About seasonal adjustment
What is 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: metadata on methods: seasonal adjustment
Seasonally adjusted series
Seasonally adjusted series and trend are calculated for the monthly series of building work started; number of dwellings, utility floor space – dwellings and utility floor space – other buildings.
To perform analyses of the underlying development in the data for building statistics, the series are seasonally adjusted.
Running an automatic pre-treatment of the raw data based on standard options in the seasonal adjustment tools.
To perform calendar adjustments on all series showing significant and plausible calendar effects within a statistically robust approach, such as regression or RegARIMA (a regression model with an ARIMA structure for the residuals).
Methods for trading/working day adjustment
RegARIMA correction – in this case, the effect of trading days is estimated in a RegArima framework. The effect of trading days can be estimated by using a correction for the length of the month or leap year, regressing the series on the number of working days, etc. In this case, the residuals will have an ARIMA structure.
Correction for moving holidays
Automatic correction. If performed by X-12-ARIMA, automatic correction of raw data will be based on US holidays.
National and EU/euro area calendars
Use of default calendars. The default in X-12-ARIMA is the US calendar.
Treatment of outliers
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.
Model selection is primarily automatic, but in some cases models are selected manually.
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.
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.
No approach needed.
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.
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.
Both raw and seasonally adjusted data are revised between two consecutive official releases of the release calendar.
Comments: Raw data are not revised.
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
The entire time series is revised in the event of a re-estimation of the seasonal factors.
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
None of the published series are viewed as problematic.
Raw and seasonally adjusted data are available.
All metadata information associated with an individual time series is 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.
Both levels/indices and different forms of growth rates are presented.