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Index of industrial production


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Key figures

0.5 %

growth in manufacturing output compared with last three-month period

Index of production. Changes in per cent and weights
Index of production. Seasonally adjustedIndex of production. Calendar adjusted1Weights2
Monthly changeThree-month changeTwelve-month change
April 2017 / March 2017February 2017 - April 2017 / November 2016 - January 2017April 2017 / April 2016
1Adjusted for working-days and for public holidays in Norway.
2The weights are updated annually, and are valid for the entire year.
Extraction, mining, manufacturing and elec0.71.20.9100.0
Extraction and related services-0.4-0.3-1.167.1
Mining and quarrying-6.9-3.2-6.20.7
Food, beverage and tobacco0.
Refined petro., chemicals, pharmac.-8.23.310.03.7
Basic metals5.
Machinery and equipment2.8-1.9-11.32.7
Ships, boats and oil platforms12.
Electricity, gas and steam4.9-0.2-1.45.7

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Table 1 
Index of production. Monthly numbers, by division and main industrial groupings. Seasonally adjusted series. 2005=100

Index of production. Monthly numbers, by division and main industrial groupings. Seasonally adjusted series. 2005=10012
April 2017Monthly change in per cent
February 2017 / January 2017March 2017 / February 2017April 2017 / March 2017
1The weights are value added at factor prices.
2The levels of aggregation refers to the Standard of Industrial Classification (SIC2007). See NOS D383 for further details
Extraction, mining, manufacturing and electricity87.3-
Extraction and related services74.2-0.80.9-0.4
Extraction of crude petroleum54.62.12.2-1.8
Extraction of natural gas116.1-2.7-0.32.7
Support act. petroleum extraction119.91.1-0.20.4
Manufacturing, mining and quarrying114.7-0.3-1.01.5
Mining and quarrying118.92.3-8.3-6.9
Food, beverage and tobacco104.60.7-0.60.4
Food products111.21.3-0.70.8
Textiles, wearing apparel, leather73.32.7-3.112.3
Wood and wood products78.4-1.1-1.8-4.4
Paper and paper products56.8-1.1-0.52.7
Printing, reproduction77.81.5-2.87.8
Refined petro., chemicals, pharmac.
Basic chemicals112.3-1.2-1.11.8
Rubber, plastic and mineral prod.91.6-0.9-0.43.5
Basic metals79.72.2-5.35.3
Non-ferrous metals89.71.8-0.8-4.0
Fabricated metal products127.4-0.7-2.44.3
Computer and electrical equipment128.7-2.4-1.01.2
Machinery and equipment157.50.4-2.72.8
Ships, boats and oil platforms172.41.3-3.912.8
Transport equipment n.e.c128.61.3-2.21.6
Repair, installation of machinery150.20.1-0.33.2
Furniture and manufacturing n.e.c.58.2-3.3-0.90.9
Electricity, gas and steam109.41.1-3.74.9
Main indstrial groupings:
Intermediate goods102.5-
Capital goods155.70.3-2.44.4
Consumer goods99.40.8-0.7-1.0
Durable consumer goods48.0-8.9-0.60.4
Non-durable consumer goods107.21.5-0.5-1.8
Energy goods77.3-0.9-0.32.5

Table 2 
Index of production. Period numbers, by division and main industrial groupings. Seasonally adjusted. 2005=100

