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About seasonal adjustment
General information on seasonal adjustment
General information on seasonal adjustment
Monthly and quarterly time series are often characterized 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: Documentation of seasonal adjustment in Statistics Norway.
Why seasonally adjust these statistics?
The Consumer Price Index (CPI) is an indicator build up of many different sub aggregates. Some of these sub aggregates show a clear seasonal pattern, for instance the price index of clothing and footwear where seasonal sales are common. To make the comparability with earlier periods easier, the figures are seasonally adjusted.
A seasonally adjusted CPI can be interpreted as one of many indicators trying to identify the underlying inflation in the original series.
Seasonally adjusted series
Seasonally adjusted series are only published for the CPI all-item index and the all-item index of CPI adjusted for tax changes and excluding energy products, CPI-ATE.
Pre-treatment
Pre-treatment routines/schemes
Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
There are no pre-treatment of raw data.
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.
Not relevant for the CPI series.
Methods for trading/working day adjustment
Not relevant for the CPI series.
Correction for moving holidays
Not relevant for the CPI series.
National and EU/euro area calendars
Not relevant for the CPI series.
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.
Model selection is automatic using established procedures in the seasonal adjustment tool.
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 method is in use.
Seasonal adjustment
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.
Not relevant for the CPI series, no adjustment of the aggregates.
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.
Direct approach is used.
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.
For the CPI and the CPI-ATE the time series from 1985 and 1995 respectively, are used.
Audit procedures
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 each time it is published.
Comments : There is no revision in original series. For the seasonally adjusted series, new data can lead to revision in the seasonally adjusted series.
Concurrent versus current adjustment
The model, filters, outliers and regression parameters are re-identified and re-estimated continuously as new or revised data become available.
Horizon for published revisions
Both the CPI and the CPI-ATE are revised back 4 years; before that the seasonal adjusted figures are final.
Quality of seasonal adjustment
Evaluation of seasonally adjustment data
Periodical evaluation using standard measures proposed by different seasonal adjustment tools.
Quality measures for seasonal adjustment
Additional specific tests are computed to complement the set of available diagnostics within the seasonal adjustment tool.
Special cases
Seasonal adjustment of short time series
Both series are sufficiently long to perform an optimal seasonal adjustment.
Treatment of problematic series
None of the series are considered problematic.
Posting procedures
Data availability
Unadjusted data and seasonally adjusted data are available.
Press releases
Index series are published in the StatBank.