Report on the seasonal adjustment pilot project.

Transcription

1 UNECE Statistical Division, Economic Statistics Section January 2008

2 Contents Executive Summary...3 Introduction...5 I The objective and scope of the pilot project...5 II Seasonal adjustment practices in the SEECIS countries...6 III Original Index of Industrial Production statistics...7 IV. Production of seasonally adjusted estimates...9 IV.1 Country specific information...9 IV.2 Methodological information...10 IV.3 SA method and software used for the SA pilot project...10 IV.4 Steps involved in producing the SA estimates...11 V. Results of the SA pilot project...12 VI. Problems with the SA by ESS...16 VII. Future of SA in ESS...17 VIII. Lessons learned and recommendations...17 References...19 Appendix I - Country Results...20 I. Armenia...21 II Azerbaijan...26 III. Belarus...31 IV. Georgia...36 V. Kazakhstan...47 VI. Kyrgyzstan...52 VII. Moldova...57 VIII. Russian Federation...62 IX. Tajikistan...68 X. Turkmenistan - estimates not suitable for seasonal adjustment...73 XI. Ukraine...74 XII. Uzbekistan - estimates not suitable for seasonal adjustment...79 XIII. Bosnia and Herzegovina...80 XIV. The former Yugoslav Republic of Macedonia...85 XV. Montenegro...90 XVI. Serbia

3 Report on the seasonal adjustment pilot project. Executive Summary Background In 20 and 2007 the Economic Statistics Section (ESS) of UNECE conducted studies of the short-term economic statistics disseminated on-line by NSOs of South-Eastern European and Commonwealth of Independent States (SEECIS) countries. The purpose of the studies was to evaluate the international comparability of the shortterm economic statistics of the SEECIS countries. One of the main factors identified for non-comparability was a lack of seasonally adjusted economic statistics. It was therefore decided to conduct a pilot project on seasonal adjustment. The project had two main objectives: Results To evaluate the possibility of the ESS to produce and publish reliable and accurate seasonally adjusted time series for countries for which such series are not available (short-term perspective). To build up experience and technical capacity so as to be able to support countries to compile and disseminate seasonally adjusted time series (longerterm perspective). In the pilot project the monthly index of industrial production (IIP) from SEECIS countries has been seasonally adjusted. The IIP was selected as a typical economic short-term statistics. The results of the pilot project are encouraging: It has been possible to compile seasonally adjusted (SA) IIP series for 14 of the 16 selected SEECIS countries. The SA series have been closely analyzed and are believed to be of satisfactory quality to be published in the UNECE Statistical Database. The key findings are: 14 out of 16 SEECIS countries publish monthly total IIPs that are suitable for seasonal adjustment. For all these it is possible for the ESS to seasonally adjust the original monthly series. In 2 out of the 14 countries (Russia and Georgia) the NSO do compile seasonally adjusted IIP. For the remaining 12 countries the seasonally adjusted series produced by ESS are suitable for publication. For Russia and Georgia (the only two SEECIS countries which produce seasonally adjusted IIP series) the series produced by ESS are very similar (almost identical in case of Russian Federation) to those published by the countries themselves. 3

4 The SA series provide a much clearer picture of real changes. It is often difficult to understand the real change in the original IIP free of any calendar and seasonal effects. Publication of the SA series would facilitate better comparison of the SEECIS countries with the remaining countries in the UNECE region. As a side effect the pilot project allowed ESS to update the original monthly IIP time series in the UNECE Statistical Database with the statistics provided by the countries and consequently improve quality and accuracy of UNECE statistics. Lessons learned The following are key lessons learned from the project: It is possible for the ESS to produce seasonally adjusted series for selected key economic short-term statistics on a regular basis for SEECIS countries who do not publish seasonally adjusted series. Besides IIP this might include also, for example, employment, BOP current account and retail trade statistics. The production and publication of seasonally adjusted series will require additional resources. Where feasible countries should be encouraged to undertake SA themselves. There is a need of methodological and/or technical assistance to countries to support the implementation of seasonal adjustment methods. Recommendations Below are key recommendations from the SA pilot project: ESS should disseminate the report and relevant background information to the SEECIS countries and following a consultation process (involving SA experts from other NSOs) release it on the UNECE website. ESS should disseminate as experimental series the SA IIP series for 12 of the 16 SEECIS countries. ESS should share its experiences with the SEECIS countries and play an active role in assisting these countries in the introduction of seasonal adjustment practices. Activities such as technical assistance or organising of expert meetings should be considered. Depending on the experiences with the IIP statistics, in its future work ESS should consider for selected countries seasonally adjusting: national accounts, BOP current account, employment and retail trade statistics. 4

5 Introduction 1. In 20 and 2007 the Economic Statistics Section (ESS) of United Nations Economic Commission for Europe (UNECE) conducted studies of the short-term economic statistics disseminated on-line by the National Statistical Organizations (NSOs) of selected UNECE countries. 2. The papers summarizing the findings (International comparability of shortterm statistics in the CIS countries 20 and International comparability of shortterm statistics in the CIS and South Eastern European countries 2007) identified a number of factors contributing to poor international comparability of the statistics produced by the South-Eastern European and Commonwealth of Independent States (CIS) countries. One of the main factors identified was a lack of seasonally adjusted (SA) estimates. 3. Consequently ESS decided to conduct a pilot project aimed at producing seasonally adjusting estimates of selected short-term economic statistics. 4. This paper presents the preliminary seasonally adjusted estimates produced by ESS. 1 It starts by outlining the objectives of the project, summarizing the current use of seasonal adjustment (SA) in the selected South-Eastern European and CIS countries. It then looks at the steps involved in producing the SA estimates and finally presents the derived statistics. I The objective and scope of the pilot project 5. The main objectives of the SA pilot project were: To evaluate the possibility of the ESS to produce and publish reliable and accurate seasonally adjusted time series for countries for which such series are not available (short-term perspective). To build up experience and technical capacity so as to be able to support countries to compile and disseminate seasonally adjusted time series (longerterm perspective). 6. ESS decided to limit the scope of the project to the following South-Eastern European and CIS countries (from now onwards referred to as (SEECIS) countries. Armenia Azerbaijan Belarus Georgia Kazakhstan Kyrgyzstan Moldova 1 Prepared by Artur Andrysiak, Economic Statistics Section, UNECE 5

