325876
/en/energi-og-industri/statistikker/ogibkoms/maaned
325876
statistikk
2018-09-07T08:00:00.000Z
Energy and manufacturing;Energy and manufacturing
en
ogibkoms, Turnover in oil and gas, manufacturing, mining and electricity supply, industrial turnover, domestic market, export market, valueEnergy , Oil and gas , Manufacturing, mining and quarrying , Energy and manufacturing
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Turnover in oil and gas, manufacturing, mining and electricity supply

Updated

Next update

Key figures

4.7 %

increase in turnover in manufacturing compared with the last three-month period

Turnover. Percentage change and NOK million
Seasonally adjustedCalendar adjusted1Unadjusted
Monthly changeThree-month changeTwelve-month changeNOK million
July 2018 / June 2018May 2018 - July 2018 / February 2018 - April 2018July 2018 / July 2017July 2018
1Adjusted for working-days and for public holidays in Norway.
Extraction, mining, manufacturing and elec2.76.126.1131 624
Extraction and related services6.29.649.457 868
Mining and quarrying-7.1-1.5-4.11 093
Manufacturing-1.24.78.960 336
Food, beverages and tobacco1.32.73.918 764
Refined petro., chemicals, pharmac.4.310.125.412 560
Basic metals2.31.712.75 914
Machinery and equipment-5.612.638.23 117
Ships, boats and oil platforms-13.08.7-10.13 300
Electricity, gas and steam8.81.034.712 327

See more tables on this subject

Table 1 
Turnover. Monthly numbers, by division and main industrial groupings and by market. Unadjusted series. NOK million

Turnover. Monthly numbers, by division and main industrial groupings and by market. Unadjusted series. NOK million123
July 201812-monthly change
May 2018 / May 2017June 2018 / June 2017July 2018 / July 2017
1The Figures can be revised twice after published, but no later than 6 months after the initial publishing.
2Total turnover based on grossed up sample figures.
3The levels of aggregation refers to the Standard Industrial Classification (SIC2007). See NOS D383 for further details.
Extraction, mining, manufacturing and electricity131 62416.019.027.5
Domestic market55 41910.511.513.5
Export market76 20521.326.440.1
 
Extraction and related services57 86826.634.549.5
Domestic market10 33214.017.521.0
Export market47 53530.139.757.5
 
Manufacturing, mining and quarrying61 42912.59.811.0
Domestic market33 54413.47.76.0
Export market27 88511.312.617.7
 
Mining and quarrying1 093-2.8-8.0-1.3
Domestic market578-4.8-17.0-6.3
Export market5150.07.55.1
 
Manufacturing60 33612.810.111.3
Domestic market32 96613.88.36.2
Export market27 37011.512.718.0
 
Electricity, gas and steam12 327-2.821.934.6
Domestic market11 542-2.321.633.7
Export market785-14.028.750.1
 
Main industrial groupings:
Intermediate goods31 6613.865.06.2
Domestic market18 0444.176.54.4
Export market13 6173.3-1.68.6
 
Capital goods11 87626.6111.66.7
Domestic market6 78827.656.94.1
Export market5 08825.3-63.210.4
 
Consumer goods18 73611.643.29.1
Domestic market14 03316.73.17.7
Export market4 704-2.0-87.513.3
 
Energy goods69 35123.237.253.8
Domestic market16 5544.533.938.0
Export market52 79730.838.459.5

Table 2 
Turnover. Monthly numbers, by division, main industrial groupings and market. Unadjusted series. 2005=100

Turnover. Monthly numbers, by division, main industrial groupings and market. Unadjusted series. 2005=100123
July 201812-monthly change in per cent
May 2018 / May 2017June 2018 / June 2017July 2018 / July 2017
1The Figures can be revised twice after published, but no later than 6 months after the initial publishing.
2Total turnover based on grossed up sample figures.
3The levels of aggregation refers to the Standard Industrial Classification (SIC2007). See NOS D383 for further details.
Extraction, mining, manufacturing and electricity137.816.019.027.6
Domestic market133.810.511.513.5
Export market140.821.326.440.1
 