Index of production. Period numbers, by division and main industrial groupings. Seasonally adjusted. 2005=10012
Three-month averageThree-month change in per cent
February 2017 - April 2017August 2016 - October 2016 / May 2016 - July 2016November 2016 - January 2017 / August 2016 - October 2016February 2017 - April 2017 / November 2016 - January 2017
1The weights are value added at factor prices.
2The levels of aggregation refers to the Standard of Industrial Classification (SIC2007). See NOS D383 for further details.
Extraction, mining, manufacturing and electricity86.9-
Extraction and related services74.2-3.95.1-0.3
Extraction of crude petroleum54.9-1.83.5-1.8
Extraction of natural gas114.1-9.410.11.7
Support act. petroleum extraction119.6-5.7-1.9-3.2
Manufacturing, mining and quarrying113.9-
Mining and quarrying128.63.7-2.7-3.2
Food, beverage and tobacco104.5-0.2-0.81.6
Food products110.9-1.1-0.62.5
Textiles, wearing apparel, leather68.7-
Wood and wood products81.3-2.43.1-2.9
Paper and paper products55.90.5-1.81.5
Printing, reproduction74.8-
Refined petro., chemicals, pharmac.
Basic chemicals111.4-5.511.7-0.8
Rubber, plastic and mineral prod.89.7-0.9-0.1-1.4
Basic metals78.4-
Non-ferrous metals92.4-
Fabricated metal products124.9-2.30.4-1.4
Computer and electrical equipment128.1-4.99.1-2.1
Machinery and equipment156.1-7.70.6-1.9
Ships, boats and oil platforms161.4-
Transport equipment n.e.c128.2-
Repair, installation of machinery147.2-
Furniture and manufacturing n.e.c.58.0-1.71.0-3.3
Electricity, gas and steam107.3-2.71.2-0.2
Main indstrial groupings:
Intermediate goods101.1-3.51.4-1.7
Capital goods152.6-6.73.0-1.3
Consumer goods100.32.3-0.82.3
Durable consumer goods48.04.13.9-5.3
Non-durable consumer goods108.72.6-0.72.5
Energy goods76.1-

Table 3 
Index of production. Period numbers, by division and main industrial groupings. Calendar adjusted series. 2005=100

Index of production. Period numbers, by division and main industrial groupings. Calendar adjusted series. 2005=100123
April 2017Twelve-month change
February 2017 / February 2016March 2017 / March 2016April 2017 / April 2016
1The weights are value added at factor prices.
2The levels of aggregation refers to the Standard of Industrial Classification (SIC2007). See NOS D383 for further details.
3Adjusted for working-days and for public holidays.
Extraction, mining, manufacturing and electricity85.
Extraction and related services75.3-0.41.8-1.1
Extraction of crude petroleum55.
Extraction of natural gas118.7-0.9-0.6-1.4
Support act. petroleum extraction115.9-10.6-3.0-13.8
Manufacturing, mining and quarrying108.6-1.1-3.30.2
Mining and quarrying114.920.2-9.6-6.2
Food, beverage and tobacco97.70.1-0.30.3
Food products103.71.4-0.42.2
Textiles, wearing apparel, leather69.
Wood and wood products73.74.6-0.2-6.0
Paper and paper products52.6-
Printing, reproduction73.91.0-3.97.3
Refined petro., chemicals, pharmac119.815.323.910.0
Basic chemicals110.1-0.4-0.24.8
Rubber, plastic and mineral prod.88.0-4.4-5.81.4
Basic metals80.32.1-5.31.5
Non-ferrous metals90.43.10.3-5.9
Fabricated metal products119.2-3.6-6.9-0.5
Computer and electrical equipment120.3-1.1-8.51.9
Machinery and equipment148.1-13.6-16.9-11.3
Ships, boats and oil platforms158.5-3.3-10.81.7
Transport equipment n.e.c121.
Repair, installation of machinery144.5-11.6-15.2-5.2
Furniture and manufacturing n.e.c.54.1-5.3-12.9-2.0
Electricity, gas and steam102.2-4.9-10.6-1.4
Main indstrial groupings:
Intermediate goods98.1-5.7-8.1-0.3
Capital goods146.1-8.9-11.7-5.9
Consumer goods92.
Durable consumer goods45.1-4.1-10.32.5
Non-durable consumer goods100.
Energy goods78.00.9-0.63.4

Table 4 
Index of production. Period numbers, by division and main industrial groupings. Unadjusted series. 2005=100