6 Russian Federation Tajikistan Turkmenistan Ukraine Uzbekistan Bosnia and Herzegovina Montenegro Serbia The former Yugoslav Republic of Macedonia 7. Following a short evaluation it has been decided that the total monthly Index of Industrial Production (IIP) statistics should be used for the purpose of SA project. Ideally the IIP statistics should be seasonally adjusted at the lower sections (NACE rev 1: C,D,E): "Mining and quarrying", "Manufacturing" and "Electricity, gas and water supply" as they tend to follow different seasonal patterns, however due to limited availability of statistics at the more detailed level, ESS decided to seasonally adjust the total monthly IIP. UNECE maintains the original total IIP statistics for almost all of its 56 Member Countries. Furthermore international comparison of the original monthly total IIP statistics is almost impossible as for the majority of countries seasonal and calendar effects have very significant impact on the IIP statistics. Finally, by producing and publishing seasonally adjusted IIP statistics, UNECE would provide the public with a unique set of statistics that could be used to make better timely cross country analysis of the changes in economic development in UNECE region. II Seasonal adjustment practices in the SEECIS countries 8. Of the 16 SEECIS countries only 2 publish any seasonally adjusted IIP statistics: Georgia and Russian Federation, whilst Ukraine foresees the application of seasonal adjustment once the necessary timeseries will be accumulated. 9. To allow users to draw some conclusions from the monthly and quarterly statistics which are free of the seasonal and calendar effects a significant number of SEECIS countries continue to publish their statistics as cumulative or smpy=100 (same month previous year equals 100) indices. Although this sort of presentation of statistics can provide users with some idea about changes in economic developments, nevertheless they still contain seasonal and calendar effects which can lead to misleading conclusions (moving holidays, different number of trading days, outliers, etc). 10. UNECE strongly believes that although publishing statistics as cumulative or smpy=100 indices can provide users with useful information, it is essential that foremost NSOs publish fixed base indices or at the very least pm=100 (previous month equals 100) indices. The cumulative or smpy=100 indices are not internationally comparable and can not be easily converted into fixed base or pm=100 indices. 6

7 III Original Index of Industrial Production statistics 11. ESS collects its statistics from a wide range of sources. The statistics collected are available to the public from the UNECE Statistical Database ( To minimize the burden on the national organizations, the statistics are usually collected from other international organizations or from the websites of the NSOs and Central Banks (CBs). Prior to the beginning of the SA pilot project, the IIP statistics for the SEECIS countries were collected from CISSTAT, NSOs websites and periodicals. In the majority of instances the monthly IIP statistics available to the ESS had/have the following problems: For a significant number of SEECIS countries only timeseries of cumulative indices were/are available, with most countries moving to publishing IIP statistics as fixed based indices or month on month movements only in the recent months/years; Majority of the SEECIS countries did/do not publish long timeseries, and the timeseries maintained by ESS were/are often out-of-date and did/do not include any historical revisions. 12. The quality of the seasonally adjusted estimates is directly related to the quality of original estimates. To ensure that the statistics used for the pilot project were of suitable quality ESS sent a request for the IIP statistics to all SEECIS countries. Out of the 16 countries included in the project, 7 provided ESS with monthly timeseries of the IIP total, 1 confirmed the accuracy of statistics in the ESS Statistical Database and 1 confirmed that only cumulative indices were available. For the remaining countries ESS used the information from the NSOs/CBs websites or from the CISSTAT. The graph and the table below summarize how the original data used for the pilot project was obtained/derived. Graph 1. Overview of the original data used for the SA pilot project Responded to UNECE request for original monthly IIP statistics Quality of the original IIP statistics used for seasonal adjustment Responded and provided statistics 2 Good 7 7 Responded but did not provide statistics 3 Average Did not respond 11 Poor 2 7

8 Table 1. Overview of the original data used for the SA pilot project SEECIS countries Responded to ESS request, and provided statistics to ESS Suitable statistics available from the NSO s or CB s websites Quality of Original/Raw statistics available to ESS Comments regarding data used for the SA project Armenia Y Y Good From 01/2000 to 12/20 data obtained directly from the NSO for 01/2007 onwards data from the NSO s website Azerbaijan Y N Good From 01/1996 to 04/2007 data obtained directly from the NSO for /2007 onwards data from CISSTAT (pm=100) Belarus Y Y Good From 01/2000 to 12/20 data obtained directly from the NSO for 01/2007 onwards data from the NSO s website Georgia N (no monthly timeseries available but quarterly timeseries published) Good, quarterly only, no suitable monthly timeseries available. The fixed base indices available from the UNECE Statistical Database have been derived using the cumulative statistics available from CISSTAT. They are indicative only and not suitable for SA. Kazakhstan Y Y Good From 01/2000 to 12/20 data obtained directly from the NSO for 01/2007 onwards data from the NSO s website Kyrgyzstan Republic of Moldova Russian Federation Only responded confirming the accuracy of statistics in UNECE Statistical Database for the later years. No statistics provided to ESS. N Y (timeseries of smpy=100 available, for the latest periods 20=100) N (no timeseries available, but pm=100 and smpy=100 are available for latest period) Average Average From 01/2000 to 12/2002 data from CISSTAT pm=100, for 01/2003 onwards data from the NSO s website (smpy=100) The fixed base indices available from the UNECE Statistical Database have been derived using the pp=100 statistics available from CISSTAT. Y Y Good From 01/2000 to 12/20 data obtained directly from the NSO for 01/2007 onwards data from the NSO s website Tajikistan N Y (Industrial Production in current prices statistics are available. The Tajikistan NSO did not respond to UNECE request for IIP constant prices/volume statistics). Average Turkmenistan N N Poor, derived from cumulative indices, not suitable for SA. The fixed base indices available from the UNECE Statistical Database have been derived using the pp=100 statistics available from CISSTAT. The fixed base indices available from the UNECE Statistical Database have been derived using the cumulative statistics available from CISSTAT. They are indicative only and not suitable for SA. Ukraine Y Y Good From 01/2000 to 12/20 data obtained directly from the NSO for 01/2007 onwards data from the NSO s website Uzbekistan Confirmed that only cumulative indices are available. No statistics provided to ESS. N Poor, derived from cumulative indices, not suitable for SA. The fixed base indices available from the UNECE Statistical Database have been derived using the cumulative statistics available from CISSTAT. They are indicative only and not suitable for SA. Bosnia and Herzegovina N Y Good From 01/2000 to present data obtained from the NSO s website Macedonia, FYR Y Y Good From 01/2000 to present data obtained directly from the NSO Montenegro N Y (timeseries only back to 2004) Good but timeseries too short From 01/2004 to present data obtained from the NSO s website Serbia N Y Good 01/1999 to 12/2004 from the website of the NSO; 01/20 to present from the website of CB; pm=100 previous month equals 100. cmpy=100 same month previous year equals