Extraction and related services149.226.534.649.5
Domestic market259.814.017.521.0
Export market136.530.139.657.4
 
Manufacturing, mining and quarrying131.012.69.711.0
Domestic market120.313.47.76.0
Export market146.711.312.617.7
 
Mining and quarrying118.9-2.9-8.0-1.2
Domestic market96.5-4.8-17.0-6.4
Export market160.70.07.55.1
 
Manufacturing131.312.810.111.3
Domestic market120.813.88.36.2
Export market146.511.512.718.0
 
Electricity, gas and steam124.9-2.721.934.6
Domestic market120.7-2.321.733.7
Export market256.0-13.928.750.1
 
Main industrial groupings:
Intermediate goods160.23.85.86.2
Domestic market166.54.1-1.14.5
Export market152.53.418.18.7
 
Capital goods99.126.716.46.7
Domestic market89.627.628.34.2
Export market115.725.31.910.5
 
Consumer goods154.411.63.59.0
Domestic market153.616.73.17.7
Export market157.0-2.04.813.4
 
Energy goods134.223.237.253.7
Domestic market119.44.533.938.0
Export market139.730.838.559.7

Table 3 
Turnover. Monthly numbers, by division, main industrial groupings and market. Seasonally adjusted series. 2005=100

Turnover. Monthly numbers, by division, main industrial groupings and market. Seasonally adjusted series. 2005=10012
July 2018Monthly change in per cent
May 2018 / April 2018June 2018 / May 2018July 2018 / June 2018
1Total turnover based on grossed up sample figures.
2The levels of aggregation refer to the Standard Industrial Classification (SIC2007). See NOS D383 for further details.
Extraction, mining, manufacturing and electricity154.3-0.33.52.7
Domestic market159.8-0.31.91.3
Export market149.21.25.21.6
 
Extraction and related services155.1-1.13.56.2
Domestic market260.51.2-0.12.4
Export market143.2-0.85.84.8
 
Manufacturing, mining and quarrying151.52.41.6-1.2
Domestic market144.72.6-0.7-1.4
Export market159.44.13.5-3.2
 
Mining and quarrying124.35.72.0-7.1
Domestic market102.317.6-2.7-5.7
Export market161.30.110.4-10.4
 
Manufacturing152.02.41.6-1.2
Domestic market145.42.3-0.7-1.6
Export market159.44.13.3-3.0
 
Electricity, gas and steam164.7-9.813.98.8
 
Main industrial groupings:
Intermediate goods175.41.31.6-1.9
Domestic market187.61.9-1.1-1.6
Export market159.3-0.87.5-4.9
 
Capital goods129.85.16.1-10.2
Domestic market129.0-2.49.0-8.5
Export market132.322.0-3.8-9.0
 
Consumer goods175.5-1.1-1.91.2
Domestic market169.52.3-4.81.9
Export market194.9-3.43.30.7
 
Energy goods143.2-2.46.63.8
Domestic market141.9-7.815.32.1
Export market139.7-0.64.72.0

Table 4 
Turnover. Period numbers by division, main industrial groupings and market. Seasonally adjusted series. 2005=100

Turnover. Period numbers by division, main industrial groupings and market. Seasonally adjusted series. 2005=10012
Three-month averageThree-month change in per cent
May 2018 - July 2018November 2017 - January 2018 / August 2017 - October 2017February 2018 - April 2018 / November 2017 - January 2018May 2018 - July 2018 / February 2018 - April 2018
1Total turnover based on grossed up sample figures.
2The levels of aggregation refer to the Standard Industrial Classification (SIC2007). See NOS D383 for further details.
Extraction, mining, manufacturing and electricity149.96.34.46.1
Domestic market157.43.55.22.7
Export market145.28.54.410.2
 
Extraction and related services147.510.38.69.6
Domestic market256.57.85.12.8
Export market136.311.210.012.2
 
Manufacturing, mining and quarrying152.02.8-0.34.5
Domestic market146.5-0.12.73.5
Export market161.15.6-2.87.0
 
Mining and quarrying129.87.70.3-1.5
Domestic market107.4-4.0-0.2-8.0
Export market168.232.0-2.45.4
 