Index of production. Period numbers, by division and main industrial groupings. Unadjusted series. 2005=10012
April 2017Twelve-month change
February 2017 / February 2016March 2017 / March 2016April 2017 / April 2016
1The weights are value added at factor prices.
2The levels of aggregation refers to the Standard of Industrial Classification (SIC2007). See NOS D383 for further details.
Extraction, mining, manufacturing and electricity83.1-4.03.3-5.1
Extraction and related services75.3-3.91.8-1.1
Extraction of crude petroleum55.3-
Extraction of natural gas118.7-4.5-0.6-1.4
Support act. petroleum extraction115.9-10.6-3.0-13.8
Manufacturing, mining and quarrying99.9-4.411.0-15.6
Mining and quarrying106.517.42.1-22.0
Food, beverage and tobacco91.1-3.99.4-11.5
Food products96.8-2.910.2-10.4
Textiles, wearing apparel, leather61.57.827.0-6.7
Wood and wood products64.50.527.0-29.0
Paper and paper products50.5-2.911.9-4.4
Printing, reproduction69.1-2.46.5-5.3
Refined petro., chemicals, pharmac.116.411.730.92.1
Basic chemicals110.1-1.9-0.24.8
Rubber, plastic and mineral prod.79.6-6.113.8-18.3
Basic metals77.5-2.31.1-6.2
Non-ferrous metals90.4-0.50.3-5.9
Fabricated metal products106.2-6.712.8-20.7
Computer and electrical equipment107.2-3.912.0-20.8
Machinery and equipment131.0-17.51.7-30.4
Ships, boats and oil platforms144.2-7.24.3-15.7
Transport equipment n.e.c108.42.926.3-16.0
Repair, installation of machinery130.6-15.5-1.1-21.4
Furniture and manufacturing n.e.c.47.7-10.311.2-25.6
Electricity, gas and steam102.2-6.5-10.6-1.4
Main indstrial groupings:
Intermediate goods92.9-5.44.3-13.7
Capital goods131.4-11.94.6-23.4
Consumer goods85.12.321.5-11.4
Durable consumer goods40.0-9.113.8-24.0
Non-durable consumer goods92.33.322.1-10.3
Energy goods77.2-

Table 5 
Index of production. Weights, by division and main industrial groupings

Index of production. Weights, by division and main industrial groupings12
1The weights are value added at factor prices.
2The levels of aggregation refers to the Standard of Industrial Classification (SIC2007). See NOS D383 for further details
Extraction, mining, manufacturing and electricity1 000.01 000.01 000.01 000.01 000.0
Extraction and related services734.5719.2718.0714.4670.8
Extraction of crude petroleum376.1366.7350.9376.3302.9
Extraction of natural gas310.3303.0313.6286.5310.7
Support act. petroleum extraction48.149.653.651.657.2
Manufacturing, mining and quarrying206.5233.1226.6236.5272.3
Mining and quarrying5.
Food, beverage and tobacco38.040.842.747.454.0
Food products34.136.738.543.349.0
Textiles, wearing apparel, leather2.
Wood and wood products8.
Paper and paper products1.
Printing, reproduction4.
Refined petro., chemicals, pharmac.20.219.720.324.037.3
Basic chemicals7.87.07.710.614.4
Rubber, plastic and mineral prod.
Basic metals10.810.78.712.016.8
Non-ferrous metals7.
Fabricated metal products18.121.720.419.120.5
Computer and electrical equipment15.218.518.519.319.1
Machinery and equipment27.631.928.729.926.9
Ships, boats and oil platforms20.623.822.618.319.7
Transport equipment n.e.c2.
Repair, installation of machinery13.918.019.319.418.8
Furniture and manufacturing n.e.c.
Electricity, gas and steam59.147.755.449.256.8
Main indstrial groupings:
Intermediate goods117.5127.7126.9133.3158.9
Capital goods80.896.192.489.489.3
Consumer goods51.854.157.161.975.4
Durable consumer goods4.
Non-durable consumer goods47.
Energy goods750.0722.0723.6715.4676.4

About the statistics

The primary goal of the Index of Industrial Production is to monitor the development of added value within oil and gas extraction, manufacturing, mining & quarrying, and electricity supply. The indicators used to measure value added are physical output and number of hours worked.


Definitions of the main concepts and variables

Long-term indices: Long-term indices are published together with reviewed figures on the Internet for the overall index, aggregates, main activities and main industrial groupings.

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.

NACE: Standard for industrial classification used by EUROSTAT. Based on the UN's international standard for industrial classification, ISIC Rev. 4.

Standard Industrial Classification 2007 (SIC2007) , which is a Norwegian adaptation of EUROSTAT's NACE Rev. 2. SIC2007 forms the basis for coding units according to principal activity in the Central Register of Establishments and Enterprises.

Processing level: The most detailed level of the statistics.

Unadjusted figures (original series): Raw data figures with primary information from the respondent.

Calender adjusted series  (pre-adjusted series): Adjusted for working-days, moving holidays and fixed public holidays in Norway.

Seasonally adjusted figures: Pre-adjusted series for which seasonal effects have been removed (included length of month). X12-ARIMA is used to calculate these figures.

Imputation: An estimated value for a missing observation.