9 13. In deciding on the quality and suitability of original monthly IIP statistics for seasonal adjustment, ESS took the following factors into consideration: Source ESS considers statistics provided directly by the NSOs or sourced from the NSO s or CB s websites to be the most appropriate for seasonal adjustment. Statistics sourced from other international organizations or other sources are still appropriate but where choice is available preference was given to those sourced directly from national sources; Availability of long timeseries ESS strongly prefers updating a complete timeseries each time a new observation is published. This means that at any point in time the timeseries published by UNECE are up-to-date and contain all historical revisions. Consequently, when deciding on the suitability of the original estimates for the seasonal adjustment, ESS looked at how the statistics were updated. Statistics for which complete timeseries are updated each time are viewed to be of higher quality then those for which only latest observations are updated; and Availability of fixed based indices - as mentioned on a number of occasions in this paper, ESS strongly believes that countries should publish their statistics as fixed based indices or at the very least pp=100 indices. ESS believes that publishing only cumulative or smpy=100 statistics contradicts international good practices. Producing an accurate fixed based index from cumulative or smpy=100 statistics is almost impossible. Consequently, when deciding on the suitability of the original estimates for the seasonal adjustment project, ESS looked at how the fixed base indices published in UNECE Statistical Database were derived. Fixed based indices derived from cumulative or smpy=100 statistics were judged to be of poor quality. IV. IV.1 Production of seasonally adjusted estimates Country specific information 14. The quality and accuracy of seasonal adjustment can be improved by having detailed country specific information. ESS obtained country specific calendars for all of the SEECIS countries from a commercial website. The calendars were used to improve the accuracy of SA and in particular treatment of calendar effects (trading days and moving holidays). 15. To further improve the quality and accuracy of SA it would be desirable to possess detail understanding of methodological, economic, political and other factors contributing to the movements in the IIP. This sort of information is usually only available to the compilers of statistics with only a small subset being available to the general public. Unfortunately ESS does not possess any country specific IIP information and for the pilot project had to rely on the seasonal adjustment methods to correctly adjust for the above factors. 9

10 IV.2 Methodological information 16. During the last two years significant progress was made in terms of availability of international manuals and guidelines for Seasonal Adjustment. In the past most of the information was available from US Census Bureau and Bank of Spain and some fragmented information from Eurostat, European Central Bank as well as NSOs and CBs. The information available ranged from technical information intended for experts (US Census Bureau and Bank of Spain) to training manuals and methodological papers describing individual country practices. However there was no International Guidelines tailored to the needs of the NSOs. This has been recently addressed with two handbooks: Seasonal Adjustment Methods and Practices released in 2007 and produced by the Hungarian Central Statistical Office in cooperation with other NSOs; Manual on Seasonal Adjustment to be released by Eurostat sometime in Both documents are intended for NSOs and offer detailed information suitable for both SA experts and more importantly statisticians. 17. For the pilot project ESS relied mostly on: the Handbook on Seasonal Adjustment; Demetra software documentation available from Eurostat; technical information available from the US Census Bureau and Bank of Spain; and SA training papers from the Australian Bureau of Statistics. IV.3 SA method and software used for the SA pilot project 18. The Handbook on Seasonal Adjustment summarizes the findings of survey into the seasonal adjustment methods used by the EU region and other NSOs. The survey found that majority of NSOs used TRAMO/SEATS (70%) method of SA, followed by X-12-ARIMA (52%), X-11-ARIMA (19%) and other methods (10%) of SA. Of all the surveyed NSOs, 39% used TRAMO/SEATS as the stand-alone method with some others using the combination of TRAMO/SEATS and X-12-ARIMA. 19. ESS decided that both TRAMO/SEATS and X-12-ARIMA are the most appropriate methods for the SA pilot project. Both methods are widely used by NSOs and have been proven to produce reliable SA estimates. ESS decided to use TRAMO/SEATS as the primary method of SA and only use X-12-ARIMA for checking and validation of the TRAMO/SEATS results. 20. Having decided on the method of SA, ESS next looked at the possible choice of the SA software. Based on the choice of SA methods, two options were available: X-12-ARIMA available free of charge from the US Census Bureau and the TRAMO/SEATS available free of charge from the National Bank of Spain; Demetra available free of charge from Eurostat. 10