Manufacturing152.52.7-0.14.7
Domestic market147.3-0.12.93.5
Export market161.05.2-2.87.0
 
Electricity, gas and steam149.711.212.81.0
 
Main industrial groupings:
Intermediate goods176.71.0-0.11.3
Domestic market190.40.31.0-1.5
Export market160.92.0-0.25.1
 
Capital goods136.83.61.25.6
Domestic market133.10.42.75.8
Export market143.02.1-4.111.4
 
Consumer goods175.31.12.32.2
Domestic market170.2-0.61.43.4
Export market192.04.82.21.9
 
Energy goods136.815.49.88.7
Domestic market133.818.915.71.1
Export market135.812.67.59.7

Table 5 
Turnover. Monthly numbers, by division, main industrial groupings and market. Calendar adjusted series. 2005=100

Turnover. Monthly numbers, by division, main industrial groupings and market. Calendar adjusted series. 2005=100123
July 201812-monthly change in per cent
May 2018 / May 2017June 2018 / June 2017July 2018 / July 2017
1The levels of aggregation refers to the Standard Industrial Classification (SIC2007). See NOS D383 for further details.
2Adjusted for working-days and for public holidays in Norway.
3Total turnover based on grossed up sample figures.
Extraction, mining, manufacturing and electricity137.916.320.226.1
Domestic market134.310.812.012.6
Export market140.621.328.538.0
 
Extraction and related services149.026.634.549.4
Domestic market259.414.017.521.0
Export market136.330.139.757.4
 
Manufacturing, mining and quarrying131.212.512.28.6
Domestic market120.613.49.34.5
Export market146.611.316.413.9
 
Mining and quarrying118.9-2.9-5.2-4.1
Domestic market96.2-4.8-15.1-8.4
Export market161.20.011.71.1
 
Manufacturing131.412.812.68.9
Domestic market121.113.89.94.8
Export market146.311.516.414.2
 
Electricity, gas and steam126.5-0.118.634.7
Domestic market122.20.618.133.7
Export market260.9-30.459.250.1
 
Main industrial groupings:
Intermediate goods160.14.77.53.6
Domestic market166.33.51.72.1
Export market152.76.918.05.2
 
Capital goods99.526.617.35.9
Domestic market89.927.627.15.0
Export market115.625.35.96.3
 
Consumer goods154.512.96.25.1
Domestic market153.917.56.33.8
Export market156.61.15.39.3
 
Energy goods134.123.237.253.8
Domestic market119.44.533.837.9
Export market139.530.838.459.6

Table 6 
Turnover. Annual numbers, by division, main industrial groupings and market. Unadjusted series. NOK million

Turnover. Annual numbers, by division, main industrial groupings and market. Unadjusted series. NOK million123
201520162017Yearly change in per cent
2016 / 20152017 / 2016
1The Figures can be revised twice after published, but no later than 6 months after the initial publishing.
2Total turnover based on grossed up sample figures.
3The levels of aggregation refer to the Standard Industrial Classification (SIC2007). See NOS D383 for further details.
Extraction, mining, manufacturing and electricity1 566 3751 401 9811 477 728-10.55.4
Domestic market762 883716 495705 568-6.1-1.5
Export market803 490685 487772 159-14.712.6
 
Extraction and related services615 387494 519540 701-19.69.3
Domestic market162 640126 948109 889-21.9-13.4
Export market452 745367 567430 809-18.817.2
 
Manufacturing, mining and quarrying831 575767 787792 448-7.73.2
Domestic market484 935455 311456 911-6.10.4
Export market346 639312 476335 537-9.97.4
 
Mining and quarrying13 29811 51814 712-13.427.7
Domestic market7 7016 3089 079-18.143.9
Export market5 5985 2105 631-6.98.1
 
Manufacturing818 275756 268777 737-7.62.8
Domestic market477 234449 003447 833-5.9-0.3
Export market341 040307 266329 905-9.97.4
 
Electricity, gas and steam119 414139 675144 58017.03.5
Domestic market115 309134 234138 76816.43.4
Export market4 1105 4415 80932.46.8
 
