Investments in oil and gas, manufacturing, mining and electricity supply

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

Division for Business Cycle Statistics

National level only

Published quarterly about 8 weeks after the end of the quarter for final investments. First estimates for the current year are published in the 1st quarter and are based entirely on estimates. Annual estimates for the current year are updated quarterly in 2nd, 3rd and 4th quarter based on final investments in the previous quarter as well as updated estimates for current quarter and the rest of the year. Final annual investments are published 1st quarter the following year. Estimates for next year are published the first time in 2nd quarter the year before, and are updated in 3rd and 4th quarter the year before.

Not relevant

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 anonymized 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 survey monitors the development in estimated and final investments within oil and gas activities, manufacturing, mining & quarrying and electricity supply in order to provide information about domestic demand for capital goods. The survey is financed by government appropriations.

From the Q3 publication in 2015, the statistics on investments in manufacturing, mining and quarrying and electricity supply (KIS) is merged with the statistics on oil and gas activities, investments (OLJEINV). The combined statistics will provide a more comprehensive presentation of final and planned investments for oil and gas, manufacturing, mining and quarrying and electricity supply.

The two merged statistics have the same conceptual build-up, regarding press release and analysis.

On a quarterly basis, both statistics provide figures for actual investments made in the previous quarter, as well as updated annual estimates for the current and the next year. However, the two statistics are quite different when it comes to the collection of data and in the formula build-up. In the statistics on oil and gas activities all active enterprise are included in the statistical sample; in other words the sample is identical to the population. Statistics on investments in manufacturing, mining and quarrying and electricity supply (KIS), on the other hand, is based on sample of the population.

Before the merger, the statistics on oil and gas activities, investments (OLJEINV) has been published quarterly since 1984, while statistics on investments in manufacturing, mining and quarrying and electricity supply (KIS) has been published quarterly since 1973.

Large revisions in oil and gas activity:

As of 2014 the category shutdown and removal was included in the statistics for oil and gas activities, investments. Until the statistical year 2012 this category was collected annually as a part of the current expenditure costs for the oil and gas industry. In accordance with international guideline changes the Norwegian annual national account regards these types of costs as an investment, as of the main revision 2014. In order to maintain accordance with the annual national account these costs were thus included in the statistics for oil and gas activities, investments. Figures going back to 2002 were also moved to the investment statistics.

Shutdown and removal also includes permanent shutdown (plugging) of wells. In the annual statistic, until the statistical year 2013, these costs were only collected for fields which where actually shut down. As of 2013 these costs are also collected for all active fields, since permanent shut down of wells may be performed while the field is still in production. Thus these types of costs are now collected by a larger population than before the revision. Hence, the figures for removal and shutdown before and after 2013 are not comparable. However, it may also be the case that costs related to shut down of wells which were not reported as shut down and removal before 2013 may have been included as production drilling investments under the category production.

In 2015 a revision of the questionnaires for oil and gas activities, investments was performed. Most of the questionnaires were simplified by removing some of the sub- cost categories.

Large revisions in manufacturing, mining and quarrying and electricity supply:

In Q2 2000 there was a comprehensive revision of the survey. The coverage was improved, and the industrial classification was revised for individual establishments. As from Q1 2004 results are estimated for the entire population. Prior to Q1 2004 the statistics was based on results for a sample of approximately 1900 establishments. High coverage and a high response rate were the incentives for using this simple form of aggregation.

As from Q1 2009 all results will refer to SIC2007. Historical series have been recalculated according to this version of SIC, and results dating back to 1995, or in some cases 1990, are available in the Statbank database. Historical series based on SIC2002 are still available in the Statbank database, but they will not be updated.

Internal users include statistics like the Quarterly National Accounts and the Economic Trends for Norway and Abroad . The results are also used in economic research.

External users include mass media, banks and financial institutions, public institutions like the Ministry of Finance and the National Bank of Norway etc.

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.

Results from the quarterly investment statistics are used by the Quarterly National Accounts to forecast the level of annual investments.