11 21. Of the two possible options, Eurostat s Demetra was chosen as the preferred one. Having been developed by Eurostat it appears to be well suited to the needs of International Organizations. It includes both TRAMO/SEATS and X-12-ARIMA in one software package. Some of the additional features include: user friendly interface, extensive SA tests and diagnostics, automatic model choice, automatic module which greatly simplifies the SA process (including automatic model selection) and country calendars for selected countries. 22. For the purpose of the SA pilot project ESS used Demetra version The improved version of Demetra version 2.1 only become available in July The major improvements occurred on the X-12-ARIMA side where now it is possible to use country calendars. ESS believes that the SA results produced using version 2.04 and TRAMO/SEATS method should be identical/almost identical to those produced using version 2.1. IV.4 Steps involved in producing the SA estimates 23. The testing of the original estimates for seasonality was largely left to the Demetra software and no manual computations were done by ESS. The quality of SA and the quality diagnostics of the SA results produced by Demetra were carefully analyzed but no additional manual computations were completed. Due to limited expertise, the ARIMA model selection was largely left to the TRAMO/SEATS, with the exception of the Airline model. In addition to the estimates being seasonally adjusted by each method with the system chosen ARIMA model, SA estimates were also produced using the Airline model with default initial parameters. As recommended in the international guidelines strong preference was given to parsimonious (simpler) ARIMA models. 24. The Demetra Manual, provides the following information about regressors: Many economical activities are strongly influenced by calendar effects like varying number of trading days and holidays in each recorded period. In order to improve the seasonal modelling and the trend estimation, such effects should be eliminated before the decomposition. No: A trading day correction is not performed. 1 regressor: A working day correction is performed: There are no differences in the economical activity between the working days (Monday to Friday) but between these and non-working days (Saturday, Sunday). The varying number of these days is considered. 2 regressors: A working day and length-of-period correction are performed: There are no differences in the economical activity between the working days (Monday to Friday) but between these and nonworking days (Saturday, Sunday). The varying number of these days and also the total number of days per period are considered. 6 regressors: A trading day correction is performed: There are differences in the economical activity between all days of the week. The varying number of these days is considered. 7 regressors: A trading day correction and length-of-period are performed: There are differences in the economical activity between all days of the week. The varying number of these days and also the total number of days per period are considered. 25. The original estimates for the countries were adjusted using both methods with automatic model selection and manual model selection of the Airline model and number of scenarios for each method. The Demetra manual strongly recommends using 1 or 2 regressors for shorter series and 6 or 7 regressors for longer series. Furthermore the manual suggests that the seasonal adjustment expert should 11

12 experiment with different numbers of regressors and based on the quality diagnostics choose the most suitable method. Consequently, for the purpose of this exercise ESS seasonally adjusted the IIP statistics using both SA methods and 0, 1, 2, 6 and 7 regressors. 26. The Handbook on Seasonal Adjustment, provides the following information about outliers: Outliers are data that do not fit in the tendency of the time series observed, that fall outside the range expected on the basis of the typical pattern of the trend- and seasonal components. Typically some oneoff economic or social event is in the background of values deemed as outliers, the effect of which cause the time series data to diverge from the earlier tendency for a shorter or longer period of time. Regarding industrial production such events may include for example the closing of a dominant manufactory in the industry causing a drop in output, since any further data indicate only the output of the remaining companies. The enactment of a new law or implementation of a new form of subsidy, or the levying of a new type of tax may have similar effect. if the adjustments ignore the outliers, then the estimation for both the trend- and seasonal components will be biased. As mentioned previously, ESS does not have the detailed background information about the IIP series. For that reason, the treatment of outliers had to be left to the SA methods. 27. Validation of the results was done in line with the international guidelines and general practices in the NSOs. The most important validation being the graphical inspection. In addition to the graphical inspection ESS analyzed the quality statistics by looking at the statistics on residuals including the Ljung-Box Q-statistics and the normality test. 28. In addition to the standard validation, ESS also did basic analytical analysis of the SA results. Two basic validations were completed: General analysis of the SA results, looking at the volatility of the SA estimates, their correlation with the original estimates and overall economic performance of the country. Detail analysis of the correlation between the SA and original same month previous year (smpy) movements. Although the original smpy can be still affected by the number of trading days and moving holidays, one would expect for most periods the original smpy movements to have some correlation with the SA smpy movements. 29. Finally where possible results produced by ESS where compared against the results produced by the countries themselves (Russian Federation and Georgia). V. Results of the SA pilot project 30. Out of the 16 SEECIS countries ESS was able to seasonally adjust the total IIP statistics for 14 countries, with data for Uzbekistan and Turkmenistan not being suitable for SA. ESS was also unable to obtain good quality original monthly IIP statistics for Georgia and used quarterly statistics for the purpose of this exercise. Furthermore out of the 14 countries that ESS was able to produce SA estimates, 12

13 statistics for 4 countries should be used with caution: Moldova, Kyrgyzstan, Tajikistan and Montenegro. SA estimates produced by ESS for Russian Federation and Georgia should not be published as these estimates are available from the countries themselves. For the remaining countries the SA estimates are of satisfactory quality and in view of ESS should be made available to the public and continue to be seasonally adjusted. 31. The graph and table below contain a summary of seasonally adjusted results produced by the ESS for the SEECIS countries. The detailed results are presented in Annex I. In line with the international guidelines the information presented in Annex I includes: original, SA and trend estimates, the information about the SA method, parameters used in the SA process and other relevant information. ESS made a conscious decision not to publish the working/trading day adjusted data. In ESS view the quality of national calendars possessed by international organizations are not high enough to put such a strong weight on the working/trading day adjusted data. Unlike other international organizations, ESS feels that countries should ensure that original, SA and where possible trend estimates are published. Countries should treat the working/trading day adjusted data as additional information that should not be substituted for the original estimates. Graph 2. Summary of the SA pilot project results Quality of the SA IIP statistics produced by ESS Suitablility of SA estimates for publication in UNECE Statistical Database Suitable, but should not be published as 2 country SA available Suitable, but should be used w ith caution Good Average No SA estimates produced 8 4 No SA estimates produced 2 Suitable 13

14 Table 2. Summary of the SA pilot project results SEECIS countries Quality of Original/Raw statistics available to ESS Count ry Calen dars availa ble Detail, underlying information about the series available Preferred Method, ARIMA Model, Number of Regressors and % of Outliers Armenia Good Y N TRAMO/SEATS; ; 2; 2.22% Quality of Seasonally Adjusted statistics produced by ESS Good Seasonally adjusted estimates suitable to be publish in the UNECE Statistical Database YES Azerbaijan Good Y N TRAMO/SEATS; ; 7; 1.47% Belarus Good Y N TRAMO/SEATS; ; 2; 0.8% Georgia Good, quarterly only, Y N TRAMO/SEATS; 011 no suitable monthly 011; 7; 0.0%; timeseries available. (Mining); TRAMO/SEATS; ; 1; 0.0%; (Manufacturing); TRAMO/SEATS; ; 7; 0.0%; (Electricity); Kazakhstan Good Y N TRAMO/SEATS; ; 1; 0.00% Kyrgyzstan Average Y N TRAMO/SEATS; ; 2; 1.12% Good Good Good Good Average, estimates should be used with caution Average YES YES YES, however as seasonally adjusted estimates are produced by the country there is no need to use the ESS seasonally adjusted estimates. YES YES, but should be used with caution Republic of Moldova Averages Y N TRAMO/SEATS; ; 0; 2.44% YES, but should be used with caution Russian Good Y N TRAMO/SEATS; 011 Good YES Federation 010; 1; 3.03% Tajikistan Averages Y N TRAMO/SEATS; 010 Average YES, but should be used 011; 2; 1.63% with caution Turkmenista Poor, derived from Y N n.a. n.a. n.a. n cumulative indices, not suitable for SA. Ukraine Good Y N TRAMO/SEATS; 011 Good YES 011; 2; 0.00% Uzbekistan Poor, derived from Y N n.a. n.a. n.a. cumulative indices, not suitable for SA. Bosnia and Herzegovina Macedonia, FYR Montenegro Good Y N TRAMO/SEATS; ; 6; 0.00% Good Y N TRAMO/SEATS; ; 6; 0.60% Good but Y N TRAMO/SEATS; 011 timeseries too short 011; 1; 0.00% Serbia Good Y N TRAMO/SEATS; ; 6; 3.45% Good Good Average, due to small number of observations the SA estimates should be treated with caution Average YES YES YES, but should be used with caution YES 14

