Construction, new orders (discontinued), Q3 2015

Statistics Norway has decided to stop the publication of the Construction, new orders from the first quarter of 2018. One reason for this was that the statistic previously was a part of Eurostat's regulation for short-term statistics, but this requirement was removed from the regulation in 2012.

Content

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: metadata on methods: seasonal adjustment

Seasonally adjusted series

Seasonally adjusted series are available for stock of orders for civil engineering, construction total and residential buildings.

 

Why seasonally adjust these statistics?

Data are adjusted for seasonal effects in order to be able to analyse the underlying development in data.

Pre-treatment

Pre-treatment routines/schemes

Running an automatic pre-treatment of the raw data based on standard options in the seasonal adjustment tools.

Calendar adjustment

No calendar adjustment of any kind is performed.

Methods for trading/working day adjustment

No correction.

Correction for moving holidays

No correction.

National and EU/euro area calendars

Definition of series not requiring calendar adjustment.

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.

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.

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.

Seasonal adjustment

Choice of seasonal adjustment approach

X-12-ARIMA

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.

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.

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.

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.

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.

Quality of seasonal adjustment

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.

Special cases

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.

Posting procedures

Data availability

Raw and seasonally adjusted data are available.

All metadata information associated with an individual time series is available.

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.

Relevant documentation