283300
/en/varehandel-og-tjenesteyting/statistikker/doi/maaned
283300
statistikk
2017-06-30T08:00:00.000Z
Wholesale and retail trade and service activities;Income and consumption
en
doi, Index of retail sales, retail sales, volume index, value index, retail trade, commodity trade, turnover (for example groceries, clothing, building materials)Wholesale and retail trade , Consumption, Income and consumption, Wholesale and retail trade and service activities
true

Index of retail sales

Updated

Next update

Key figures

1.3 %

change in retail sales (seasonally-adjusted volume index) from March to April 2017

Index of retail sales (2010=100)
May 2017Change in per cent
April 2017 - May 2017May 2016 - May 2017March 2016 / May 2016 - March 2017 / May 2017
1Excluding sales of motor vehicles
 
Retail sales - seasonally adjusted volume index1111.81.3
Retail sales - workingday adjusted volume index1117.12.42.4
Retail sales - workingday adjusted sales value index1130.33.93.8

See more tables on this subject

Table 1 
Retail sales. Index of value. 2010=100

Retail sales. Index of value. 2010=100
Retail trade, except of motor vehicles and motorcyclesRetail sale, except of motor vehicles and motorcycles and automotive fuelRetail sale in non-specialised storesRetail sale of food, beverages and tobacco in specialised storesRetail sale of automotive fuel in specialised storesRetail sale of information and communication equipment in specialised storesRetail sale of other household equipment in specialised storesRetail sale of cultural and recreation goods in specialised storesRetail sale of other goods in specialised storesRetail trade not in stores, stalls and markets
200684.984.380.082.795.197.392.383.086.885.2
200791.791.385.289.499.4108.4100.597.193.296.3
200895.594.791.595.1107.0105.6100.796.595.698.6
200997.897.897.698.097.5100.997.898.498.595.8
2010100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0
2011102.9102.5103.3105.3105.6105.1101.899.7101.4103.7
2012106.3105.9107.4109.3109.9104.8104.998.5104.6112.0
2013108.3107.7110.9112.7113.4100.3104.999.6104.3120.4
2014111.9112.1115.4116.5110.8102.5108.9100.5107.1144.4
2015115.5116.3119.1118.7108.4101.9112.7106.9110.9163.4
2016118.6120.1122.9122.9106.496.0114.5110.1115.2179.4
 
July 2014115.4114.8120.1129.5120.597.5112.0113.1103.3127.2
August 2014115.1115.1117.5113.3115.0100.7118.3108.7105.8149.1
September 2014110.3109.8111.9106.9113.7100.1112.992.7103.0155.3
October 2014114.0114.2116.7114.5112.7105.5119.288.2107.3155.7
November 2014113.4114.4114.0111.3105.4112.3116.5100.2109.3180.0
December 2014142.8147.1138.3177.6107.3174.7130.6176.1156.4187.4
January 2015101.8102.3108.793.197.2103.395.891.494.6150.9
February 201595.495.5103.996.294.681.387.183.183.9146.1
March 2015110.7110.6119.5120.8111.888.699.497.799.9155.1
April 2015105.2105.1111.1101.9105.979.796.895.2100.6147.0
May 2015113.7114.1118.9114.9110.381.9108.096.3113.0147.2
June 2015125.0125.2122.1122.1123.8102.9127.0114.4130.3160.9
July 2015120.5120.8122.3133.1117.999.1121.9117.4113.2140.2
August 2015118.4118.1122.8113.2120.7103.2119.2110.5104.9166.4
September 2015113.4114.0116.4111.8108.294.3116.299.4105.8169.2
October 2015118.3119.6123.1118.8107.4109.8121.892.0113.2168.3
November 2015117.6119.5117.1112.7102.5108.9125.2107.8113.2203.6
December 2015145.6151.2143.1185.899.9170.0134.4177.4157.8205.3
January 2016102.1102.7108.493.197.092.693.795.095.5169.5
February 2016101.3102.1111.9103.395.182.290.686.791.4155.0
March 2016106.2107.2119.0120.198.079.090.396.595.1154.3
April 2016113.8115.5119.6112.8100.380.0108.6104.6113.2164.9
May 2016119.1119.9125.6122.7111.976.1110.195.8121.3165.9
June 2016128.9130.2127.9128.7118.5100.4127.8119.6136.1176.7
July 2016120.5120.9123.8133.9116.795.7118.9115.5112.5150.9
August 2016124.2125.2127.0117.5116.2100.8128.1120.1114.3186.3
September 2016116.8118.2121.8119.2106.094.5118.998.8108.3178.8
October 2016118.3119.2121.4114.3110.294.9121.793.1111.9196.5
November 2016124.7127.3124.6118.9103.3103.7130.0115.4122.8227.5
December 2016147.4152.7143.5190.3103.7152.6135.3180.5160.0227.0
January 2017105.9105.5111.491.3109.991.298.186.697.8194.8
February 2017101.0101.1111.3100.6100.079.390.883.389.2159.5
March 2017114.6115.0124.1116.5111.785.2103.596.8103.3194.6
April 2017110.1110.3119.4116.2108.275.797.4102.1100.3164.2
May 2017125.4125.8129.6118.3122.181.8120.5101.2126.1194.3

