due to a revision of the unadjusted (raw) data or the addition of new data. Such changes are called revisions, and there are several ways to deal with the problem of revisions when publishing the seasonally adjusted statistics.
Seasonally adjusted data are revised in accordance with a well-defined and publicly available revision policy and release calendar. Revised seasonal adjusted data are released with every publication. Stocks are updated with possible revisions for the latest 25 periods.
Concurrent versus current adjustment
Seasonal factors are estimated with every release. The model, filters and outliers are assessed once a year and are constant for at least one year.
Horizon for published revisions
With every release of data, seasonally adjusted figures are updated for the latest 25 periods. More periods are updated if necessary due to larger revisions.
Evaluation of seasonally adjustment data
Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.
Quality measures for seasonal adjustment
For most of the series, a selected set of diagnostics and graphical facilities for bulk treatment of data is used.
Seasonal adjustment of short time series
All series are sufficiently long to perform an optimal seasonal adjustment.
Treatment of problematic series
Νο series are treated in a special way, irrespective of their characteristics.
Raw and seasonally adjusted data are available.
In addition to raw data, at least one of the following series is released: pre-treated, seasonally adjusted, seasonally plus working day adjusted, trend-cycle series.
Eirin Ingvaldsen Brynestad
Mons Even Oppedal