Investment statistics are also available in the annual structural business surveys for oil and gas activities , manufacturing, mining & quarrying , electricity supply and district heating. This data is also included in the build- up of the National Accounts

Oil and gas activity:

The information is collected by Statistics Norway on behalf of Norwegian Petroleum Directorate authorized in law by the Act of 29 November 1996 No. 72 relating to petroleum activities. Statistics Norway makes use of the information in preparation of official statistics, authorized in law by The Statistics Act of 1989.

Manufacturing, mining and electricity supply:

The statistics are developed, produced and disseminated pursuant to The Statistics Act.

Not relevant.

The statistics covers mining and quarrying (05, 07, 08, 09), manufacturing (10-33) and electricity supply (35), extraction of crude oil and natural gas (06) as well as pipeline transport (49.5), see Standard Industrial Classification 2007(SIC2007). The population is defined by the Central Register of Establishments and Enterprises, and establishment is used as observation unit since it has a more delineated and homogenous activity than an enterprise.

Manufacturing, mining and electricity supply:

The survey uses information from the Central Register of Establishments and Enterprises and investment data collected by questionnaire from the observation units included in the sample. Annual structural data for manufacturing, mining & quarrying and annual structural data for electricity supply are used to inflate the results to population level.

The gross sample includes about 1850 establishments and represents 9 per cent of the population. The observation units cover 80 per cent of the total level of investment for the industries covered by the survey.

The sample includes all establishments with 100 employees or more. Large investment projects in industries covered by the survey are also included. The remaining units are drawn by using a method based on stratification and optimal allocation with probability proportional to the size of the unit measured by the number of employees.

In principle, establishments with less than 10 employees are not included in the survey, but some exceptions are made. This can be newly started establishments which have not yet registered employment figures, units within electricity supply which have inadequate employment figures and units in industries whit very few numbers of establishments.

Oil and gas activity:

For extraction of crude oil (06.100), extraction of natural gas (06.200) and pipeline transport (49.500) questionnaires are filled out by operators with extraction licenses. For these industries all active enterprises are included in the survey; the sample is identical to the population.

Editing is defined here as checking, examining and amending data.

Manufacturing, mining and electricity supply:

The survey is based on data collected by questionnaires. The questionnaires are returned electronically via Altinn. The questionnaires are available in Altinn in the middle of the first month of each quarter for the first, second and fourth quarter. For the third quarter, the questionnaires are available at the end of June.

The deadline for returning the questionnaire is normally in the beginning of February, May, August and November. Respondents who fail to return the questionnaire receive a compulsory fine 5 days after the deadline, whereupon a new deadline of 7-9 days is set.

The questionnaires are downloaded from Altinn, and the data are automatically checked for duplicates and errors in totals. The results are also checked for deviations from data collected in previous quarters. When there are considerable deviations the establishment is contacted. Editing on an aggregated level is conducted by assessing the development over time, and unacceptable series lead to further editing of the data. Comparisons with annual structural statistics for manufacturing, mining & quarrying, electricity supply and district heating are also carried out.

Results are inflated to population level by using a ratio estimator. Investment data from the annual structural statistics for manufacturing, mining & quarrying and for electricity supply are used for calculating the ratios. The population and the sample are divided into sub-groups prior to estimation, first by grouping the establishments into publication levels approximately equal to 2-digit NACE (SIC2007) and then by stratifying the publication levels. The size of the establishment's average investments as registered in the annual structural statistics is used as stratification variable. Results from the three most recent annual surveys are used for calculating average investments.

Estimates are made for each publication level * stratum. The largest projects are not included in the estimation, but are added to the results to calculate the total level of investment within each publication level. Totals for mining & quarrying, manufacturing and electricity supply are created by summarising the estimates.

Oil and gas activity:

Figures for accrued and estimated investments in the oil and gas activity are collected for a number of goods and services. The accrued investment costs are requested. The investments contain the industry sector`s extraction of crude oil and natural gas, and pipeline transport (06.100, 06.200, 49.50) and are divided into investments for exploration, field development, fields on stream, shutdown and removal onshore activities and pipeline transport.

The data is collected by an electronic file transfer system (MoveIT), and the respondents are licence operators on the Norwegian continental shelf.