Table 2 
Retail sales. Index of volume. Unadjusted 2010=100

Retail sales. Index of volume. Unadjusted 2010=100
Retail trade, except of motor vehicles and motorcyclesRetail sale, except of motor vehicles and motorcycles and automotive fuelRetail sale in non-specialised storesRetail sale of food, beverages and tobacco in specialised storesRetail sale of automotive fuel in specialised storesRetail sale of information and communication equipment in specialised storesRetail sale of other household equipment in specialised storesRetail sale of cultural and recreation goods in specialised storesRetail sale of other goods in specialised storesRetail trade not in stores, stalls and markets
200689.988.089.591.3109.878.797.076.182.382.1
200796.094.493.797.0111.991.6102.788.690.193.2
200897.996.596.999.7112.098.9101.788.193.194.9
200998.497.898.499.5104.096.297.897.796.394.9
2010100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0
2011102.0102.6102.3102.097.9113.6102.5100.3101.8105.1
2012105.1105.9105.9104.498.2120.9105.6100.4105.2114.0
2013106.6107.2107.7105.5100.8118.4105.5103.4106.0127.8
2014108.6109.7109.4106.697.6123.4106.0104.8108.3152.3
2015109.2110.5109.5105.798.4114.0104.1112.1110.4163.0
2016107.6108.8109.3105.598.9106.3100.7104.7108.8169.8
 
July 2014110.9111.6111.2118.8103.8112.9107.9125.2106.4133.1
August 2014112.1113.3110.1103.399.8120.9116.4112.9112.0159.3
September 2014106.5107.2105.397.899.4121.8108.497.8105.0164.2
October 2014109.9110.9110.2104.499.9126.4114.091.7108.7162.7
November 2014110.0111.6108.6101.393.7138.0111.898.5110.3185.0
December 2014140.2144.4133.5161.898.7212.7130.7164.4153.8191.5
January 2015100.4101.6102.483.589.1122.394.699.499.9159.1
February 201592.092.596.285.887.994.583.088.885.9153.0
March 2015105.9106.3111.9108.4101.6100.191.9106.599.3157.9
April 2015100.0100.5103.391.594.588.587.9105.799.7146.2
May 2015107.6108.3109.8102.7100.589.998.7105.8111.1149.0
June 2015118.3119.3111.8108.8108.3112.6116.8125.7130.7161.5
July 2015113.7114.9110.5118.8101.9109.3112.7130.0116.3141.3
August 2015111.9112.3111.5100.6108.0116.6111.6115.4107.0168.4
September 2015106.4107.1105.299.099.6103.5106.3106.5104.1167.0
October 2015110.9112.1112.4105.499.7118.3110.495.0111.8163.3
November 2015108.5110.3106.099.095.0121.4113.2102.1108.7193.5
December 2015135.1140.5132.4164.394.3191.1122.5163.8149.8195.9
January 201695.796.299.481.091.7104.285.792.193.9164.3
February 201693.593.799.689.092.391.681.185.388.2152.5
March 201697.998.8108.7104.291.588.179.293.890.1148.6
April 2016103.5104.7106.797.294.089.494.799.4107.2156.4
May 2016108.1108.5112.1105.5105.384.295.391.9113.8158.9
June 2016116.4117.6113.4110.4107.5112.0111.6113.8128.7167.6
July 2016108.0108.3107.2114.0105.9107.6104.3110.8107.2143.7
August 2016112.0112.6110.9100.2107.7112.1113.0114.0109.3177.3
September 2016104.9105.7106.6101.799.0104.9103.893.3101.6167.4
October 2016106.2106.6106.697.8103.7104.3105.787.9104.3182.5
November 2016112.0114.4110.5101.794.7111.9114.1108.5113.7210.2
December 2016133.1138.4130.0163.293.8165.3119.3165.8147.0208.5
January 201797.097.4100.177.794.3100.288.683.795.3187.5
February 201791.392.098.685.286.187.180.081.285.8153.1
March 2017103.3104.1110.398.697.994.190.495.396.0186.6
April 2017100.0100.4107.798.997.485.085.199.492.9156.2
May 2017112.5113.0114.7100.1109.092.1104.298.4116.5185.0

