As from 2017 the statistics is published with Credit indicator.
Updated
Key figures
5.7 %
twelve-month growth in the general public’s domestic debt
| January 2017 | February 2017 | March 2017 | April 2017 | May 2017 | June 2017 | |
|---|---|---|---|---|---|---|
| 1Annualised figure | ||||||
| 12-month growth, total | 5.1 | 5.0 | 5.2 | 5.1 | 5.4 | 5.7 |
| 3-month moving average, total1 | 4.3 | 5.4 | 6.0 | 6.6 | 6.7 | .. |
| 12-month growth, households | 6.5 | 6.6 | 6.7 | 6.5 | 6.7 | 6.6 |
| 12-month growth, non-financial corporations | 2.2 | 1.9 | 2.3 | 2.3 | 3.1 | 4.0 |
See selected tables from this statistics
Table 1
Domestic credit to general public. Stocks. NOK million
| Actual figures | Seasonally adjusted figures | |||||
|---|---|---|---|---|---|---|
| C2 | C1 | Foreign exchange | C2 | C1 | Foreign exchange | |
| June 2016 | 5 041 438 | 4 805 034 | 236 404 | 5 029 819 | 4 792 172 | 237 647 |
| July 2016 | 5 053 466 | 4 818 202 | 235 264 | 5 052 006 | 4 818 159 | 233 847 |
| August 2016 | 5 066 917 | 4 837 784 | 229 133 | 5 069 598 | 4 843 510 | 226 088 |
| September 2016 | 5 086 681 | 4 865 618 | 221 063 | 5 085 145 | 4 865 029 | 220 116 |
| October 2016 | 5 117 008 | 4 894 495 | 222 513 | 5 110 865 | 4 888 975 | 221 890 |
| November 2016 | 5 137 286 | 4 911 720 | 225 566 | 5 127 262 | 4 904 163 | 223 099 |
| December 2016 | 5 139 631 | 4 916 211 | 223 420 | 5 141 235 | 4 922 255 | 218 980 |
| January 2017 | 5 160 916 | 4 947 530 | 213 386 | 5 164 217 | 4 955 000 | 209 217 |
| February 2017 | 5 179 816 | 4 966 748 | 213 068 | 5 186 674 | 4 975 607 | 211 067 |
| March 2017 | 5 216 235 | 4 997 314 | 218 921 | 5 227 218 | 5 004 827 | 222 391 |
| April 2017 | 5 240 299 | 5 018 985 | 221 314 | 5 249 681 | 5 021 666 | 228 015 |
| May 2017 | 5 282 794 | 5 057 918 | 224 876 | 5 279 922 | 5 047 173 | 232 749 |
| June 2017 | 5 328 535 | 5 096 325 | 232 210 | 5 316 663 | 5 082 627 | 234 036 |
Table 2
Domestic credit to general public. Transactions
| C2 | |||||||
|---|---|---|---|---|---|---|---|
| Actual figures | Seasonally adjusted figures | ||||||
| 12-month growth, transactions | 12-month growth, percentage change | 1-month growth | Growth this year | Annualised 1-month growth | Annualised growth this year | Growth based on the 3-month moving average2 | |
| 1All growth rate calculations based on holdings that include foreign currency loans are adjusted for exchange rate fluctuations in order to eliminate all changes not related to transactions. The growth rate calculations are also adjusted for statistical breaks that are not attributable to transactions or valuation changes. | |||||||
| 2Annualised figure. | |||||||
| June 2016 | 237 440 | 5.0 | 20 990 | 128 145 | 5.1 | 5.3 | 5.1 |
| July 2016 | 239 220 | 5.0 | 17 207 | 145 352 | 4.2 | 5.1 | 4.9 |
| August 2016 | 240 720 | 5.0 | 23 824 | 169 176 | 5.8 | 5.2 | 4.9 |
| September 2016 | 246 737 | 5.1 | 23 077 | 192 253 | 5.6 | 5.3 | 5.3 |
| October 2016 | 244 262 | 5.0 | 22 447 | 214 700 | 5.4 | 5.3 | 5.2 |