The data are automatically checked for duplicates and errors in totals. The results are also checked for deviations from data collected in previous quarters. When there are considerable deviations the establishment is contacted. Editing on an aggregated level is conducted by assessing the development over time, and unacceptable series lead to further revision of the data.

Quarterly investment figures on oil and gas activity are also seasonally adjusted as of Q3 2015.

Time series sometimes contain significant seasonal variation that makes it difficult to interpret the results from one period to another. This problem is solved with the help of sesonal adjustment. The Quarterly Investment Statistics uses X12-ARIMA to calculate seasonally adjusted figures and smoothed seasonally adjusted figures. For further information, please see the section About seasonal adjustment.

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.

As from Q1 2009, SIC2002 is replaced by SIC2007 (chapter 2.1). Users must ensure that they use results based on the same version of SIC when making comparisons over time. Historical series based on SIC2002 are available in the Statbank database for 1989 to 2008.

Measurement errors are caused by the questionnaire or the respondent’s internal systems for obtaining the data. One source of measurement errors may be ambiguous guidelines. Great effort is put into avoiding this kind of errors, and the introduction of electronic data collection has reduced the scope of measurement errors by integrating logical controls in the questionnaires.

Processing errors may occur when Statistics Norway process the data. Typical examples are misinterpretations, or when correct answers are assumed to be false and corrected. Electronic data collection through Altinn has reduced these kinds of errors.

Errors of non-response refer to errors that either occurs due to missing questionnaires or blank boxes in the questionnaire. The response rate when the deadline expires is around 97 per cent for manufacturing, mining and electricity supply, and 99.5 per cent for oil and gas activity. 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. Calculations of the effect of missing units have been done, but so far any evidence of skewness has not been uncovered. Missing questionnaires for manufacturing, mining and electricity supply are imputated by the use of figures from previous surveys (cold-deck). Boxes in the questionnaire that are left blank (partial non-response) are treated as zero. This is not always correct. In general, it is difficult to identify this type of errors, and a great deal of effort is put into securing the quality of the data from units with a considerable effect on the overall level of investment within a single industry.

Sampling errors refer to uncertainty that occur in sample surveys as opposed to a full count. The sample variance equals the expected deviation between a sample survey and a full count. 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. Calculations of the size and significance of this type of error have not yet been carried out. Establishments that close down may be a source of skewness if the proportion of close downs in the sample deviates from the population. The quarterly investment statistics is based on a fixed sample (panel). Periodic updates of the sample ensure that this is in accordance with the population. Sampling errors are not relevant for oil and gas activity since all active units are included in the survey; i.e the sample is identical to the population.

Coverage errors refer to errors in the registers that define the population. As a result, units may be incorrectly included in or excluded from the population. This is usually due to misleading or insufficient information at a certain time. Calculations of the size and significance of such errors have not yet been carried out. However, such errors are not considered to be greater than for other quantitative short-term statistics. Annual revisions of the industry classification for the sample units are performed around Q2 every year in order to ease these types of errors.

Modelling errors are first and foremost related to problems with 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 Easter. However, such problems are considered greater for surveys published on a monthly basis. 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 for manufacturing, electricity supply and oil and gas activities. However, this is not the case for mining and quarrying.

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 Sesonal Adjusment on the Eurostat

For more information on the revision of seasonally adjusted figures, see the ‘About seasonal adjustment’ section in the relevant statistics.

What is 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 do we seasonally adjust statistics on quarterly investments in oil and gas, manufacturing, mining and electricity supply?

The statistics on quarterly investments in oil and gas, manufacturing, mining and electricity supply is part of a system of short-term statistics compiled to monitor the economy. The primary goal of the survey is to monitor the development in actual and estimated investments as they are important indicators on the demand for capital goods in the economy.

Historical data reveal that time series on quarterly investments in oil and gas, manufacturing, mining and electricity supply have a clear seasonal pattern, and this might complicate the interpretation of the time series from quarter to quarter.

However, it is possible to adjust for this type of seasonal variations.

