About seasonal adjustment
General information on 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, see http://www.ssb.no/a/english/metadata/methods/seasonal_adjustment.pdf
Why seasonally adjust these statistics?
Within lots of industries the number of job vacancies and of employees follow patterns that are repeated each year. This is among other Things due to seasonal workers and announcements of these jobs, and various Activity throughout the year. The seasonal variations complicate a direct comparison from one quarter to the next. To adjust for this the time series are seasonally adjusted.
Series that are seasonally adjusted
The number of job vancancies and the number of employees are seasonally adjusted by 20 industries. The 20 seasonally adjusted series are aggregated to get the two seasonally adjusted totals. The figures for employees are not published, but they are included in the job vacancy rate. This means that the job vacancy rate is indirectly seasonally adjusted. In addition, seasonally adjusted figures are published by 10 industries. These are aggregated from the 20 seasonally adjusted series.
Running a detailed pre-treatment. This means using models which are specially adapted for the pre-treatment of the raw data for a given series.
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
The series are checked for outliers of different types. Once identified, outliers are explained/modelled using all available information. Outliers for which a Clear interpretation exists (strikes, consequences of (government) policy changes etc.) are included as regressors in the model.
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.
Automatic decomposition scheme selection.
Choice of seasonal adjustment approach
Consistency between raw and seasonally adjusted data
Do not apply any constraint.
Consistency between aggregate/definition of seasonally adjusted data
In some series, consistency between seasonally adjusted totals and the original series 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.
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.
The whole time series is used to estimate the model and the correction factors.
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 well-defined and publicly available revision policy and release calendar.
Concurrent versus current adjustment
Partial concurrent adjustment: The model and the seasonal filters are identified yearly, while all coefficients and seasonal factors are estimated continously as new or revised data become available.
Horizon for published revisions
The period of revisions is defined according to the characteristic features of the series based on information from the seasonal adjustment tool.
Quality of seasonal adjustment
Evaluation of seasonally adjusted data
A detailed set of graphical, non-Parametric and Parametric criteria defined to assess the relevant characteristics of seasonally adjusted data is used.
Quality measures for seasonal adjustment
The full set of Diagnostics and graphical facilities to assess the whole process of seasonal adjustment is used. (Only relevant for some/a few series.) Table of quality measurement for this statistics
For more information on the quality indicators in the table see: metadata on methods: seasonal adjustment
Seasonal adjustment of short time series
All series are sufficiently long to perform an optimal seasonal adjustment.
Treatment of problematic series
Problematic series are treated in a special way only when thay are relevant. The remaining series are treated according to normal procedures.
Raw and seasonally adjusted data are available.
For each series, some quality measures of the seasonal adjustment are presented.