| November 2016 | 252 790 | 5.2 | 12 523 | 227 223 | 3.0 | 5.1 | 4.6 |
| December 2016 | 238 098 | 4.8 | 11 001 | 238 224 | 2.6 | 4.8 | 4.2 |
| January 2017 | 248 775 | 5.1 | 30 286 | 30 286 | 7.3 | 7.3 | 4.3 |
| February 2017 | 248 648 | 5.0 | 21 003 | 51 289 | 5.0 | 6.1 | 5.4 |
| March 2017 | 258 768 | 5.2 | 34 572 | 85 861 | 8.3 | 6.8 | 6.0 |
| April 2017 | 253 857 | 5.1 | 21 666 | 107 527 | 5.1 | 6.4 | 6.6 |
| May 2017 | 270 030 | 5.4 | 31 282 | 138 809 | 7.4 | 6.6 | 6.7 |
| June 2017 | 284 965 | 5.7 | 35 824 | 174 633 | 8.5 | 6.9 | .. |
Table 3
C2 credit sources. Stocks. NOK million
| Domestic sources, total | Loans issued by | Bond debt | Certificate debt | Other sources | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Loans from banks | Loans from state lending institutions | Mortgage companies | Financing companies | Life insurance companies | Non-life insurance companies | Pension funds | |||||
| June 2016 | 5 041 438 | 2 432 262 | 316 217 | 1 601 448 | 141 224 | 83 555 | 2 057 | 1 785 | 271 266 | 77 672 | 113 952 |
| July 2016 | 5 053 466 | 2 447 455 | 315 820 | 1 598 773 | 141 314 | 83 920 | 2 059 | 1 767 | 272 568 | 77 866 | 111 924 |
| August 2016 | 5 066 917 | 2 455 819 | 319 000 | 1 602 808 | 142 489 | 84 284 | 2 060 | 1 749 | 270 457 | 77 210 | 111 041 |
| September 2016 | 5 086 681 | 2 454 173 | 321 044 | 1 618 073 | 144 461 | 84 649 | 2 062 | 1 722 | 279 073 | 72 171 | 109 253 |
| October 2016 | 5 117 008 | 2 469 685 | 322 521 | 1 617 878 | 146 708 | 91 212 | 1 977 | 1 713 | 282 151 | 74 308 | 108 855 |
| November 2016 | 5 137 286 | 2 486 696 | 317 796 | 1 620 724 | 148 471 | 97 777 | 1 891 | 1 695 | 280 597 | 73 372 | 108 267 |
| December 2016 | 5 139 631 | 2 473 733 | 318 771 | 1 627 933 | 148 235 | 104 340 | 1 806 | 1 675 | 283 671 | 72 543 | 106 924 |
| January 2017 | 5 160 916 | 2 492 679 | 322 241 | 1 636 750 | 139 818 | 105 210 | 1 951 | 1 675 | 285 182 | 71 220 | 104 190 |
| February 2017 | 5 179 816 | 2 489 296 | 323 200 | 1 654 118 | 141 777 | 106 083 | 2 095 | 1 675 | 286 670 | 71 430 | 103 472 |
| March 2017 | 5 216 235 | 2 514 139 | 325 724 | 1 662 136 | 142 878 | 106 953 | 2 240 | 1 675 | 290 219 | 67 394 | 102 877 |
| April 2017 | 5 240 299 | 2 493 934 | 328 455 | 1 703 594 | 144 125 | 107 817 | 2 178 | 1 675 | 286 885 | 69 804 | 101 832 |
| May 2017 | 5 282 794 | 2 519 357 | 329 101 | 1 706 436 | 146 420 | 108 681 | 2 114 | 1 675 | 295 200 | 73 684 | 100 126 |
| June 2017 | 5 328 535 | 2 553 659 | 328 954 | 1 712 795 | 149 129 | 109 545 | 2 052 | 1 675 | 297 704 | 73 604 | 99 418 |
Table 4
C2 credit sources. Transactions over past 12 months
| Per cent | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Domestic sources, total | Loans issued by | Bond debt | Certificate debt | Loans from other sources | |||||||
| Loans from banks | Loans from state lending institutions | Mortgage companies | Financing companies | Life insurance companies | Non-life insurance companies | Pension funds | |||||