Table 3 
Retail sales. Index of volume. Seasonally adjusted 2010=100

Retail sales. Index of volume. Seasonally adjusted 2010=100
Retail trade, except of motor vehicles and motorcyclesRetail sale, except of motor vehicles and motorcycles and automotive fuelRetail sale in non-specialised storesRetail sale of food, beverages and tobacco in specialised storesRetail sale of automotive fuel in specialised storesRetail sale of information and communication equipment in specialised storesRetail sale of other household equipment in specialised storesRetail sale of cultural and recreation goods in specialised storesRetail sale of other goods in specialised storesRetail trade not in stores, stalls and markets
July 2014107.6108.9111.0108.697.9124.0101.2109.3104.9146.0
August 2014108.5110.4109.3106.794.0123.5104.9106.3111.6158.6
September 2014108.8110.1110.2106.499.1125.8105.5104.3108.7155.7
October 2014108.6110.1110.1106.697.9125.4105.3108.0107.2158.0
November 2014108.7110.1109.6105.698.3129.8104.9103.9109.5159.8
December 2014109.6110.8110.3106.7100.8124.7104.9107.7110.4160.5
January 2015108.6110.3109.9105.895.6130.6104.5106.4110.0155.7
February 2015109.4110.7109.3105.899.4120.1106.4107.3109.6171.9
March 2015109.8110.9109.3106.3101.2118.0106.7109.9111.6159.6
April 2015112.4114.0113.5108.1100.6120.1107.3113.0114.2166.6
May 2015107.4109.0107.7104.095.6118.8105.8110.3106.9160.1
June 2015108.8110.1109.1106.198.5116.1104.4109.4110.6160.4
July 2015109.5111.3109.5105.996.0116.7104.9114.0113.6159.9
August 2015109.8110.9112.2107.1101.3117.3102.9107.8108.6164.1
September 2015108.5109.7109.6106.599.3107.2103.2111.1108.3162.4
October 2015109.3110.7111.1106.898.2116.7103.2108.3110.4160.2
November 2015109.4110.8109.5104.799.0112.6105.9112.9109.9166.7
December 2015108.8110.4110.8107.097.4111.0103.5107.0108.8166.9
January 2016109.3110.7110.9107.698.8108.7101.5112.4110.1168.0
February 2016108.5109.6109.7106.0100.4106.7100.9109.9109.0167.7
March 2016107.4109.2108.6105.493.4104.9100.8110.3110.4166.3
April 2016107.9109.2109.5105.098.0106.3100.6106.5109.3167.9
May 2016109.0110.3111.5108.498.9104.199.2109.2110.1169.5
June 2016108.7110.2110.7105.698.2106.399.3111.0110.7170.9
July 2016108.1109.1108.7104.3100.4109.6100.1107.9109.7173.5
August 2016108.6109.7109.8105.1100.0103.4100.6110.7109.6170.7
September 2016108.1109.3110.7105.598.9105.7100.4107.9107.0170.5
October 2016109.2110.2109.7104.9101.7101.5102.3111.4108.9182.1
November 2016109.5111.0110.4104.698.8101.4102.8113.5110.7177.2
December 2016106.9108.1106.9103.397.799.0101.4107.7107.1182.3
January 2017108.5109.5110.1103.0100.5101.4102.1101.2108.5182.8
February 2017109.7111.3112.1104.497.8105.8103.1105.6110.3179.4
March 2017109.9111.2112.4104.4100.6105.1102.9109.4106.6188.9
April 2017110.4111.6111.7103.8101.2110.8104.3113.8107.2189.2
May 2017111.8112.9112.9103.4103.3110.5106.8111.5108.8192.9