Standard classifications

The survey is classified according to the Standard Industrial Classification 2007 (SIC2007). This is a Norwegian adaptation of NACE Rev. 2. SIC2007 forms the basis for classifying units according to principal activity in the CRE. The use of common standards is essential in order to enable 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 SIC 2007. Six end-use categories are included in the survey:

MIG Code



Intermediate goods


Capital goods


Consumer durables


Consumer non-durables


Consumer goods (E3+E4)


Energy goods

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


Main industries included

Intermediate goods

Support activities for oil and gas extraction, wood and wood products, Pulp, paper and paper products, Basic chemicals and Basic metals

Capital goods

Fabricated metal products, Machinery and equipment and Building of ships, oil platforms and modules

Consumer durables

Production of furniture

Consumer non-durables

Food products, Printing and reproduction and Pharmaceuticals

Consumer goods (E3+E4)

Food products, Printing and reproduction, Pharmaceuticals and Production of furniture

Energy goods

Oil and gas extraction, Refined petroleum products and Electricity, gas and steam supply

For a complete description of industries covered in each MIG, see the Commision Regulation 656/2007. .

The objective of this classification is to provide an activity breakdown of NACE, which is an intermediate level between the Sections (for example C: Manufacturing) and the Subsections (for example CA: Manufacture of food products, beverages and tobacco). 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, and the consumer durables heading in particular is smaller than the others.

Administrative information

Name and topic

Name: Index of industrial production
Topic: Energy and manufacturing

Next release

Responsible division

Division for Manufacturing and R&D statistics

Regional level

National level only

Frequency and timeliness

Monthly. Published approximately 35 days after the end of the relevant month.

International reporting

The Index of Industrial Production is reported to EURO STAT on a monthly basis.


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


Background and purpose

The Index of Industrial Production is part of a system of short-term statistics compiled to monitor the economy. The primary goal of the Index of Industrial Production is to monitor the development of added value (production minus intermediate consumption) in the industries covered. However, this is quite difficult on a monthly basis, so the index maps out the development of production in oil and gas extraction, mining and quarrying, manufacturing and electricity and gas supply. No calculations are made for the intermediate consumption, which it is assumed develops in the same manner as the production. The survey is financed exclusively by government appropriations.

As from January 2009 all results will refer to Standard Industrial Classification 2007 (SIC2007) . The historical series are recalculated according to this version of SIC, and results dating back to 1995 and 1990 (totals) are available in the Statbank database.

Users and applications

The Index of Industrial Production is used in the quarterly national accounts in Statistics Norway to help calculate the gross domestic product. Added value from the activity covered was approximately 32 per cent of the gross domestic product in 2012. It is also an important input to prognoses on the industrial production carried out by other authorities and organisations in Norway.

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 8 am. 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 Index of Industrial Production is a leading indicator of future production 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 :

is utilised for control purposes. The Statistics on turnover for Oil and gas extraction, mining and quarrying, manufacturing, electricity and gas supply and the Production Volume Index have joint data collection.

Legal authority

The Statistics Act of 16 June 1989 no. 54, §§ 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. Commision Regulation 472/2008.



The population covers all establishments in mining and quarrying (SIC 05, SIC 07-08, SIC 09.9), oil and gas extraction (SIC 06, SIC 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 Definitions for a complete definition of establishment and enterprise.)

Data sources and sampling

The survey uses production data collected by questionnaires from the units included in the sample. Monthly production figures on oil and gas extraction in Norway are transmitted electronically by the Norwegian Petroleum Directorate. Production figures for electricity supply from water resources are provided by the Norwegian Water Resources and Energy Directorate and the Division of Energy and Industrial Production Statistics in Statistics Norway.

The sample includes about 2,100 establishments (2013), which includes all establishments with 100 employees or more, or 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. The sample does not include establishments with less than ten employees.

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 made available on Internet at the end of the relevant month. The deadline for answering the questionnaire is normally the 15th of the following month. Establishments are notified by SMS or e-mail when the questionnaire is available in Altinn. The establishment's local office fills in the questionnaire. Establishments that fail to return the questionnaire receive a compulsory fine within a week of the deadline, whereupon a new deadline of seven days is set.

The questionnaires are downloaded from the Internet, and the data are automatically checked for duplicates. Where there are significant deviations, the establishment is contacted. In cases of extreme deviations, further editing is carried out. Comparisons with the annual industrial statistics are also carried out.

