Accuracy and reliability

Overall accuracy

The overall accuracy is considered to be high, in particular on main products and inputs.

Sampling error

Wherever possible, complete information is collected for a given commodity, for example, seed companies’ purchases of harvested seeds from agricultural holdings or dairies’ purchases of milk from farmers. In other cases, the statistics are based on a representative sample of market participants and individual products. For example, selling prices for cereals are based on information broken down by cereal type from the largest grain and feed companies, which together account for more than 90% of turnover. By contrast, price indices for vegetables are based on reports of selling prices from the largest producers, as well as information from wholesalers on the prices they pay growers for the most significant vegetables. The methods selected take into account statistical reliability, the importance of the commodities, and the availability of data. At the same time, efforts are made to minimise the reporting burden on businesses submitting data to Statistics Denmark.

Non-sampling error

The indices are not fully comparable over a long period of time (development consistent), except for indices for homogeneous individual products. Comparability is affected by the fact that the weight basis for the comparison of indexes of sales products and consumption in production changes in connection with the re-assessment every five years.

The uncertainty of the index over many years will increase with the number of years elucidated, as there are continuous changes in weight basis (in the case of rebates) and in the qualitative properties of products. The quality factor is not least important for investment indices, as technological development means new products that differ significantly from previous ones. But even some items, e.g. plant protection products, can have major quality changes.

The starting point of the weight basis can change significantly, which can cause distortions. In the base year there may be goods that change a lot afterwards. For example, mink skins had a high price in 2015, and at the same time there was a large production, ie. the weight became high. Since then the price has fallen extremely and it has also affected production.

Production within the simple range can also change extremely. For example, pig production, where the number of pigs slaughtered in relation to exported piglets has changed greatly.

It may happen that a product changes characteristics during the period between rebates, but otherwise must be considered to be the same product. If it is possible to find a basis for calculating the quality-related shift in price, this is corrected.

The index calculation assumes reported information for all individual products for all months of sales and purchases. This condition will not always be met. In some cases, information is delayed, in others it fails to get the information.

The specific handling of data deficiencies is done by one of the following solutions, with a priority in the order:

• Months where experience is often not data are not included in the calculations.
• A company that has not provided data is excluded from the calculation during that period.
• An average of the previous and following month is used.
• A price or quantity based on a professional estimate is used based on historical knowledge.

Applied replacement information will be updated with the relevant data if it arrives at a later date.

Quality management

Statistics Denmark follows the recommendations on the organisation and management of quality set out in the Code of Practice for European Statistics and the accompanying implementation model, the Quality Assurance Framework. Read more about these on the page Quality in official statistics.

Quality management is supported by common principles, guidelines and managerial responsibility for quality in statistical products and processes. The aim is to ensure a systematic and consistent approach to quality across the entire statistical production.

Quality assurance

Quality assurance at Statistics Denmark is integrated into the statistical production processes and is carried out in accordance with the principles of the Code of Practice for European Statistics. Read more on the page Quality in official statistics.

Quality assurance includes the continuous use of standardised procedures, documentation in line with international standards and internal controls related to data collection, data processing and dissemination. Experience gained from production is continuously used to identify needs for adjustments and improvements.

Quality assessment

The overall accuracy is described as good, especially for products with great importance. For some products, such as horticultural products, there is a very wide range of grades and product types, which can make it difficult to ensure price indices agree continuous representation. For the production factors which price is based on the general price statistics, there may be special circumstances relating to agriculture, which is not reflected. Some volume for the use of indices to gross agricultural factor income is calculated indirectly using the value development and price index, which does not give a completely accurate result.

Data revision - policy

Statistics Denmark revises published figures in accordance with the Revision Policy for Statistics Denmark. The common procedures and principles of the Revision Policy are for some statistics supplemented by a specific revision practice.

Data revision practice

In connection with the rebate every five years, the list of individual products is reviewed to ensure that there is a broad coverage of a product price. Here, the range will typically be replaced, so that only individual products of relatively high importance appear. This usually means that only a few individual products will be discontinued before the next rebase.