Retail sales, investment statistics
Updated: 12 February 2024
Next update: 7 May 2024
|Seasonally adjusted series
|Change in per cent
|Change in per cent
|4th quarter 2022
|4th quarter 2023
|4th quarter 2022 - 4th quarter 2023
|3rd quarter 2023
|4th quarter 2023
|3rd quarter 2023 - 4th quarter 2023
|Retail trade, except of motor vehicles
|Machines and inventory
|Motor vehicles and other means of transport
|New buildings and renovation
About the statistics
The statistics describes the development in final investments for retail trade. Investments cover all acquisitions of new business equipment/assets with a lifetime of more than one year. Investments in used equipment are not included, while total renovation and assets acquired from financial leasing are included.
Establishment is defined as a locally delimited functional unit which mainlyoperates within a particular industry group (Standard Industrial Classification).
Investments: All acquisitions of new business equipment/assets with a lifetime of more than one year. Investments in used equipment are not included, while total renovation and assets acquired from financial leasing are included. Operational leasing is not included. With this kind of leasing the ownership and responsibility for the object is not transferred from the lessor. Machines, fixtures, vehicles and other means of transport are reported in the quarter they were received even if they have not been paid for or capitalised in the accounts. Machinery and equipment include investments in software. For dwellings started before the end of the quarter, only costs accrued in the given quarter are included. Value added tax is reported in net value, which means that refunded VAT is not to be included, whereas non-refundable VAT is to be included.
Final investments: Acquisitions made by the establishments in a given quarter. The concept of final investments does not necessarily imply that the acquired material has been put to use.
Estimated investments: Acquisitions that the establishments expect to make in the short and medium term. These figures can be based on approved plans, anticipations, etc. They are usually more uncertain in the longer term.
Machines and inventory: This includes for example computer equipment, software, cash registers, cleaning equipment, time clocks, alarm installations, video, TV, furniture, parts including assembly, and also tools with a lifetime of more than one year.
Motor vehicles and other means of transport: Motor vehicles, trailers and other means of transport.
New buildings and renovation: New buildings and total renovation of shops, storehouses, office buildings, manufacturing plants and social welfare installations.
Name: Retail sales, investment statistics
Topic: Wholesale and retail trade and service activities
Division for Business Cycle Statistics
National level only.
Quarterly. Published about 40 days after given quarter.
Collected and revised data are stored securely by Statistics Norway in compliance with applicable legislation on data processing.
Statistics Norway can grant access to the source data (de-identified or anonymised microdata) on which the statistics are based, for researchers and public authorities for the purposes of preparing statistical results and analyses. Access can be granted upon application and subject to conditions. Refer to the details about this at Access to data from Statistics Norway.
The index covers the development in investments in retail trade. The statistics are published as indices as of 2008, including time series from the first quarter 2005.
The statistics are used by the financial and economic sectors, trade organisations and other users within wholesale and retail trade. They are also used by Statistics Norway's Division for National Accounts.
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 in the Statistics Release Calendar. This is one of Statistics Norway’s key principles for ensuring that all users are treated equally.
Similar statistics exist for manufacturing, mining and quarrying and electricity supply. The statistics are used in the quarterly national accounts to forecast yearly national account figures.
The investment statistics are also published in the yearly structural survey for wholesale and retail trade. The difference is that the quarterly statistics only publish investments in new materials, while the structural survey also includes investments in second-hand materials.
The statistics are developed, produced and disseminated pursuant to Act no. 32 of 21 June 2019 relating to official statistics and Statistics Norway (the Statistics Act).
The statistics are part of the national program for official statistics, main area Wholesale and retail trade and service activities, sub-area Wholesale and retail trade.
All establishments in retail trade, except of motor vehicles and motorcycles (SIC2007: 47.1-9).
Quarterly statistical surveys (simplified questionnaire), the VAT register and Statistics Norway's Register of Establishments and Enterprises.
A sample of about 12 500 units is selected from the population of retail trade establishments in the VAT register, and consist of two sub-samples. This represents about a fourth of all units and covers approximately 70 per cent of the total turnover in retail sales. The sampling fraction and coverage rate vary within different main industries. The sample consist of two sub-samples.
