Turnover in oil and gas, manufacturing, mining and electricity supply, February 2017

The statistics on turnover is part of a system of short-term statistics compiled to monitor the economy. The primary goal of the survey is to monitor the level and development of sales in mining and quarrying, oil and gas extraction, manufacturing, electricity and gas supply.

The turnover will vary through the year because of public holidays. Typically the sales will always be lower in July because of general staff holiday. The different number of working-days in the different months will also influence the pattern of turnover through the year. These circumstances make it difficult to compare the data from month to month. To adjust for these effects the statistics on turnover is seasonally adjusted, and in this way we are able to analyse the underlying development in turnover which says something about the economic cycle from month to month.

Seasonally adjusted series

For statistics on turnover seasonally adjusted series are published for 32 industry aggregates. These industries are the total, mining and quarrying, oil and gas extraction, manufacturing, electricity supply and different manufacturing industries. I addition seasonally adjusted series aggregated according to Eurostat’s main industrial groupings are published.

Seasonally adjusted series are published for total turnover, export turnover and domestic turnover for each industry aggregate. In total 96 seasonally adjusted series are published.

Pre-treatment routines/schemes

Pre-treatment is an adjustment for variations caused by calendar effects and outliers.

Running a detailed pre-treatment. This means using models which are specially adapted for the pre-treatment of the raw data for a given series.

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.

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

Correction based on an estimation of the duration of the moving holidays effects, specifically adjusted to Norwegian circumstances.

National and EU/euro area calendars

Use of the Norwegian calendar or the EU/euro area calendar as appropriate; the EU/euro area calendar is based on the mean number of working days in the different member states.

  Comments: The Norwegian calendar is in use

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 primarily automatic, but in some cases models are selected manually. 

Comments: Log transformation of the unadjusted figures is carried out

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.

For series with zero or negative values, adding a constant to make the series positive and select the appropriate decomposition scheme. 

Comments: Log additiv method is in use

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.

 Impose the equality between aggregated series and the component series.

Comments : Only equality between the overall index and extraction and related services, manufacturing, mining and quarrying, and electricity, gas and steam is imposed.

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.

Comments : The overall index is a formula of extraction and related services, manufacturing, mining and quarrying and electricity, gas and steam supply.

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. 

Seasonally adjusted data are revised in accordance with a well-defined and publicly available revision policy and release calendar.

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 revision period for the seasonally adjusted results is limited to 3-4 years (preferably 4) prior to the revision period of the unadjusted data, while older data are frozen. The whole time-series may be revised in the case of implementation of new and improved methods.

Comments: The seasonally adjusted figures are updated 4 years back when new data is added.

Evaluation of seasonally adjustment data

Continuous/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.

A table containing selected quality indicators for the seasonal adjustment is available. The table covers the published industry aggregates for total turnover. The table is availible here : Indicators of quality in seasonal adjusted figures.

For more information on the quality indicator in the table see: metadata on methods: seasonal adjustment

Seasonal adjustment of short time series

All series are sufficiently long to perform an optimal seasonal adjustment.

Treatment of problematic series

Problematic series are treated in a special way only when they are relevant. The remaining series are treated according to normal procedures.

Data availability

Unadjusted data, calendar adjusted and seasonally adjusted data are 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.