Table 4 
Petrol stations, turnover statistics. NOK 1 000

Petrol stations, turnover statistics. NOK 1 000
TotalEngine fuel and lubricantsCar equipmentEveryday commoditiesOther goods including music and video salesFood serviceWorkshop service and car washRentingGames/commision
201645 355 62235 551 446596 1654 851 483343 9852 291 3511 118 06677 567525 559
 
June 20154 401 4793 526 89745 664443 52244 192214 96370 3089 02446 909
July 20154 192 1993 307 62146 017452 29351 567217 14860 7919 43147 331
August 20154 288 7893 419 60339 594460 94639 148206 32867 6718 49047 009
September 20153 847 4253 105 28236 403388 83426 671184 06651 7347 14747 288
October 20153 816 2093 027 88358 198380 28626 462184 16883 2117 46648 535
November 20153 643 2862 874 52358 965366 42025 563166 11596 9215 71949 060
December 20153 552 5162 722 52551 076391 37332 487192 077104 2694 61654 093
January 20163 448 2852 703 30858 575363 66024 839168 18189 3253 91036 487
February 20163 379 0092 659 67541 860348 85522 697156 906106 2874 24038 489
March 20163 484 2712 647 14043 615405 43228 121177 509137 2524 87640 326
April 20163 564 9192 782 87457 601365 44724 127170 878112 0976 72645 169
May 20163 978 1523 081 53746 191471 03331 376207 49588 4575 96746 096
June 20164 212 1523 319 91347 733470 77941 018212 27077 3168 89034 233
July 20164 146 8883 214 72246 528482 99544 772230 84768 2719 03449 719
August 20164 107 2283 290 30240 317427 81131 315208 29056 1978 42044 576
September 20163 766 3992 990 05738 859406 44724 320190 84064 5517 43543 890
October 20163 916 6173 099 16260 777390 14224 376191 54297 2107 30546 103
November 20163 672 5732 922 65061 093352 43121 116172 45094 6115 93342 289
December 20163 684 6862 842 98253 074373 20024 530199 199135 2384 75651 707
January 20173 905 8363 152 08449 374347 71518 537178 590111 8744 33043 332
February 20173 553 2932 846 49541 445326 77618 832159 168125 7344 29630 547
March 20173 972 3613 183 37041 621359 04819 875176 796128 2675 53257 852
April 20173 844 6912 967 02244 682437 94829 328195 22399 65910 92459 905
May 20174 340 1643 377 37553 827498 27836 213208 58097 8228 36059 709

