Production index for construction, Q1 2021

The production index is adjusted for seasonal variations since analysis show a stable seasonally pattern. By removing the seasonal effects, the underlying economic development becomes more evident.

Pre-treatment routines/schemes

A detailed pre-tratment is done. This means that the tools are based on special adjusted models and not based on standard options in the seasonal adjustment tools.

Calendar adjustment

Calendar adjustments are performed 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

Working days are not adjusted for, but the figures are adjusted for easter-variations. Still these figures are called and reffered to as working days adjusted figures.

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. This is adjusted for Norwegian holidays.

National and EU/euro area calendars

Depending on use eighter a Norwegian calendar or a European EU/EU-area calendar is used.

Treatment of outliers

Outliers are detected automatically by the seasonal adjustment tool. When they are identifyed and an explanation for their values are found they are included as a regressor in the model.

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.

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.

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.

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.