Main industrial groupings:
Intermediate goods437 165404 349404 730-7.50.1
Domestic market273 460255 561246 727-6.5-3.5
Export market163 705148 787158 000-9.16.2
 
Capital goods246 500191 370175 584-22.4-8.2
Domestic market147 028107 253102 187-27.1-4.7
Export market99 47384 11673 395-15.4-12.7
 
Consumer goods219 577237 221240 9328.01.6
Domestic market160 911172 029176 2846.92.5
Export market58 66465 19164 64711.1-0.8
 
Energy goods663 132569 040656 483-14.215.4
Domestic market181 485181 651180 3680.1-0.7
Export market481 648387 390476 117-19.622.9

About the statistics

The goal of the survey is to monitor the level and development of the turnover within oil and gas, manufacturing, mining and electricity supply distributed on the domestic and export market.

Definitions

Definitions of the main concepts and variables

Local unit (establishment): An enterprise or part of an enterprise that is located in one particular place and can be identified geographically.

Enterprise: The smallest combination of legal units that is an organisational unit producing goods or services and that benefits from a certain degree of autonomy in decision-making.

Altinn: The Reporting portal for delivery of figures electronically to Statistics Norway. 

NACE: Standard for industrial classification used by EUROSTAT. Based on the UN's international standard for industrial classification, ISIC Rev. 3.

Standard Industrial Classification (SIC) : The standard is primarily a statistical standard. It forms the basis for classifying units according to main activity in the Central Register of Establishments and Enterprises (CRE). The use of common standards is essential in enabling the comparison and analysis of statistical data at national/international level and over time. The standard is identical to NACE. However, a fifth figure (subclass) is added to the standard to create a national Norwegian level.

Processing level: The most detailed level of the statistics.

Seasonally-adjusted figures: Time series for which calendar and seasonal effects have been removed. X12-ARIMA is used to calculate these figures.

Unadjusted figures: Raw data figures with primary information from the respondent.

Value index: Statistically comparable figures that are used, for example, to throw light on the development of turnover over time.

Imputation: An estimated value for a missing observation.

Accrual: A method that is used to adjust a reported value (total turnover and self-produced turnover) in order to correspond to the calendar month in cases where submitted figures are given for a different period.

Period of turnover: The period for which the turnover is reported.

Period of shutdown: Shutdown in production due to vacation, strike, maintenance etc.

Total turnover for the establishment: Total turnover (including self-production), liable to duty, excl. VAT.

Turnover, self-production: Total turnover of self-production, liable to duty and duty-free sales, excl. VAT.

Standard classifications

From 2002 to 2008, the survey was classified according to the Standard Industrial Classification 2002 (SIC2002), which is a Norwegian adaptation of NACE Rev.1 (EUROSTAT). As from January 2009, SIC2002 has been replaced by the Standard Industrial Classification 2007 (SIC2007), which is a Norwegian adaptation of NACE Rev. 2 (EUROSTAT). SIC2007 forms the basis for coding units according to principal activity in the Central Register of Establishments and Enterprises. The use of common standards is essential in enabling the comparison and analysis of statistical data at national/international level and over time.

The survey is also classified according to EUROSTAT's end-use categories (Main Industrial Groupings, MIG). The end-use categories (MIGs) are based on the 3-digit level industrial groupings in SIC2007. Six end-use categories are included in the survey:

MIG Code

Description

E1

Intermediate goods

E2

Capital goods

E3

Consumer durables

E4

Consumer non-durables

E5

Consumer goods (E3+E4)

E6

Energy goods


The following table summarises the most important industries included in the different end-use categories:

MIG

Main industries included

Intermediate goods

Services in oil and gas extraction, wood and wood products, Pulp, paper and paper products, Basic chemicals and Basic metals

Capital goods

Machinery and equipment and Building of ships, boats and oil platforms, Repair and installation of machinery

Consumer durables

Manufacture of furniture

Consumer non-durables

Food products, Printing and reproduction, Basic pharmaceuticals

Consumer goods (E3+E4)

Manufacture of furniture, Food products, Printing and reproduction, Basic pharmaceuticals

Energy goods

Mining of coal, Oil and gas extraction, Refined petroleum products and Electricity, gas and steam supply


The objective of this classification is to provide an activity breakdown of NACE, which is more detailed. The classification of the different units is based on the application of the produced products. It should be noted that the MIGs are not comparable in size, in particular the consumer durables heading is smaller than the others. For a complete description of industries covered in each MIG, see Commission regulation (EC) No 656/2007 .

