On seasonal adjustment of production index, construction
1. What is seasonal adjustment?
2. Pre-treatment
3. Seasonal adjustment
4. Revision policies
5. Quality of seasonal adjustment
6. Specific issues on seasonal adjustment
7. Data presentation issues
8. References
1. What is seasonal adjustment?
1.1 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: seasonal adjustment
1.2 Why do we seasonally adjust Production index, construction
The production index is adjusted for seasonal variations applying the X12ARIMA method with direct seasonal adjustment, non-fixed seasonal effects, multiplicative model and pre-correction of Easter effects. It is assumed that the employed persons in the construction industry are off duty the whole Easter-week.
Time series are adjusted for calendar effects. Trading adjustment also includes movable public holidays.
Seasonally adjusted figures: By removing the seasonal effects, the underlying economic development becomes more evident.
1.3 Seasonally adjusted series
For the production index, construction 5 seasonally series are published; Total, buildings, new buildings, renovation, and civil engineering works.
2. Pre-treatment
2.1 Pre-treatment routines/schemes
Options:
- Running an automatic pre-treatment of the raw data based on standard options in the seasonal adjustment tools.
2.2 Calendar adjustment
Options
- 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).
2.2.1 Methods for trading/working day adjustment
Options:
- 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.
2.2.2 Correction for moving holidays
- Correction within proportional number of day adjustment, in which the effect of moving holidays is estimated using the proportion of the different holidays in each month/quarter.
2.2.3 National and EU/euro area calendars
- Use of default calendars. The default in X-12-ARIMA is the US calendar.
2.3 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.
2.4 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 primarily automatic, but in some cases models are selected manually
2.5 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.
- Manual decomposition scheme selection after graphical inspection of the series.
3. Seasonal adjustment
3.1 Choice of seasonal adjustment approach
3.2 Consistency between raw and seasonally adjusted data
- Do not apply any constraint.
3.3 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.
3.4 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 where the raw data are aggregated and the aggregates and components are then directly seasonally adjusted using the same approach and software. Any discrepancies across the aggregation structure are not removed.
3.5 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
4. Revision policies
4.1 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.
4.2 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.
4.3 Horizon for published revisions
- The entire time series is revised in the event of a re-estimation of the seasonal factors.
5. Quality of seasonal adjustment
5.1 Evaluation of seasonally adjustment data
- Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.
5.2 Quality measures for seasonal adjustment
- No quality measures for seasonal adjustment assessment are used.
6. Specific issues on seasonal adjustment
6.1 Seasonal adjustment of short time series
- All series are sufficiently long to perform an optimal seasonal adjustment.
6.2 Treatment of problematic series
- None of the published series are viewed as problematic.
7. Data presentation issues
7.1 Data availability
- Raw and seasonally adjusted data are available.
- All metadata information associated with an individual time series is available.
7.2 Press releases
- 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.
- For each series, some quality measures of the seasonal adjustment are presented.
8. References
Metadata on methods: seasonal adjustment
The Committee for Monetary, Financial and Balance of Payments statistics: ESS-Guidelines on seasonal adjustment
EUROSTAT:
Seasonal Adjustment. Methods and Practices
US census: X-12-ARIMA-manual
Dinh Quang Pham: Nye US Census-baserte metoder for ukedagseffekter for norske data, Notater 2008/58, Statistisk sentralbyrå
Dinh Quang Pham: Ny metode for påskekorrigering for norske data, Notater 2007/43, Statistisk sentralbyrå.
Ole Klungsøyr: Sesongjustering av tidsserier. Spektralanalyse og filtrering, Notat 2001/54, Statistisk sentralbyrå
Dinh Quang Pham: Innføring i tidsserier - sesongjustering og X-12-ARIMA, Notater 2001/2, Statistisk sentralbyrå