Estimated production values in fixed prices are calculated for the observation units. For the first stage, the indices at group or processing level are estimated by summing up these production values. The values of the reference period are then set in relation to the value of base period 0. The values of the base period are estimated average production values from the previous year. This provides the short-term index for the current month.

First stage of index compilation



Qm = short term index of production value in fixed prices for month m

p0 = price in base year

qm = quantity in reference period

q0 = average monthly quantity in base year

The prices in the calculation of the short-term index are derived from statistics of external trade in goods, indicies of volume and price as well as the producer price index of oil and gas, manufacturing, mining and electricity in Norway. Some prices are also collected directly from the relevant enterprises in Norway.

2005 is the base year in calculation of the short-term index of 2015. The price in base year p0 is from 2014. Previous prices are put forward to 2014 by numbers from the producer price index for oil and gas, manufacturing, mining and electricity in Norway. Some prices are directly updated to 2014-level. 

Production is measured using two types of indicators - reflecting (more or less) the change in gross production/turnover. The indicators are physical output and hours worked. The production is measured by physical output in the following industries (SIC 2007: 05-08, 10-12, 16-17, 19-24, 27.3, 27.5, 31, 32 (ex. 32.5) and 35)). The calculations follow a set of identical observation units over a period of 2 years and an activity classification (SIC 2007). No adjustments are made for quality changes in the relevant products over time.

The production is measured by hours worked in the engineering industries (SIC 2007: 25-27 (ex. 27.3 and 27.5), 28-30, 32.5 and 33)) and in publishing and printing (SIC 2007: 18). The production is also measured by hours worked in support activities from petroleum and natural gas extraction (SIC 2007: 09.1) from 2010 and in manufacturing of textiles, wearing apparel and leather and related products (SIC 2007: 13-15) from 2011.

New projects in industries with hours worked as indicator are intercepted as they start up, as long as the project is carried out by establishments covered by the sample. It is the total number of hours carried out in the survey period that are reported, independent of which project the hours are used on.

Numbers of hours worked from contracted workers are collected from 2011. Numbers of hours worked from contracted workers are included in the calculation of the index of production from 2012.

Numbers of hours worked from the officials are collected from 2014. As of 2015 are these included in the reporting and the calculations.

Change in productivity from year to year is taken into account, by fixed factors, when using the work input as basic information for compiling the index. The use of this type of indicator implies that output changes proportionally with input.

These factors are updated in 2015 and are based on numbers from Annual final national accounts . Value added per houred work in basic value, annual change in volume in per cent from the last 5 years are used.

Second stage of index compilation: Index of Industrial Production at higher aggregation levels

Production indices for the industries at the 4-digit level can be aggregated according to the hierarchical structure of NACE Rev. 1 to indices at higher levels and also to main industrial groupings (See Defenitions). The share of added value of each class in the base year is used for the calculation of the aggregates. Last available value added from structural data are forcasted to base year by numbers from quarterly national accounts. In the case of a chain index, such as the Production Volume Index, the weights are updated annually.



Um,k = unadjusted short-term index aggregated over the underlying sectors, month m

BAs,0 = added value at factor cost, sector s, weight-year 0 (base year)

Us,m,k = unadjusted short-term index of sector k and month m

The calculation applies a Lays Peres formula because the weights are from a base year.

Chaining to long-term indices:

Unadjusted short-term indices will be chained to the long-term index (base 2005) at various levels. This must be done because the bases of the short-term indices and the weights change once a year, and to evaluate the results of the index calculation over time. Chaining to the long-term indices is done using the following formula:




Um,k = short-term index, unadjusted, month m

Um,l = long-term index, unadjusted, month m

Ut-1,l = long-term indices, unadjusted, average last year

Seasonal adjustment

The production output will normally vary from month to month in several industries due to factors such as the length of the 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 (Calender adjusted series).

Since the effect due to the length of month is the same every year, it should not be included in the calender component, but in the seasonal component and be disregarded in the calender adjustments. This has been done from 2007.

Improved routine from 2009

The new routine takes into account the Norwegian calendar and thereby improving the quality of the seasonally adjusted results. The change has been applied from the January 2009 publishing, and concerns the pre-treatment method (calendar adjustment). The old method adjusted for working-days and for moving holidays (Easter, Pentecost, Ascension Day), leap year and outliers. The new method also adjusts for fixed Norwegian public holidays (1. January, 1. and 17. May) and for the Christmas holiday (24. - 26. December).