15 32. In deciding on the quality of the SA IIP statistics produced by ESS and their suitability for publication in UNECE Statistical Database, ESS took the following factors into consideration: Quality of the underlying original statistics - the SA estimates can only be as good as the original estimates from which they were derived (paragraph 13). Consequently if the original statistics used for the SA pilot project were judged to be of average quality irrespective of all the other factors, the derived SA estimates can at the very most be only judged to be of average quality. Length of the original timeseries - ESS decided that the minimum length of the monthly timeseries required to produce satisfactory SA estimates is 7 years. Consequently, SA estimates produced base on the shorter series (Montenegro) were judged to be of average quality. Quality diagnostics - ESS analyzed the quality statistics by looking at the statistics on residuals including the Ljung-Box Q-statistics and the normality test. For the SA estimates to be judged as good quality, the produced SA estimates had to be within the acceptable boundaries for each of the tests (paragraph 27). Validation of the results - as mentioned previously ESS conducted both general analysis of the SA results, (looking at the volatility of the SA estimates, their correlation with the original estimates and overall economic performance of the country) and detail analysis of the correlation between the SA and original same month previous year (smpy) movements (paragraph 28). Information on outliers and regressors - in deciding on the quality of SA estimates ESS took into consideration the per cent of outliers and number of regressors used and their appropriateness for the length of the series (paragraph 24 and 26). Comparision with country results in the case of Russian Federation and Georgia, the SA estimates were confronted against the SA estimates produced by the countries. This has permitted ESS to validate its overall SA procedures and assess how close its SA estimates are to the country estimates. Availability of country specific information in all instances ESS used country specific calendars. Unfortunately, ESS was unable to use any additional country information to further improve the quality of SA estimates. 33. The results of the SA pilot project are very encouraging. As shown in the Table 2, ESS was able to produce SA estimates for 14 of the 16 selected countries. The SA estimates have been closely analyzed and are believed to be of satisfactory quality to be published in the UNECE Statistical Database. Below is a list of the key findings: 14 out of 16 SEECIS countries publish monthly total IIPs that are suitable for seasonal adjustment. For all these it is possible for the ESS to seasonally adjust the original monthly series. In 2 out of the 14 countries (Russia and Georgia) the NSO do compile seasonally adjusted IIP. 15

16 For the remaining 12 countries the seasonally adjusted series produced by ESS are suitable for publication. For Russia and Georgia (the only two SEECIS countries which produce seasonally adjusted IIP series) the series produced by ESS are very similar (almost identical in case of Russian Federation) to those published by the countries themselves. The SA series provide a much clearer picture of real changes. It is often difficult to understand the real change in the original IIP free of any calendar and seasonal effects. Publication of the SA series would facilitate better comparison of the SEECIS countries with the remaining countries in the UNECE region. As a side effect the pilot project allowed ESS to update the original monthly IIP time series in the UNECE Statistical Database with the statistics provided by the countries and consequently improve quality and accuracy of UNECE statistics. VI. Problems with the SA by ESS 34. The SA pilot project has proven that ESS can and should produce SA estimates for selected countries. There is a strong demand for internationally comparable statistics and ESS has to ensure that a basic set of internationally comparable statistics is available for the UNECE region. Nevertheless there are a number of problems that reduce ESS ability to produce high quality SA estimates, these are: Accuracy and timeliness of the original estimates the quality of the SA estimates is directly related to the original estimates. ESS tries to ensure that the original country statistics it publishes are of the highest quality. Unfortunately the statistics for some countries are not easily available and continue to be compiled and published in a non-internationally comparable format. Below is a list of main problems impacting on the quality of the original estimates available to UNECE: o Some countries continue to publish only the recent observations which makes obtaining a good quality timeseries almost impossible. o A number of countries continue to publish their statistics in a noninternationally comparable formats. For example in UNECE region countries continue to publish IIP statistics as cumulative indices or smpy=100. Limited resources to correctly validate each series ESS statisticians have limited time which does not always allows them to carefully and in detail analyze the SA estimates produced for each country. Lack of country specific knowledge to spot obvious problems with the SA estimates ESS statisticians do not have the detailed understanding of factors contributing to the movements in the series and therefore may not be able to spot problems with treatment of extreme values and others that might be obvious to national experts. 16