The first sub-sample of approximately 1 500 units is selected from other existing establishments, i.e. independent shops and the remaining chain stores not yet included in the sample above. This population is stratified according to size in terms of number of employees. The sample is adjusted as necessary to ensure a reasonably even geographical coverage. The sample is rotated annually based on the second term of the turnover statistics. Establishments are retained in the sample for a maximum of four years unless they are part of a full coverage stratum.
The second sub-sample consist of about 11 000 chain stores with direct reporting from head office.
The statistics only include investments that the establishments pay for themselves. Investments made by property companies that rents out premises are not included.
Questionnaires are submitted via the Internet (ALTINN) for both the sample of establishments in the survey and the chain stores. Both establishments and the head office of chain stores normally receive the questionnaire at the start of the first month following the given quarter, and the deadline is the 25th of the same month.
Editing is defined here as checking, examining and amending data.
Prior to the statistical compilation, arithmetic and logical checks are carried out. Final investments are checked against previous figures for estimated investments. The respondents are contacted if there are indications of incorrect figures. Checks at macro level are carried out by the use of figures and tables. Later on, the data are compared with investment figures from the structural business statistics for wholesale and retail trade, which are published approximately 16 months after the given year.
As the sub-sample of chain stores with direct reporting from head office is based on a full count, the final investment figures are aggregated.
To calculate investment figures for the sub-sample of units selected from other existing establishments, a ratio estimator is applied to each stratum (3-4 employee groups per fivedigit sector level) to inflate the sample data to population level. The inflation of sample data of identical units is based on the ratio between the turnover of the population and the turnover of the sample. The ratio estimator uses turnover figures from the second term, the so-called base term, of the wholesale and retail statistics as auxiliary variables. The base term is substituted in October every year in connection with the rotation of the sample.
The establishments are divided into identical units and newly established units. Identical units are establishments with turnover in both the base term and the given quarter. Newly established units are establishments that have been established between the base term and the given quarter. The turnover in these enterprises is based on estimates as this information is not available. The estimates are based on information on newly established units in the past three years, as well as on the estimate for identical units in the given quarter.
See "About seasonal adjustment" at the end of "About the statistics".
Employees of Statistics Norway have a duty of confidentiality.
Statistics Norway does not publish figures if there is a risk of the respondent’s contribution being identified. This means that, as a general rule, figures are not published if fewer than three units form the basis of a cell in a table or if the contribution of one or two respondents constitutes a very large part of the cell total.
Statistics Norway can make exceptions to the general rule if deemed necessary to meet the requirements of the EEA agreement, if the respondent is a public authority, if the respondent has consented to this, or when the information disclosed is openly accessible to the public.
More information can be found on Statistics Norway’s website under Methods in official statistics, in the ‘Confidentiality’ section.
Control routines are in place to try and reveal measurement errors (the respondent supplies erroneous data) and processing errors (wrong interpretation of figures and letters during optical scanning). Three types of errors are common:
*Wrong unit: The respondent does not supply investment figures for the establishment, but for a part of the establishment or the enterprise that the establishment is part of.
*Wrong survey time period: The respondent does not report data for the correct time period (quarter).
*Wrong unit of measurement: Figures are in the wrong unit of measurement (usually in NOK instead of NOK 1 000).
Reminders are sent to enterprises that fail to respond in time, and failure to respond is subject to fines. Large enterprises that do not respond are reminded via telephone shortly before publication. Non-respondents are allocated the same change in per cent in the investments as the average for the stratum that they belong to. They normally constitute about 3 per cent of the total sample at the time of publication.
The results are uncertain as they are based on information from a sample of enterprises. The sample used to calculate the index is updated annually. When the sub-sample of chain stores is included, the total sample covers more than 60 per cent of the population in terms of turnover. Errors in the sample may also occur as a result of errors in the information that the sample is stratified according to. Sample skewness and variance are not calculated.
The coefficient of variation (CV) describes the relative uncertainty in the calculated index, given the assumptions which are the basis of the calculation modell. The CV is given in per cent in relation to the calculated index. Normally, the coefficient of variation for the total is between 4-10 per cent in the investment statistics. The CV forms the basis for a confidence interval of 95 per cent, if the lower and upper bound are defined in the same scale as the calculated index.
Using the second quarter of 2006 as an example, the results are as follows:
Given the assumptions which are the basis of the calculation model, we can say that it is 95 per cent certain that the real index lies between the interval 88,0 and 109,5.