Monthly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Seasonal effects are therefore 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. Once data have been adjusted for seasonal effects, a clearer picture of the time series and the changes in the time series emerges. Routines are updated on an ongoing 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.

For more details about seasonal adjustment of the Index of industrial Production, see the separate tab " About seasonal adjustment".


Confidential micro data: According to § 2-4 of the Statistics Act, collected data are subject to confidentiality 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: Edited data that are not published are subject to confidentiality. This implies that they are unavailable to users without explicit approval. Such agreements only apply to internal users.

Comparability over time and space

Historical series classified according to SIC2007 and dating back to 1990 and 1995 are available.

Users must ensure that they use the same version of SIC when making comparision over time. As from January 2009 SIC 2002 is replaced by SIC 2007.

Finished historical series classified according to SIC2002 and dating from 1986 to 2008 are also available in StatBank Norway.

Accuracy and reliability

Sources of error and uncertainty

Measurement errors are caused by the questionnaire or the respondent’s internal system for obtaining the data. Examples are ambiguous questions, misunderstood questions or erroneous data from the respondents. In the Production Volume Index, errors in reported figures may originate from misunderstandings of the concept of production or the definition of the main variables used in the survey. Unambiguous guidelines and definitions are therefore emphasised.

Processing errors may occur when Statistics Norway processes the data. However, data from electronic questionnaires are loaded directly into the system. Considerable deviations from previous periods will normally become evident during the editing of the data.

Errors of non-response refer to errors that either occurs due to missing questionnaires or empty boxes in the questionnaire. The response rate after the deadline has expired is around 95 per cent. 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 or empty boxes in the questionnaires are mainly imputed automatically, based on previous reported figures (cold-deck method). No calculations of non-response errors have been made.

Sampling errors refer to uncertainties that occur in sample surveys as opposed to full counts. The sample variance equals the expected deviation between a sample survey and a full count. The Production Volume Index covers about 80 per cent of the turnover in the population (2012). 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. No calculations of the sampling errors for the survey have been conducted. The survey will normally try to correct for major new enterprises in the population by routine checks.

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, a limited share of the population units is incorrectly classified. This is usually due to misleading or insufficient information at a certain time. No calculations on the size and significance of such errors have 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 movable 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.


Not relevant

About seasonal adjustment

General information on seasonal adjustment

Monthly 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 and the changes in the time series emerges.

For more information on seasonal adjustment: metadata on methods: seasonal adjustment .

Seasonally adjusted series

For the index of industrial production seasonal adjusted series for 32 industry aggregates are published. This is the overall index, sections, divisions and groups according to the structure of SIC 2007 as well as indexes classified according to EUROSTAT’s end-use categories (Main Industrial groupings MIG’s).

Why seasonally adjust these statistics?

The index of industrial production is a part of a system of short-term statistics complied to monitor the economy. The primary goal of the survey is to monitor the level and development in the volume of production in oil and gas extraction, mining and quarrying, manufacturing and electricity and gas supply.

The output in the industrial production will normally vary from month to month because of public holidays and vacations in July and December and other things. The main aim of seasonal adjustment is to remove changes that are due to seasonal or calendar influences to better be able to compare the output in the industrial production from month to month.


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 serie.

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.

Calendar adjustments are done 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). The regression variables for the calendar adjustment are adapted to reflect the working days, public holidays and so forth specific to Norway.

Methods for trading/working day adjustment

RegARIMA correction is used – 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 is used.

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


Consistency between raw and seasonally adjusted data

In some series, consistency between raw and seasonally adjusted series is imposed.

Do not apply any constraint.

Comments: (see comments below)

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 is used. 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, mining and quarrying, manufacturing 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 between two consecutive official releases of the release calendar. The unadjusted (raw) data are not normally 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 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.

Comments : Seasonally adjusted numbers are updated from 2010.

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 adjustements is available. The table covers the published industry aggregates for the volume of production. The table is available 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.

Comments : Only problematic series with huge inconsistencies will be treated in a special way.

Posting procedures

Data availability

Unadjusted figures (orignial series or raw data), calendar adjusted, seasonally adjusted and smoothed seasonally adjusted figures are available.

Press releases

In addition to the unadjusted figures (raw data), the following series are released: calendar adjusted, seasonally adjusted and smoothed seasonally adjusted figures.

Both levels/indices and different forms of growth rates are presented.

Relevant documentation