| 1The growth rates in banks and mortgage companies’ loans are revised in the period October 2015 to March 2016 due to changes in the method for statistical breaks caused by portfolio shifts between banks and mortgage companies. | |||||||||||
| June 2016 | 5.0 | 3.3 | 4.4 | 7.8 | 10.3 | 59.4 | -39.7 | -18.6 | -0.9 | 20.8 | -16.5 |
| July 2016 | 5.0 | 3.5 | 4.3 | 7.6 | 10.6 | 59.1 | -39.1 | -17.5 | -0.1 | 17.0 | -18.0 |
| August 2016 | 5.0 | 3.5 | 4.2 | 7.6 | 10.7 | 58.8 | -38.3 | -16.4 | -0.6 | 17.6 | -16.9 |
| September 2016 | 5.1 | 3.3 | 4.3 | 8.0 | 11.2 | 58.4 | -37.7 | -15.7 | 2.1 | 10.7 | -16.6 |
| October 2016 | 5.0 | 3.9 | 3.9 | 6.4 | 12.0 | 46.5 | -30.9 | -14.0 | 3.6 | 12.4 | -15.9 |
| November 2016 | 5.2 | 3.8 | 3.9 | 7.4 | 12.2 | 37.4 | -21.8 | -12.8 | 3.1 | 7.7 | -14.5 |
| December 2016 | 4.8 | 3.7 | 3.6 | 6.7 | 11.3 | 30.0 | -8.4 | -11.1 | 2.7 | 7.1 | -13.2 |
| January 2017 | 5.1 | 4.4 | 3.8 | 6.4 | 11.0 | 30.2 | -0.9 | -10.7 | 3.0 | 4.2 | -14.4 |
| February 2017 | 5.0 | 3.7 | 3.9 | 7.4 | 11.1 | 30.3 | 6.7 | -9.8 | 5.1 | -2.4 | -14.1 |
| March 2017 | 5.2 | 4.4 | 4.3 | 6.6 | 10.8 | 30.5 | 14.3 | -8.9 | 9.2 | -13.9 | -13.6 |
| April 2017 | 5.1 | 3.0 | 3.9 | 8.6 | 10.2 | 30.7 | 9.3 | -8.0 | 7.3 | -11.6 | -12.9 |
| May 2017 | 5.4 | 3.5 | 3.9 | 7.7 | 10.5 | 30.9 | 4.4 | -7.1 | 10.8 | -3.8 | -13.9 |
| June 2017 | 5.7 | 4.6 | 4.0 | 7.0 | 10.3 | 31.1 | -0.2 | -6.2 | 9.7 | -5.2 | -13.1 |
Table 5
C2 by debtor sector. Stocks. NOK million
| Municipal government | Non-financial corporations | Households etc. | |
|---|---|---|---|
| June 2016 | 446 423 | 1 621 002 | 2 974 013 |
| July 2016 | 447 014 | 1 619 291 | 2 987 161 |
| August 2016 | 448 010 | 1 615 120 | 3 003 787 |
| September 2016 | 451 427 | 1 608 683 | 3 026 571 |
| October 2016 | 452 846 | 1 616 589 | 3 047 573 |
| November 2016 | 454 831 | 1 623 443 | 3 059 012 |
| December 2016 | 457 327 | 1 615 806 | 3 066 498 |
| January 2017 | 461 548 | 1 618 169 | 3 081 199 |
| February 2017 | 465 438 | 1 622 542 | 3 091 836 |
| March 2017 | 466 540 | 1 638 834 | 3 110 861 |
| April 2017 | 469 060 | 1 646 298 | 3 124 941 |
| May 2017 | 467 521 | 1 665 870 | 3 149 403 |
| June 2017 | 470 302 | 1 687 891 | 3 170 342 |
Table 6
C2 by debtor sector. Transactions
| 12-month growth, transactions. NOK million | 12-month growth, transactions. Per cent | |||||
|---|---|---|---|---|---|---|
| Municipal government | Non-financial corporations | Households etc. | Municipal government | Non-financial corporations | Households etc. | |
| 1All growth rate calculations based on holdings that include foreign currency loans are adjusted for exchange rate fluctuations in order to eliminate all changes not related to transactions. The growth rate calculations are also adjusted for statistical breaks that are not attributable to transactions or valuation changes. | ||||||
| June 2016 | 22 861 | 47 273 | 167 306 | 5.4 | 3.0 | 6.0 |
| July 2016 | 21 699 | 51 424 | 166 097 | 5.1 | 3.3 | 5.9 |
| August 2016 | 21 287 | 46 755 | 172 678 | 5.0 | 3.0 | 6.1 |