Table 5 
Retail sales value index. Exclude value added tax. May 2017. 2010=100

Retail sales value index. Exclude value added tax. May 2017. 2010=100
 <May 2017Weight May 2017Change in per cent
 May 2016-May 2017March 2016-May 2016 - March 2017-May 2017January 2016-May 2017 - January 2017-May 2017
Retail sale, except sales of motor vehicles125.4100.05.43.32.7
47.1 Retail sale in non-specialized stores129.639.03.22.41.9
47.11 Retail sale in non-specialised stores with food, beverages or tobacco predominating129.136.52.62.01.6
47.2 Retail sale of food, beverages and tobacco in specialized stores118.34.2-3.5-1.3-1.6
47.251 Retail sale of wines and spirits in specialised stores120.23.0-2.10.1-0.8
47.3 Retail sale of automotive fuel in specialised stores122.110.49.110.29.9
47.4 Retail sale of information and communication equipment in specialised stores81.80.87.53.20.8
Retail sale of other household equipment in specialised stores120.518.39.54.03.5
47.51 Retail sale of textiles in specialised stores87.90.715.08.36.3
47.521Retail sale of variety of hardware, paints and glass in specialised stores148.27.63.90.11.1
47.54 Retail sale of electrical household appliances in specialised stores100.33.029.214.88.5
47.59 Retail sale of furniture, lighting equipment and other household articles in specialised stores99.54.87.83.32.8
47.591 Retail sale of furniture in specialised stores105.53.310.84.33.8
47.6 Retail sale of cultural and recreation goods in specialised stores101.24.85.71.1-1.8
47.61 Retail sale of books in specialised stores58.30.50.59.1-2.7
47.641Retail sale of sporting equipment in specialised stores99.62.62.7-3.3-4.9
47.7Retail sale of other goods in specialised stores126.118.23.90.10.1
47.71Retail sale of clothing in specialised stores109.66.4-0.6-5.2-4.2
47.72 Retail sale of footwear and leather goods in specialised stores100.61.11.3-5.3-5.4
47.721 Retail sale of footwear in specialised stores106.01.01.1-5.9-6.7
47.73 Dispensing chemist in specialised stores141.44.58.25.35.0
47.75 Retail sale of cosmetic and toilet articles in specialised stores113.80.9-4.9-2.3-1.4
47.761 Retail sale of flowers, plants, seeds and fertilisers242.32.611.76.86.4
47.78 Other retail sale of new goods in specialised stores112.31.87.15.23.6
47.9 Retail trade not in stores, stalls and markets194.34.217.114.012.1
Retail sale via mail order houses or via Internet204.73.917.114.512.8
Other retail sale not in stores, stalls or markets146.40.324.014.78.8

About the statistics

The Retail sales index describes developments in values and volumes in the retail trade (excluding sales of motor vehicles). Retailers are companies that sell goods to private households. Previously published seasonally-adjusted figures may be revised when figures are added for a new month in the series.

Definitions

Definitions of the main concepts and variables

Turnover includes dutiable and duty-free sales income from goods and services as well as rents, commission fees and royalties. Financial revenues are not included.

Standard classifications

Standard Industrial Classification (SIC2007) ( http://www.ssb.no/nace ).

Administrative information

Name and topic

Name: Index of retail sales
Topic: Wholesale and retail trade and service activities

Next release

Responsible division

Division for Construction and Service

Regional level

No geographical breakdown available. National level only.

Frequency and timeliness

Monthly. The statistics are normally published on the 28th or 29th day of the following month.

International reporting

The statistics are reported to Eurostat at the time of publication in Norway.

Microdata

Primary data and compiled statistics are stored electronically in SAS files.

Background

Background and purpose

The objective of the Index of retail sales is to describe the value and volume development in retail sales, excluding sales of motor vehicles. Retail sales consist of enterprises involved in the sale of new and used goods to private households. The sale is executed from either a fixed or moveable sales outlet, a market place or via the Internet or mail order. Examples of retail sales are the sale of food, beverages, clothing, shoes, domestic electrical appliances, furniture, building equipment and so on. Retail sales are the main component in the calculation of household consumption. Statistics have been published since 1936.

From November 1999, the sample of retail stores has been supplemented gradually with turnover figures for chain stores reported directly by head office. As a consequence, figures for newly established and closed down units for the above-mentioned stores are also more up to date.

Users and applications

Users include public and private sector agencies and organisations. Statistics Norway's national accounts statistics rely on timely production of the retail sales index. Other users include Statistics Norway&#8217;s research department.

