Monetary aggregates, August 2016

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 routines/schemes

Pre-treatment is an adjustment for variations caused by calendar effects and outliers.

No pre-treatment.

Calendar adjustment

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

No correction.

Correction for moving holidays

No correction.

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.

Model selection

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.

Decomposition scheme

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:

X-12-ARIMA

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.

Data availability

Raw and seasonally adjusted data are available.

Press releases

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.