| September 2016 | 27 067 | 40 641 | 179 029 | 6.4 | 2.6 | 6.3 |
| October 2016 | 25 279 | 42 715 | 176 269 | 5.9 | 2.7 | 6.1 |
| November 2016 | 21 460 | 43 286 | 188 046 | 5.0 | 2.7 | 6.5 |
| December 2016 | 24 080 | 30 897 | 183 122 | 5.6 | 1.9 | 6.3 |
| January 2017 | 25 360 | 34 679 | 188 737 | 5.8 | 2.2 | 6.5 |
| February 2017 | 28 143 | 29 994 | 190 512 | 6.4 | 1.9 | 6.6 |
| March 2017 | 27 005 | 36 382 | 195 382 | 6.1 | 2.3 | 6.7 |
| April 2017 | 25 050 | 37 388 | 191 419 | 5.6 | 2.3 | 6.5 |
| May 2017 | 23 175 | 50 319 | 196 536 | 5.2 | 3.1 | 6.7 |
| June 2017 | 23 879 | 64 875 | 196 210 | 5.3 | 4.0 | 6.6 |
About the statistics
The main focus of the credit indicator C2 is the growth in the general public’s domestic debt over the past twelve months (twelve-month growth). Transaction and growth estimations are adjusted for changes in stocks that are not due to new borrowings or repayments of loans.
Definitions
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The public comprises the institutional sectors general government, non-financial enterprises and households etc. Non-profit institutions serving households is included in the household sector in C2.
- C1 stands for "Credit from domestic sources in NOK", i.e. the indicator of the general public’s gross domestic debt in NOK".
- C2 stands for "Credit from domestic sources in NOK and foreign currency", i.e. the indicator of the general public’s gross domestic debt in NOK and foreign currency".
Oil activities comprise all enterprises in industry 22 (Services linked to extraction of crude petroleum and natural gas) and industry 23 (Extraction of crude petroleum and natural gas).
Ocean transport comprises all enterprises classified in industry 49 (Sea transport abroad and transport via pipelines).
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The C2 statistics have three types of classifications; NOK/foreign currency, credit sources and borrowing sectors:
NOK/foreign currency: The C2 data is divided into NOK and foreign currency.
Credit sources: The C2 data is classified according to credit source, where a source could either be a combination of a lending sector and a finance object, for instance bank loan, or just a finance object, for instance bond debt.
Borrowing sector: The C2 data is classified according to the borrowing sector’s general government, non-financial enterprises and households etc.
Administrative information
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Name: The credit indicator C2
Topic: Banking and financial markets
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Division for Financial Markets Statistics
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Only at national level
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Monthly
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No mandatory reporting, but data are posted on Statistics Norway’s website under “Economic Indicators".
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Published data is stored in SSB's data base.