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 in the Statistics Release Calendar. Principles of communication and dissemination

Coherence with other statistics

The index of household consumption of goods is published at the same time as the index of retail sales. The former is more extensive than the latter, which can lead to different developments in the two indices. Price indices of retail sales are applied when calculating the volume indices of the retail sales index. Changes in turnover are later compared with bimonthly retail sales statistics, which are based on the VAT register.

Legal authority

The Statistics Act, § 2-1, 2-2 and 2-3

EEA reference

EU Council Regulation No 1165/98. Commission Regulation 586/2001.

Production

Population

All establishments in retail trade, except of motor vehicles and motorcycles (SN2007: 47.).

Data sources and sampling

Monthly statistical surveys (simplified questionnaire), Statistics Norway&#8217;s annual survey of retail trade establishments (detailed questionnaire), the VAT register and Statistics Norway's Register of Establishments and Enterprises.

A sample of about 16 100 units is selected from the population of retail trade establishments in the VAT register, representing about a quarter of all units. This includes a sub-sample of 14 600 chain stores with direct reporting from head office.

Another sub-sample of 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 and to incorporate available new information from the annual survey of retail trade establishments and the bimonthly updated VAT register. In the latter case, the adjustments focus on diverging trends between turnover as measured by the sample and turnover as measured by a survey of the VAT register. In 1997, the sample was adjusted to take into account the variation of strata variances. 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.

Collection of data, editing and estimations

Questionnaires are submitted by mail, via the Internet or electronically. If the respondents need help in filling in the questionnaire, Statistics Norway can be contacted by telephone. The establishments normally receive the questionnaire before the expiry of the survey month. The deadline is the 12th day of the following month. Failure to respond is subject to fines.

Prior to the statistical compilation, arithmetic and logical checks are carried out. This procedure also includes comparing the results with other data sources, mainly the wholesale and retail trade statistics.

Results broken down by sector and stratum are compared with data for the previous period and the corresponding period in the previous year. If the discrepancy is considerable, the respondent is consulted.

A ratio estimator is applied to each stratum to inflate sample data to population level. The ratio estimator uses turnover figures from the VAT register as auxiliary variables.

The establishments are divided into identical units and newly established units. These concepts are defined in Notater 93/17 Detaljomsetningsindeksen (in Norwegian only).

Newly established units are enterprises that have been registered in the Central Register of Establishments and Enterprises after the last rotation of the sample. The turnover of these enterprises is based on estimates as this information is not available. The estimates are based on information about newly established units in the same period in the previous year. The average turnover per establishment is calculated for newly established units in the same period in the previous year. On this basis the turnover for all recent establishments for the period in question is aggregated. The estimates for newly established units are made on stratum and NACE level.

Volume indices at NACE four-digit sector level are calculated by deflating value indices directly by means of the price index for retail sales. At NACE two and three-digit sector level the volume indices are calculated as a weighted sum of the volume indices at the NACE four-digit sector level using value shares in the reference year as weight.

The index is adjusted for seasonal variations applying the X12ARIMA method with non-fixed seasonal effects and multiplicative model. As a supplement to seasonal variations, the new model takes into account the effect of weekdays, fixed holidays as 1 st and 17 th May, Easter, Pentecost, Ascension Day and 1 st New Year's Day. 24 th to 26 th of December are considered as seasonal variations with a method developed by Statistics Norway.

Seasonal adjustment

Refers to the separate tab "About seasonal adjustment" on statistics website.

Confidentiality

Not relevant

Comparability over time and space

The index has been published since 1936. A new version of Norwegian industry classification (SIC2007) has been implemented from January 2009. The toal index, as well as three-digit sector levels, has been recalculated back to 2000 according to SIC2007. In January 2003, Statistics Norway altered the calculation method for the price index of retail sales. The new method is based on the price development of product groups from the consumer price index, as well as on sector-wise product allocation. The product allocation comes from a survey with that particular focus. The price index of retail sales is calculated using the same methods used to calculate the deflator of the retail sales component in the index of household consumption of goods. Volume figures have been calculated back to August 1999 using the current deflator.