The seasonal factors (chart 1) show that quarterly investments seem to be low in the first quarter and high in the fourth quarter. The estimates for the second and the third quarter lie in between the two extremes and more or less share the same value. The stable seasonal pattern in the unadjusted series indicates a low degree of white noise. This conclusion is supported by the small gap between the seasonally adjusted time series and the smoothed seasonally adjusted time series (trend).

No empirical studies are conducted to explain the seasonal pattern for quarterly investments in oil and gas, manufacturing, mining and electricity supply. However, years of experience has shown that the following factors are possible sources of seasonal variations.

Seasonal variations

Temperatures and weather conditions seem to influence investment behaviour.

The nature of projects

Projects (particularly in manufacturing and electricity supply) often start at the beginning of the year and are scheduled to be finished within twelve months. Investments are most likely to be low in the initial phase of a project. The reason for this is that the groundwork often costs substantially less than new machinery and equipment.

Accounting principles

Some of the smaller establishments choose to report total investments in the fourth quarter of the year. This is against the guidelines, but can be explained by a lack of proper tools for making budgets and measure costs.

Seasonally adjusted time series

Seasonally adjusted time series are published for estimated and actual quarterly investments in:

- extraction of oil and gas, pipeline, mining, manuf. and electricity supply

- extraction of oil and gas and pipeline transport

- manufacturing, mining & quarrying and electricity supply

- manufacturing, mining & quarrying

- manufacturing

- mining & quarrying

- electricity supply

Pre-treatment routines/schemes

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

Automatic pre-treatment of raw data based on standard options in the seasonal adjustment tools.

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 performed on all time 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. The effect of trading days is estimated in a RegArima framework.

The effect of trading days is estimated by using a correction for the length of the month and for leap year before regressing the series on the number of working days. The residuals will have an ARIMA-structure.

Correction for moving holidays

Automatic correction. If performed by X-12-ARIMA, automatic correction of raw data will be based on US holidays.

National and EU/euro area calendars

Use of default calendars. The default in X-12-ARIMA is the US calendar.

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.

Automatic model selection by established routines in the seasonal adjustment tool.

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 time series.

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.

Do not apply any constraint.

Direct versus indirect approach

Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.

Direct seasonal adjustment is performed on aggregates and components using the same approach and software. Any discrepancies across the aggregation structure are not removed.

Horizon for estimating the model and the correction factors

When performing seasonal adjustment of a time series, it is possible to choose the period to be used in estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the series.

The whole time series is used to estimate the model and the correction factors

General revision policy

Seasonally adjusted data may change due to a revision of the unadjusted (raw) data or the addition of new data. Such changes are called revisions, and there are several ways to deal with the problem of revisions when publishing the seasonally adjusted statistics.

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 prior to the revision period of the unadjusted data, while older data are frozen.

Comment: The revision period for the seasonally adjusted figures is 4 years when new data are added. The whole time series may be revised when implementing new or improved methods.

Evaluation of seasonally adjustment 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.

A table containing selected quality indicators for the seasonal adjustment is available here .

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

Seasonal adjustment of short time series

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

Treatment of problematic series

None of the published series are viewed as problematic.

Data availability

Unadjusted and seasonally adjusted data are available.

Press releases

In addition to unadjusted data, at least one of the following series is released: Calendar adjusted, seasonally adjusted, smoothed seasonally adjusted (trend).

Metadata on methods: seasonal adjustment

EUROSTAT: Seasonal Adjustment. Methods and Practices

US census: X-12-ARIMA-manual.

The Committee for Monetary, Financial and Balance of Payments statistics: ESS-Guidelines on seasonal adjustment

Ole Klungsøyr: Sesongjustering av tidsserier. Spektralanalyse og filtrering, Notat 2001/54, Statistisk sentralbyrå

Dinh Quang Pham: Innføring i tidsserier - sesongjustering og X-12-ARIMA, Notater 2001/2, Statistisk sentralbyrå

Dinh Quang Pham: Nye US Census-baserte metoder for ukedageffekter for norske data, Notater 2008/58, Statistisk sentralbyrå

Ny metode for påskekorrigering for norske data, Notater 2007/43, Statistisk sentralbyrå.