Background
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C2 is an approximate measure of the magnitude of the gross domestic debt of the public (households etc., non-financial enterprises and general government) in NOK and foreign currency.
The purpose is to contribute to the basis of information for the monetary policy. The statistics provide an overview of the development of credit at an early stage and is an important indicator of economic activity.
Norges Bank introduced the credit indicator statistics in the mid 1980s, and such data are available dating back to December 1985. After Statistics Norway took over most of the work involved in collecting and publishing financial statistics from Norges Bank in 2007, the work with the credit indicator statistics, C2, was also transferred to Statistics Norway.
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Monetary authorities, i.e. Norges Bank and the Ministry of Finance. Other important users are the Financial Supervisory Authority of Norway, the financial markets and research institutions. These statistics attract a great deal of media attention.
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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. For more information, see Principles for equal treatment of users in releasing statistics and analyses.
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The statistics are based on the guidelines in the System of National Accounts (SNA 1993), European System of Accounts (ESA 1995), Manual on Monetary and Financial Statistics (IMF) and Balance of Payments Manual (IMF 1993).
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Not relevant
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Derived statistics, without direct Council Directives or Council Regulations from the EU.
Production
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Sources included in C2 are loans in NOK and foreign currency to the public by banks, state lending institutions, finance companies, life and non-life insurance companies, mortgage companies, pension funds, the Norwegian Public Service Pension Fund, Export Credit Norway and Norges Bank. C2 also includes the public's bond loan debt to domestic lenders and the public's certificate debt in the domestic market. The public's debt, in the form of secured and non-secured domestic inter-company loans, is included up to the end of 1999.
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The C2 statistics are derived from the accounting statistics of ORBOF (Reporting of banks, mortgage companies, state lending institutions and finance companies accounts to the public authorities), FORT (Reporting of life and non-life insurance companies accounts for the public authorities) and PORT (Reporting of pension funds account to the public authorities), The data for the public's bond and certificate debts are derived from statistics for securities registered in VPS. The Norwegian Public Service Pension Fund and Export Credit Norway also report data for these statistics.
The calculations of revaluations due to exchange rate fluctuations are based on stock data for the public’s gross debt to the credit sources in the C2 statistics, official data for exchange rates and data for the composition of currencies of the bank’s receivables and debts from the quarterly BIS survey.
The C2 statistics, are in principle, based on total censuses.
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From 2007, Statistics Norway have had the responsibility for collecting accounting data for banks and financial corporations, while the Financial Supervisory Authority of Norway and Statistics Norway are jointly collecting accounting data from insurance companies and pensions funds. In addition, Statistics Norway obtains data from the Norwegian Public Service Pension Fund, Export Credit Norway and VPS.
The editing of the financial corporations’ accounting statements are undertaken by Statistics Norway and the Financial Supervisory Authority of Norway. The data from the Norwegian Public Service Pension Fund and VPS are controlled by Statistics Norway.
The policy is to disseminate changes of the previous month’s data together with the current month’s data. Statistics Norway is fully prepared to edit in a timely manner, with appropriate notification to users and the media, should it be deemed necessary by the magnitude of a past error, or, owing to other exceptional circumstances. Some of the reported data may contain preliminary data that are subsequently corrected.
All growth rate calculations based on holdings that include foreign currency loans are adjusted for exchange rate fluctuations in order to eliminate all changes not related to transactions. The growth rate calculations are also adjusted for statistical breaks that are not attributable to transactions or valuation changes. Examples of this kind of break could be that a financial enterprise moves from one sector to another or an introduction of a new financial source.
Growth based on the three-month moving average is defined as growth in average outstanding credit (seasonally-adjusted figures) in the latest three-month period in relation to the previous three-month period adjusted for exchange rate valuation changes and statistical breaks as an annualised figure. The calculation is centred, i.e. the observation is set at the middle month of the latest three-month period.