The volume indices at NACE four-digit sector level are calculated by deflating value indices using the aid of the price index for retail sales. From January 2002, volume indices at NACE two and three-digit sector level are calculated as a weighted sum of the volume indices at NACE four-digit sector level with value shares in the reference year as weight. In the past, volume indices at the NACE two and three-digit sector level were calculated using immediate deflation of value indices.

Accuracy and reliability

Sources of error and uncertainty

Measurement errors (the respondent supplies erroneous data) and processing errors (wrong interpretation of figures and letters during optical scanning) may occur.

Three types of errors are common:

  • The respondent does not supply turnover figures for the establishment, but for a part of the establishment or the enterprise that the establishment is part of.
  • The respondent does not report data for the correct time period (calendar month).
  • 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. Failure to respond is subject to fines. Large enterprises that do not respond are reminded via telephone shortly before publication. These enterprises are treated in the same way as enterprises that are not included in the sample. This means that the change in turnover applied to these enterprises is the average percentage change that is applied to their stratum. Non-respondents normally constitute about 4 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 is updated once a year. The coverage exceeds 80 per cent of the population in terms of turnover, including the sub-sample of chain stores. Sample variance accounted for by the sub-sample of other stores than chain stores is estimated to about 1 per cent. Sample errors may also occur as a result of errors in the information that the sample is stratified according to.

Revision

Not relevant

About seasonal adjustment

General information on seasonal adjustment

Montly and quarterly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Such time series are therefore subjected to a process of seasonal adjustment in order to remove the effects of these seasonal fluctuations. Once data have been adjusted for seasonal effects by X-12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.

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

Why seasonally adjust these statistics?

Due to our shopping habits the retail sales index will vary from month to month. For instance the month of December shows higher sale than the rest of the months. This combined with the influence of how the Easter holiday varies between March and April and also the influence of movable public holidays make a comparison from one month to the next difficult. To adjust for these circumstances the retail sales index is adjusted for seasonal variations, so the underlying development of the index can be analyzed.

Seasonally adjusted series

The retail sales index is published in a three-digit NACE level, and constitutes 10 seasonally adjusted series.

Pre-treatment

Pre-treatment routines/schemes

Running a detailed pre-treatment. This means using models which are specially adapted for the pre-treatment of the raw data for a given series.

Calendar adjustment

To perform calendar adjustments on all series showing significant and plausible calendar effects within a statistically robust approach, such as regression or RegARIMA (a regression model with an ARIMA structure for the residuals). 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 – 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.

Comments:

For 1 January , 1 May and 17 May the correction of working days has been modified so that these days are regarded as a Sunday.

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

A calendar based on Norwegian holidays is used.

Treatment of outliers

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 series.

Seasonal adjustment

Choice of seasonal adjustment approach

X-12-ARIMA

Consistency between raw and seasonally adjusted data

Do not apply any constraint.

Consistency between aggregate/definition of seasonally adjusted data

In some series, consistency between seasonally adjusted totals and the original series 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.

Indirect approach where the seasonal adjustment of components occurs using the same approach and software, and then totals are derived by aggregation of the seasonally adjusted components.

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.

Both raw and seasonally adjusted data are revised between two consecutive official releases of the release calendar.

Comments:

Raw data is not revised.

Concurrent versus current adjustment

Partial concurrent adjustment: the model is identified and estimated yearly, while filters, outliers and regression parameters are re-identified and estimated continuously as new or revised data become available.

Comments:

Factors concerning the Easter holyday are estimated yearly.

Horizon for published revisions

The entire time series is revised in the event of a re-estimation of the seasonal factors.

Quality of seasonal adjustment

Evaluation of seasonally adjustment data

Evaluation of quality based only on graphical inspection and descriptive statistics.

Comments:

A model where the various quality indicators will be evaluated continuous/periodically in the future.

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.

Special cases

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.

Posting procedures

Data availability

Raw data, pre-treated data and seasonally adjusted series are available.

All metadata information associated with an individual time series is available.

Press releases

In addition to raw data, at least one of the following series is released: Seasonally plus working day adjusted.

Relevant documentation

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