About seasonal adjustment
Monthly 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 X12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.
For more 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 credit fluctuates through the year. This complicates a direct comparison of debt figures from one month to the next. To adjust for these relations the debt is seasonally adjusted for the actual levels, so that one can analyse the underlying credit indicator development.
Series that are seasonally adjusted
The following seasonally adjusted series are produced for the credit indicator statistics; C1 households, C1 non-financial corporations and C1 municipal government. The seasonally adjusted total C1 and the seasonally adjusted series for C2 by sector are given by the seasonally adjusted lower aggregates for C1. Seasonally adjusted C1 equals the sum of the seasonally adjusted stocks of loans in NOK by sector. The seasonally adjusted series for C2 are given by the sum of the seasonally adjusted C1 and loans in foreign currency for each sector. Note that there is no seasonal pattern for loans in foreign currency, implying that this series is not seasonally adjusted.
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.