Administrative information

Name and topic

Name: Turnover in oil and gas, manufacturing, mining and electricity supply
Topic: Energy and manufacturing

Next release

Responsible division

Division for business cycle statistics

Regional level

National level only

Frequency and timeliness

The statistics on turnover has a monthly frequency and is released about 35 days after the reference month

International reporting

The statistics on turnover is reported to EUROSTAT on a monthly basis.

Microdata

Non-revised and revised micro data are stored in accordance with Statistics Norway's guidelines for storing computer files.

Background

Background and purpose

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. Turnover data have been collected since February1996,however the survey has existed in its current form since May 2002. As from March 2006, all results are divided by market. A new method for inflating sample data to population level (see paragraph 3.6) was implemented in January 2009. Results dating back to 2006 are recalculated using this method.

As from January 2009, all results will refer to SIC2007 (see paragraph 4.2). The historical series are recalculated according to this version of SIC, and results for total turnover dating back to 1998 are available in the Statbank database. Results classified by market are available back to 2000. Historical series based on SIC2002 (see paragraph 6.1) also remain available for the period 1998 to 2008.

The survey is financed exclusively by government appropriations.

Users and applications

The results are used in internal controls in other economic trend surveys such as Index of production and Quarterly national accounts . Other users include financial and analytical institutions and, to some extent, public institutions (the Ministry of Finance and Norges Bank among others).

Equal treatment of users

No external users have access to the statistics and analyses before they are published and accessible simultaneously for all users on ssb.no at 08:00. Prior to this, a minimum of three months' advance notice is given inthe Statistics Release Calendar. This is one of Statistics Norway’s key principles for ensuring that all users are treated equally.

Coherence with other statistics

The Statistics on turnover is a leading indicator of future turnover of oil and gas extraction, mining and quarrying, manufacturing, electricity and gas supply, and is one of several indicators that monitor the performance of the economy. The correlation with the following statistics is utilised for control purposes:

The Statistics on turnover. Oil and gas extraction, mining and quarrying, manufacturing, electricity and gas supply and the Index of production have joint data collection.

Legal authority

The Statistics Act of 16 June 1989, §§ 2-1, 2-2 and 2-3

EEA reference

Council Regulation (EC) no. 1165/98 of 19 May 1998 concerning short-term statistics

Commission Regulation 586/2001

Commission and Council Regulation 1158/2005

Commission and Council Regulation 1983/2005

Commission Regulation 1503/2006

Commission Regulation 656/2007

Commission Regulation 472/2008

Production

Population

The population covers all establishments in mining and quarrying (SIC 05, SIC 07-08, SIC 09.9), oil and gas extraction (SIC 06, 09.1), manufacturing (SIC 10-33) and electricity and gas supply (SIC 35), see Standard Industrial Classification 2007 (SIC2007) . The Central Register of Establishments and Enterprises defines the population, and the establishment is the observation unit in the survey. (See paragraph 4.1 for a complete definition of establishment and enterprise.)

Data sources and sampling

The survey uses turnover data collected by questionnaire from the units included in the sample, in addition to information from the Central Register of Establishments and Enterprises. Annual Manufacturing statistics , monthly statistics on External trade in goods and annual Electricity statistics are used to estimate the total turnover. Monthly statistics on External trade in goods are used for classifying results by market.

The sample consists of 1780 establishments (January 2009). This includes all establishments with 100 employees or more, or establishments with a turnover of at least 10 per cent of the publishing level. The remaining units are drawn based on stratification and optimal allocation, proportional to the size of the unit measured by the number of employees (PPS). The sample does not include establishments with less than 10 employees.

Strata (employees)

Sample

Distribution

Method

1 - (> 100)

470

All included

Cut-off

2 - (50-99)

410

31 per cent of the remaining 1310 establishments.