17 VII. Future of SA in ESS 35. The SA Pilot project has clearly shown that ESS should produce SA estimates for countries for which such estimates are not available. Having SA estimates for all countries greatly improves international comparability of short-term statistics across UNECE region. 36. Subject to consultation with SEECIS countries, SA experts from other NSOs and availability of sufficient quality original timeseries, ESS will continue to produce SA estimates for the countries for which such estimates are not available. Subject to resource availability the SA project might be extended to include other economic statistics. Following is a summary of ESS future SA policy: Issue SA method SA software Seasonal reanalysis Concurrent/Forward factor Statistics published on the website Base year ESS Policy Primary TRAMO/SEATS; secondary X12-ARIMA Demetra Unless there are special circumstances, once a year, at the beginning of calendar year with all parameters fixed for the following 12 months Forward factor method, with seasonal factors produces after seasonal reanalysis for the following 12 months SA and Original estimates for each country; publishing of trend estimates and the forward factors to be implemented at a later date For fixed base indices, the seasonally adjusted estimates to be re-based so that the base year is the same as original estimates and equals 100 VIII. Lessons learned and recommendations 37. The SA pilot project allowed ESS to build on its experience in the SA area. The following are key lessons learned from the SA pilot project: It is possible for the ESS to produce seasonally adjusted series for selected key economic short-term statistics on a regular basis for SEECIS countries who do not publish seasonally adjusted series. Besides IIP this might include also, for example, employment, BOP current account and retail trade statistics. The production and publication of seasonally adjusted series will require additional resources. Where feasible countries should be encouraged to undertake SA themselves. There is a need of methodological and/or technical assistance to countries to support the implementation of seasonal adjustment methods. 17

18 38. Below are key recommendations from the SA pilot project ESS should disseminate the report and relevant background information to the SEECIS countries and following a consultation process (involving SA experts from other NSOs) release it on the UNECE website. ESS should disseminate as experimental series the SA IIP series for 12 of the 16 SEECIS countries. ESS should share its experiences with the SEECIS countries and play an active role in assisting these countries in the introduction of seasonal adjustment practices. Activities such as technical assistance or organising of expert meetings should be considered. Depending on the experiences with the IIP statistics, in its future work ESS should consider for selected countries seasonally adjusting: national accounts, BOP current account, employment and retail trade statistics. Cartoon, The New Yorker, December 10, 1984 One man to another as they walk down the street at Christmas time: Yes, I m somewhat depressed, but seasonally adjusted I m probably happy enough. 18

19 References Artur Andrysiak and Chiara Radieli (2007). Report on international comparability of short-term statistics in the CIS and South-East European countries. UNECE Hungarian Central Statistical Office (2007). Seasonal Adjustment Methods and Practices. US Census Bureau. The X-12-ARIMA Seasonal Adjustment Program. Bank of Spain. Statistics and Econometrics Software. Eurostat. Eurostat Seasonal Adjustment Project. Australian Bureau of Statistics (20). Information Paper, An Introduction Course on Time Series Analysis Electronic Delivery CA2570B1002A31DB?OpenDocument 19

20 Appendix I - Country Results List of acronyms/codes used Graphs Country name SA_Arim_7R original/raw series Seasonally adjusted estimates produced using the X-12- ARIMA method, 7 regressors, best ARIMA model chosen by the system, country calendars not used; Tren_Arim_7R Trend estimates produced using the X-12-ARIMA method, 7 regressors, best ARIMA model chosen by the system, country calendars not used; SA_TS_7R_noHo Tren_TS_7R_noHo SA_TS_7R_Ho Tren_TS_7R_Ho Seasonally adjusted estimates produced using the TRAMO/SEATS method, 7 regressors, best ARIMA model - chosen by the system, country calendars not used; Trend estimates produced using the TRAMO/SEATS method, 7 regressors, best ARIMA model chosen by the system, country calendars not used; Seasonally adjusted estimates produced using the TRAMO/SEATS method, 7 regressors, best ARIMA model - chosen by the system, country calendars used; Trend estimates produced using the TRAMO/SEATS method, 7 regressors, best ARIMA model chosen by the system, country calendars used; SA_TS_7R_Ho_ Seasonally adjusted estimates produced using the TRAMO/SEATS method, 7 regressors, manually selected model, calendars used; Tren_TS_7R_Ho_ Trend estimates produced using the TRAMO/SEATS method, 7 regressors, manually selected model, calendars used; SA_TS_Airline Tren_TS_Airline Seasonally adjusted estimates produced using the TRAMO/SEATS method, 7 regressors, Airline model with default initial parameters; Trend estimates produced using the TRAMO/SEATS method, 7 regressors, Airline model with default initial parameters; Text SA seasonally adjusted pm=100 previous month equals 100 smpy=100 same month previous year equals

21 I. Armenia Original Estimates - Comments The original total IIP timeseries shows a lot of volatility. The available series starts in January The series does follow an annual pattern, which appears to be changing over time. In particular the periods after November 2002 appear to show a more clear and very different seasonal pattern from the earlier years. The quality of the original estimates is good, as they have been provided to ESS directly by the National Statistical Service of the Republic of Armenia. Original Estimates - Details: Source National Statistical Service of the Republic of Armenia Length of the timeseries: January 2000 to present Quality: Good Issues: Volatile series, changing seasonality Seasonally Adjusted Estimates - Comments: Highly volatile original series, changing seasonality and lack of information for the significant movements makes the seasonal adjustment particularly challenging for this country. The treatment of the earlier years 2000 to 2003 and in particular September 2002, would be simplified with having more detailed information about the underlying factors contributing to the unusual movements which are different from the annual pattern of the later years. The different SA scenarios considered by ESS produced similar results for the later years but somewhat different results for the earlier years, in particular the results for the first two years of the timeseries differ considerably. Overall, upon closer inspection the seasonality in the original estimates can be observed, but appears to be changing over time. The TRAMO/SEATS ARIMA model selected by the system produces results that do not pass all of the quality diagnostic in particular Box-Pierce on squared residual showing evidence of autocorrelations. For that reason a fixed model was used which produced satisfactory results. TRAMO/SEATS reduced the number of regressors from 7 to 0. The chosen model appears to produce acceptable results. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with high correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 2000 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 0 Country Calendars YES Availability of detailed information NO Quality of SA estimates Average Suitable for publication on the Internet YES Model passes all diagnostic test YES 21

22 Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 2.22 % Demetra Report for final seasonally adjusted and trend estimates 22

23 Jul- Sep- Nov- Jan-07 Mar-07 May-07 Report on the seasonal adjustment pilot project. Graph I.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan-00 Mar-00 May-00 Jul-00 Sep-00 Nov-00 Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Jan- Mar- May- Armenia SA_TS_0R_noHo_ Tren_TS_0R_noHo_