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The seasonal adjustment of the credit indicator C2 is carried out using the X12 Arima program. Seasonal components are calculated for one year ahead, when publishing data for January. Revisions of the components for previous periods are also carried out when publishing the January figures. Seasonally adjusted volume figures for previous periods and growth rates based on such figures are thus affected. For more information, se About seasonal adjustment.
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Normally, the debt data will not be published if there is a risk of identification, i.e. that the figures can be traced back to the reporting unit. Exceptions here are Norges Bank and the Norwegian Public Service Pension Fund, who do not object to such identification.
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The revision of international standards and major changes in accountancy laws may result in a gap in the time series data. A change of sectors may have the same result, for instance if a financial corporation moves from one institutional sector to another. We try as far as possible to make adjustments for statistical breaks in our calculations of transactions (break corrections).
New institutional sector classification
As from January 2012, the Norwegian institutional sector classification has been revised in line with the international classification. This change implies a break in stock time series between February and March 2012.
Accuracy and reliability
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The C2 statistics are mainly derived from the financial statistics. Errors and inconsistencies in these statistics will also affect C2. In this context, we refer to the sections on sources of error and uncertainty from these statistics. The sources of inconsistencies for data from the Norwegian Public Service Pension Fund and Export Credit Norway will also be of the same type as the statistics mentioned above.
The response rate for the C2 statistics are 100 per cent.
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The statistics show preliminary figures. Data may be revised in future publications.
About seasonal adjustment
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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
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On the basis of public holidays and holiday period in July and December the intensity of the supply and demand of credit fluctuates through the year. This complicates a direct comparison of gross debt figures from one month to the next. To adjust for these relations the gross debt is seasonally adjusted for the actual levels, so that one can analyse the underlying credit indicator development.
The credit indicator statistics publishes five seasonally adjusted series; K1, K2, K2 foreign currency, K2-households and K2-non financial corporations. The seasonally adjusted figures for K2-foreign currency is not a result of own seasonal adjustment, but a residual from the difference of seasonally adjusted K2 and the seasonally adjusted K1.
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Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
No pre-treatment.
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.
No calendar adjustment of any kind is performed.
Methods for trading/working day adjustment
No correction.
Correction for moving holidays
No correction.
National and EU/euro area calendars
Definition of series not requiring calendar adjustment.
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 series.
Comments: We have chosen compulsory multiplicative decompositions. The program chose automatically this option until 2008, and it is now incorporated as a claim.
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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.
Mixed indirect approach where the seasonal adjustment of components possibly occurs using different approaches and software.
Comments: The total is computed independently of the components. The last component is computed as a residual of the difference between the total and the other 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.
Only part of the time series is used to estimate the correction factors and the model.
Comments: The data is used for K1 and K2 from January 1989 to the last observed December figure. For K2-households and K2-non financial corporations the data is used from December 1987 to the last observed December figure.
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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.
Comments: The program for seasonal adjustment with new seasonal components is made once a year, but seasonally adjusted figures are audited in accordance with audited raw data.
Concurrent versus current adjustment
Controlled current adjustment: Forecasted calendar factors derived from a current adjustment are used to seasonally adjust the new or revised raw data. The numbers are revised when new, fixed factors are estimated once a year.
Horizon for published revisions
The entire time series is revised in the event of a re-estimation of the seasonal factors.
Comments: The whole series which enters into seasonal adjustment is audited once a year. Apart from this, the elderly seasonal adjustment figures are only audited when unadjusted figures are been audited.
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Evaluation of seasonally adjustment data
Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.
Quality measures for seasonal adjustment
No quality measures for seasonal adjustment assessment are used.
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Seasonal adjustment of short time series
All series are sufficiently long to perform an optimal seasonal adjustment.
Treatment of problematic series
Νο series are treated in a special way, irrespective of their characteristics.
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Data availability
Raw and seasonally adjusted data are available.
Press releases
In addition to raw data, at least one of the following series is released: pre-treated, seasonally adjusted, seasonally plus working day adjusted, trend-cycle series.