PPS

3 - (20-49)

550

41 per cent of the remaining 1310 establishments.

PPS

4 - (10-19)

350

27 per cent of the remaining 1310 establishments.

PPS

5 - (< 10)

0

None included.

Cut-off

 

&#8721; 1780

 

Collection of data, editing and estimations

The survey is based on data collected by questionnaires. The questionnaires are returned electronically via Altinn.  The questionnaire is sent at the end of the month. The deadline for returning the questionnaire is normally the 15th of the following month. Establishments registered with an e-mail address in Altinn are notified by e-mail when the questionnaire is available on the internet.

The establishment's local office normally fills in the questionnaire, but in some cases the head office reports data for several units. Establishments that fail to return the questionnaire receive a reminder within a week of the deadline. A new deadline of seven days is set. Establishments that still fail to return the questionnaire receive a second reminder and a compulsory fine if they do not return the questionnaire within five days.

The questionnaires are downloaded from the Internet, and the data are automatically checked for duplicates and errors in totals. The figures are revised on the basis of a revision program (for example errors regarding reporting in NOK million or major deviations from previous reported figures). Where there are considerable deviations, the establishment is contacted. In cases of extreme deviations, further revisions are carried out.

Annual Manufacturing statistics , monthly statistics on External trade in goods and annual Electricity statistics are used to estimate the total turnover.

Mining and quarrying and Manufacturing: About 70 per cent of the total turnover is covered by the sample. In addition, data from the annual Manufacturing statistics is used to estimate the turnover for the establishments not covered by the sample. This is done by a stratified ratio estimator.

Extraction of oil and natural gas: Total turnover for the industry Extraction and related services is estimated by using export figures from monthly statistics on External trade in goods. Total export turnover is inflated to total turnover by using a factor giving the home market share. Data from the Norwegian Petroleum Directorate (NPD) are used to update the home market share on an annual basis (January).

Electricity and gas supply: Total turnover is estimated as the sum of sample figures for the industry, multiplied with a fixed factor. The factor is calculated as the sample coverage of turnover in the annual Electricity statistics. This factor is updated in January each year.

Division of total turnover by market: Administrative data from customs declarations are used for classifying results by market. The ratio between estimated total turnover and the sum of exports for a particular industry determines the distribution by home and export market. The product classification in the statistics on External trade in goods is used to aggregate export turnover for each industry. The home market share is defined as the difference between total turnover and export turnover.

Seasonal adjustments: The turnover will normally vary from month to month in several industries due to factors such as the length of month, number of working days and holidays such as Easter. Pre-adjusted series are calculated and published in order to deal with some of these effects (Calendar adjusted series).

Improved method from 2013: The new method takes into account the Norwegian calendar and thereby improving the quality of the seasonally adjusted results. The change has been applied from the July 2013 publishing, and concerns the pre-treatment method (calendar adjustment). The new method adjusts for different effects of working-days in each industry and distinctively Norwegian effects in relation to moving holidays (Easter, Pentecost, and Ascension Day). The new method also adjusts for fixed Norwegian public holidays (1 January, 1 and 17 May) and for the Christmas holiday (24-26 December).

Seasonal effects are also corrected for and seasonally adjusted figures are published. These adjustments are carried out by X12-Arima, and multiplicative forms are the main method. Aggregated series are adjusted directly. Routines are updated on a yearly basis.

The index for oil and gas extraction, mining and quarrying, manufacturing and electricity, gas and steam supply (total index) is adjusted indirectly as a result of the underlying main aggregated series. Macro-controls are carried out each month based on results from the index of production, producer price statistics, new orders and the business tendency survey. A part of these macro-controls is an evaluation of seasonally adjusted figures for all published aggregates.

Seasonal adjustment

Not relevant

Confidentiality

Confidential micro data: According to § 2-4 of the Statistics Act, collected data are subject to secrecy and are to be kept or destroyed in a secure manner. Any use of the data must be in accordance with the rules set out by the Data Inspectorate.