24 Graph I.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Jan-00 Mar-00 May-00 Jul-00 Sep-00 Nov-00 Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Jan- Mar- May- Jul- Sep- Nov- Jan-07 Mar-07 May-07 Armenia SA_TS_0R_Ho_ SA_TS_1R_Ho SA_TS_0R_noHo SA_TS_Airline SA_Arim_6R SA_Arim_2R SA_Arim_0R SA_Arim_Airline 24

25 Graph I.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- 07 Feb- 07 Mar- 07 Apr- 07 May- 07 Jun- 07 Armenia SA_TS_0R_Ho_ SA_TS_1R_Ho SA_TS_0R_noHo SA_TS_Airline SA_Arim_6R SA_Arim_2R SA_Arim_0R SA_Arim_Airline 25

26 II Azerbaijan Original Estimates - Comments The original total IIP timeseries shows a lot of volatility. The available series starts in January From January 1996 to January 20 the total IIP grows at a steady rate, however from February 20 a very high growth rate was experience. The series does not follow a clear annual pattern. The quality of the original estimates is good, as they have been provided to ESS directly by the State Statistical Committee of the Republic of Azerbaijan. Original Estimates - Details: Source State Statistical Committee of the Republic of Azerbaijan Length of the timeseries: January 1996 to present Quality: Good Issues: Volatile series, showing very high rate of growth after April 20 Seasonally Adjusted Estimates - Comments: The seasonally adjusted estimates for Azerbaijan should be treated with some caution. The volatility of the series and the high growth from February 20 makes the seasonal adjustment challenging. In particular the treatment of the extremely high values for Feb 20 and Mar 20 would be simplified with having more detailed information about the underlying factors contributing to high growth rates during these periods. The different SA scenarios considered by ESS produced relatively similar results but significant differences could be observed for the most recent and the earliest results. Overall upon closer inspection the seasonality in the original estimates can be observed, but appears to be changing over time. The preferred model appears to produce acceptable results. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with high correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 1996 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 7 Country Calendars YES Availability of detailed information NO Quality of SA estimates Average Suitable for publication on the Internet YES Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 1.47 % 26

27 Demetra Report for final seasonally adjusted and trend estimates 27

28 Oct- Jan- Apr- Jul- Oct- Jan-07 Apr-07 Apr- Jul- Jan- Report on the seasonal adjustment pilot project. Graph II.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan-96 Apr-96 Jul-96 Oct-96 Jan-97 Apr-97 Jul-97 Oct-97 Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Azerbaijan SA_TS_7R_Ho Trend_TS_7R_Ho 28

29 Graph II.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Jan-96 Apr-96 Jul-96 Oct-96 Jan-97 Apr-97 Jul-97 Oct-97 Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan-07 Apr-07 Azerbaijan SA_TS_1R_Ho SA_TS_2R_Ho SA_TS_7R_Ho SA_TS_0R_noHo SA_Arima_6R SA_Arima_1R SA_Arima_0R SA_Arima_Airline 29

30 Graph II.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan-07 Feb-07 Mar-07 Apr-07 Azerbaijan SA_TS_1R_Ho SA_TS_2R_Ho SA_TS_7R_Ho SA_TS_0R_noHo SA_Arima_6R SA_Arima_1R SA_Arima_0R SA_Arima_Airline 30

31 III. Belarus Original Estimates - Comments The original total IIP timeseries shows some volatility. The available series starts in January The series does follow an annual pattern, which appears to be changing over time. In particular the period after December 20 appears to show different seasonal pattern. The quality of the original estimates is good, as they have been provided to ESS directly by the Ministry of Statistics and Analysis of the Republic of Belarus. Original Estimates - Details: Source The Ministry of Statistics and Analysis of the Republic of Belarus Length of the timeseries: January 1997 to present Quality: Good Issues: Changing seasonality Seasonally Adjusted Estimates - Comments: The changing seasonality and lack of information for the significant movements in 20 and 2007 makes the seasonal adjustment challenging. In particular the treatment of the 1 st half of 20 and in particular Mar 20 would be simplified with having more detailed information about the underlying factors contributing to the unusual movements which are different from the annual pattern of the previous years. The different SA scenarios considered by ESS produced similar results. Overall upon closer inspection the seasonality in the original estimates can be observed, but appears to be changing over time. The preferred model appears to produce good results. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with high correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 1997 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 2 Country Calendars YES Availability of detailed information NO Quality of SA estimates Good Suitable for publication on the Internet YES Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 0.8 % 31

32 Demetra Report for final seasonally adjusted and trend estimates 32

33 Sep- Jan- May- Sep- Jan-07 May-07 May- Report on the seasonal adjustment pilot project. Graph III.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan-97 May-97 Sep-97 Jan-98 May-98 Sep-98 Jan-99 May-99 Sep-99 Jan-00 May-00 Sep-00 Jan-01 May-01 Sep-01 Jan-02 May-02 Sep-02 Jan-03 May-03 Sep-03 Jan-04 May-04 Sep-04 Jan- Belarus SA_TS_2R_Ho Trend_TS_2R_Ho 33

34 Graph III.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Jan-97 Apr-97 Jul-97 Oct-97 Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan-07 Apr-07 Belarus SA_TS_0R_noHo SA_TS_1R_Ho SA_TS_2R_Ho SA_TS_2R_noHo SA_TS_Airl SA_Arim_0R SA_Arim_1R SA_Arim_6R SA_Arim_7R SA_Arim_Airl 34

35 Graph III.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- 07 Feb- 07 Mar- 07 Apr- 07 May- 07 Belarus SA_TS_0R_noHo SA_TS_1R_Ho SA_TS_2R_Ho SA_TS_2R_noHo SA_TS_Airl SA_Arim_0R SA_Arim_1R SA_Arim_6R SA_Arim_7R SA_Arim_Airl 35

