About seasonal adjustment
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 information on seasonal adjustment: 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 credit 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 money supply M1 and M2, so that one can analyse the underlying money supply development.
Seasonally adjusted series
US census: X-12-ARIMA-manual
Money supply statistics publishes six seasonally adjusted series; M1, M2, M2-households, M2-non financial corporations, M2-municipal government and M2-other financial corporations. The seasonally adjusted figures for M2-other financial corporations is not a result of own seasonal adjustment, but a residual from the difference of seasonally adjusted M2 and the sum of the other sectors’ seasonally adjusted figures.
Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
- No pre-treatment.
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.
Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.
- Automatic model selection by established routines in the seasonal adjustment tool.
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.
- Manual decomposition scheme selection after graphical inspection of the series.
- We have chosen compulsory multiplicative decompositions. The program chose automatically this option until 2008, and it is now incorporated as a claim.
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.
- Do not apply any constraint.
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.
- Mixed indirect approach where the seasonal adjustment of components possibly occurs using different approaches and software.
Comments: The total is computed independently of the components. The last component is computed as a residual of the difference between the total and the other 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.
Comments: The data is used for M2 from January 1989 to the last observed December figure. For M1 and to other M2-series the data is used from December 1992 to the last observed December figure.
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.
Comments: The program for seasonal adjustment with new seasonal components is made once in a year, but seasonally adjusted figures are audited in accordance with audited raw data.
Concurrent versus current adjustment
- Controlled current adjustment: Forecasted calendar factors derived from a current adjustment are used to seasonally adjust the new or revised raw data. The numbers are revised when new, fixed factors are estimated once a year.
Horizon for published revisions
- The entire time series is revised in the event of a re-estimation of the seasonal factors.
Comments: The whole series which enters into seasonal adjustment is audited once a year. Apart from this, the elderly seasonal adjustment figures are only audited when unadjusted figures are been audited.
Evaluation of seasonally adjustment data
- Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.
Quality measures for seasonal adjustment
- No quality measures for seasonal adjustment assessment are 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.