Time series that are not to be published: The publication of data is subject to the provisions of § 2-6 of the Statistics Act. The main rule is that data should not be published if they can be traced back to the respondent, i.e. figures for which less than three respondents make up the foundation for a cell in the table, figures where one respondent represents more than 90 per cent of the total value, or figures where two respondents represent at least 95 per cent of the total value.

Unpublished data: Revised data that are not published are subject to secrecy. This means they are unavailable to users without explicit approval. Such agreements only apply to internal users.

Comparability over time and space

As from January 2009, has the Standard Industrial Classification 2002 (SIC2002) been replaced by the Standard Industrial Classification 2007 (SIC 2007) (see paragraph 2.1). The historical series based on this new version of SIC have been recalculated back to 1998. For the years 1998 to 2005, a macro-approach is used for back-casting the time series, and for the years 2006-2008 the survey is recalculated by a detailed re-working of individual data (micro-approaches). Users of the data must ensure they use results based on the same version of SIC when making comparisons over time. When looking at changes from January 1998 to December 2008 either the series based on SIC2002 or the series based on SIC2007 must be used.

Historical series based on SIC2002 remain available in the Statbank database under Completed time series. However, as from January 2009, only series based on SIC2007 will be continued. To get an overview of possible changes in industrial groupings, see the article on new Standard for Industrial Classification .

Accuracy and reliability

Sources of error and uncertainty

Measurement errors are caused by the questionnaire or the respondents internal system for obtaining the data. Examples are ambiguous questions, misunderstood questions or erroneous data from the respondents. In the Statistics on turnover, errors in reported figures may originate from misunderstandings of the concept of turnover or the definition of the main variables used in the survey. Unambiguous guidelines and definitions are therefore emphasised. The use of incorrect units of measurement can occur since the figures should be reported in NOK million. This type of error will become evident during the revision of the data.

Processing errors can occur when Statistics Norway processes the data. Typical examples are misinterpretations of answers (1 may be interpreted as 7 and so on) or that correct answers for some reason are assumed to be false and corrected. Paper questionnaires are optically read with automatic verification and transmission to an electronic medium. The current techniques for optical reading are of a high quality, and few errors are found in this phase of the production. The introduction of Altinn also helpes to reduce such errors, as data from electronic questionnaires are loaded directly into the system. Questionnaires that are not verified by the optical reading are dealt with manually. Thus there is room for human error, but considerable deviations will normally become evident during the revision of the data.

Errors of non-response refer to errors that either occur due to missing questionnaires or due to blank boxes in the questionnaires. The response rate when the deadline expires lies around 96 per cent (2008). Critical units, i.e. units that have a considerable impact on the results on a detailed level aggregation (2-digit NACE), are contacted by telephone. Missing questionnaires are mainly imputed automatically, based on previous reported figures (cold-deck method).

Sampling errors refer to uncertainties that occur in sample surveys as opposed to full counts. The Statistics on turnover covers about 70 per cent of the turnover in the population. In order to ensure a high degree of relevance at the lowest cost possible, great effort is put into including all large units in the population in the sample. Uncertainty due to sampling errors is estimated when inflating sample data to population level, and the result is compared with figures from previous months. The coefficient of variation for the aggregate Manufacturing and mining and quarrying is estimated to be 1.6 (January 2009).

Coverage errors refer to errors in registers that define the population, in this case the Central Register of Establishments and Enterprises. As a result of such errors, units may be incorrectly included in or excluded from the population. Other problems are related to delays in the update of the registers and units that are incorrectly classified. From experience it is known that a limited share of the population units is incorrectly classified. This is usually due to misleading or insufficient information at a certain time. Calculations of the size and significance of such errors have not been carried out. However, such errors are not considered to be greater than for other quantitative short-term statistics.

Modelling errors are mainly related to problems with the seasonal adjustment of time series. Such problems are caused by deviation from the conditions that form the basis for the model used. Typical problems are related to public holidays such as Christmas and Easter. X12-ARIMA generates a number of indicators that are used to evaluate the quality of the seasonal adjustment. These indicators have identified a stable seasonal pattern.

Revision

Not relevant

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

Why seasonally adjust these statistics?

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

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

Seasonal adjustment

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

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. 

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.

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

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

Special cases

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.

Posting 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.

Relevant documentation