A revision is a planned change to figures that have already been published, for example when releasing final figures as a follow-up to published preliminary figures. See also Statistics Norway’s principles for revisions.
Revisions in previously published seasonally adjusted figures can take place when new observations (or revised previous observations) are included in the basis of calculation. The scope of the revision is usually greatest in the most relevant part (last 1–2 years) of seasonally adjusted time series. A corresponding revision in trends is also typical, particularly at the end of the time series. The extent of the revision of trends and seasonally adjusted figures is partly determined by the revision policy, see Section 4 of the European Statistical System (ESS) Guidelines on Seasonal Adjustment on the Eurostat website. For more information on the revision of seasonally adjusted figures, see the ‘About seasonal adjustment’ section in the relevant statistics.
Monthly and quarterly time series are often characterised by considerable seasonal variations, which can 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 using 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
Historical data for investments for the retail sales industry (DETINV) reveal a clear pattern of higher investments towards the end of the year, i.e. in the 4th quarter. There could be many reasons for this, such as if a company decides to use up its entire investment budget before the end of the current year, or if they are registering the investments at the end of the year, etc. These patterns can complicate the interpretation of the time series from quarter to quarter. In order to enable an analysis of the underlying development of the investments of the retail sales industry, the timeseries are adjusted for seasonality.
Seasonally adjusted series
The investment for the retail sales industry are made up of four timeseries; retail trade, except of motor vehicles, motorcycles and automotive fuel, machines and Inventory, motor vehicles and other means of transport, and new buildings and renovation. All four timeseries are seasonally adjusted using the same model and method.
Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
No pre-treatment is performed on the timeseries.Checks are carried out for outliers.
Calendar adjustment involves adjusting for the effects of working days/trading days and for moving holidays. Working days/trading days are adjusted for both the number of working days/trading days and for the fact that the composition of days can vary from one month to another.
Methods for trading/working day adjustment
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
Treatment of outliers
Outliers, or extreme values, are abnormal values in the series.
The software automatically detects additive outliers, and temporary- and lasting level shifts.
Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.
Manual selection of model after statistical tests have been carried out.
Following statistical tests, the ARIMA model (0,1,1) (0,1,1) has been chosen, where the time series have been log-transformed.
The decomposition scheme specifies how the various components – trend cycle, seasonal and irregular – combine to form the original series. The most frequently used decomposition schemes are the multiplicative, additive or log additive.
Automatic decomposition scheme selection.
Choice of seasonal adjustment approach
X-12-ARIMA Version 0.3
Consistency between raw and seasonally adjusted data
No consistency conditions imposed.
Consistency between aggregate/definition of seasonally adjusted data
No consistency conditions 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.
A direct approach is used where the seasonal adjustment of components occurs using the same approach and software for each individual timeseries.
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 for 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 can 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 seasonally adjusted statistics.
The seasonally adjusted- and trendseries will change when a new time period (quarter) is added to the time series.
Concurrent versus current adjustment
The model, filters and calendar regressors are re-identified once a year and the respective parameters and factors are remain fixed between the yearly reviews.
The seasonality filter for retail trade, except of motor vehicles, motorcycles and automotive fuel, and new buildings and renovation is 3x5. For machines and inventory, and motor vehicles and other means of transport the seasonality filter is 3x9. The trendfiler for all four timeseries is 3x5.
Horizon for published revisions
The entire time series is revised in the event of a re-estimation of the seasonal factors.
Evaluation of seasonally adjusted data
Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.
Quality measures for seasonal adjustment
For most of the series, a selected set of diagnostics and graphical facilities for bulk treatment of data is used.
The quality measures for the four timeseries in DETINV vary. The series for retail trade, except of motor vehicles, motorcycles and automotive fuel, and machines and Inventory have good quality measures. The series for motor vehicles and other means of transport, and new buildings and renovation have poor quality measures. The poor level of the quality measures for motor vehicles and other means of transport means that adjusting for seasonality is not normally recommended.
In order to keep the seasonal adjustment to remain consistent, seasonal adjustment will be used on all four timeseries.
Seasonal adjustment of short time series
All series are sufficiently long to perform an optimal seasonal adjustment.
Treatment of problematic series
Raw data-, seasonally adjusted- and trendseries are available for all four time series.