36 IV. Georgia Original Estimates - Comments The original monthly total IIP timeseries has been compiled by the ESS using the information from the CISSTAT. The fixed based indices published by the ESS have been derived from cumulative statistics published on the CISSTAT website. ESS believes that these estimates are indicative only and should not be used for any analytical work. The website of Statistics Georgia contains good quality quarterly and annual series of the total IIP. Unfortunately ESS was unable to locate any monthly total IIP timeseries. The statistics Georgia did not respond to ESS request for the monthly total IIP timeseries. For the purpose of this exercise UNECE made a decision to use the quarterly subtotals for Mining and Quarrying; Manufacturing; and Electricity, gas and water supply. Statistics Georgia publishes both original and seasonally adjusted estimates. The main reason for UNECE trying to produce seasonally adjusted estimates for these series was to check whether the seasonally adjusted estimates produced by UNECE would be close to those published by the countries. The original quarterly IIP (Mining and Quarrying; Manufacturing; and Electricity, gas and water supply) timeseries show some volatility. The available series start in March 2001 and continue to September 20. The series do not follow a clear annual pattern. The quality of the original estimates is good, as they have been downloaded from the website of Statistics Georgia. Original Monthly Estimates - Details: Source CISSTAT Length of the timeseries: January 1995 to November 20 Quality: Poor Issues: Derived from cumulative statistics, not suitable for further analysis Original Quarterly Estimates - Details: Source Statistics Georgia Length of the timeseries: March 2001 to September 20 Quality: Good Issues: For the purpose of SA pilot project only the subtotals for Mining and Quarrying; Manufacturing; and Electricity, gas and water supply were used. Seasonally Adjusted Estimates - Comments: The main objective of producing seasonally adjusted estimates for Georgia was to check the validity and appropriateness of the ESS seasonally adjusted estimates. ESS believes that countries should produce seasonally adjusted estimates themselves and that such estimates will always be of better quality then any seasonally adjusted 36

37 estimates produced by international organizations. NSOs have access to the essential detailed information underlying the movements in the series and explaining significant events. This information in conjunction with excellent understanding of economic and social factors affecting the series, places NSOs in the best position to produce seasonally adjusted estimates of their statistics. Overall the results of the validation of ESS seasonally adjusted estimates are encouraging. In most instances ESS produced SA estimates that are relatively close to the official seasonally adjusted statistics. The results are summarized in Graphs IV.1 IV.6. In all instances the seasonally adjusted estimates produced by ESS are relatively close to these produced by Statistics Georgia. It is clear that ESS does not have the detailed information that would further improve the seasonally adjusted statistics. Finally, the comparison of the results has highlighted potential problems with the country calendars used by ESS. For IIP Manufacturing the seasonally adjusted statistics produced without using country calendars produced better results (and more in line with these produced by Statistics Georgia) then the SA statistics produced with using the country calendars. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted March 2001 to September 20 timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model (Mining); (Manufacturing); (Electricity); Preferred Number of Regressors 7 (Mining); 1 (Manufacturing); 7 (Electricity); Country Calendars YES Availability of detailed information NO Quality of SA estimates Good Suitable for publication on the Internet YES, but because seasonally adjusted estimates are produced by the country there is no need to use the ESS seasonally adjusted estimates. Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 0.0% (Mining); 0.0% (Manufacturing); 0.0% (Electricity); 37

38 Demetra Report for final seasonally adjusted and trend estimates Mining and Quarrying 38

39 Demetra Report for final seasonally adjusted and trend estimates Manufacturing 39

40 Demetra Report for final seasonally adjusted and trend estimates Electricity, gas and water supply 40

41 Graph IV.1 Original, UNECE seasonally adjusted (produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models) and Statistics Georgia seasonally adjusted estimates complete timeseries Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Jan- Mar- May- Jul- Mining and Quarrying Mining and Quarrying_Arim_1R Mining and Quarrying_TS_1R_HO Mining and Quarrying_TS_Airline Mining and Quarrying_SA_Georgia Mining and Quarrying_Arim_0R Mining and Quarrying_TS_0R_noHO Mining and Quarrying_Arim_Airline Mining and Quarrying_TS_7R_011011_HO 41

42 Graph IV.2 Original, UNECE seasonally adjusted and Statistics Georgia seasonally adjusted estimates complete timeseries Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Jan- Mar- May- Jul- Mining and Quarrying Mining and Quarrying_TS_7R_011011_HO Mining and Quarrying_SA_Georgia 42

43 Graph IV.3 Original, UNECE seasonally adjusted (produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models) and Statistics Georgia seasonally adjusted estimates complete timeseries Manufacturing Manufacturing_TS_0R_noHO Manufacturing_TS_7R_HO Manufacturing_Arim_Airline Manufacturing_TS_7R_011011_HO Manufacturing_SA_Georgia Manufacturing_Arim_0R Manufacturing_TS_1R_HO Manufacturing_TS_7R_noHO Manufacturing_TS_7R_011011_noHO Manufacturing_TS_Airline 43

44 May- Jul- Report on the seasonal adjustment pilot project. Graph IV.4 Original, UNECE seasonally adjusted and Statistics Georgia seasonally adjusted estimates complete timeseries Mar- Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Jan- Manufacturing Manufacturing_TS_1R_HO Manufacturing_SA_Georgia 44

45 Graph IV.5 Original, UNECE seasonally adjusted (produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models) and Statistics Georgia seasonally adjusted estimates complete timeseries Electricity, gas and water supply Electricity, gas and water supply_arim_6r Electricity, gas and water supply_ts_0r_noho Electricity, gas and water supply_ts_airline Electricity, gas and water supply_ts_7r_011011_ho Electricity, gas and water supply_arim_0r Electricity, gas and water supply_arim_7r Electricity, gas and water supply_arim_airline Electricity, gas and water supply_ts_7r_011011_noho Electricity, gas and water supply_sa_georgia 45

46 Graph IV.6 Original, UNECE seasonally adjusted and Statistics Georgia seasonally adjusted estimates complete timeseries Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Jan- Mar- May- Jul- Electricity, gas and water supply Electricity, gas and water supply_ts_7r_011011_noho Electricity, gas and water supply_sa_georgia 46

47 V. Kazakhstan Original Estimates - Comments The original total IIP timeseries shows some volatility and changing seasonality. The available series starts in January With exception of the last year or so the series follows a very clear annual pattern and grows at steady rate. The quality of the original estimates is good, as they have been provided to ESS directly by the Agency of Statistics of the Republic of Kazakhstan. Original Estimates - Details: Source The Agency of Statistics of the Republic of Kazakhstan Length of the timeseries: January 2000 to present Quality: Good Issues: Different annual pattern in the most recent periods Seasonally Adjusted Estimates - Comments: With exception of the last year, the different SA scenarios considered by ESS produced relatively similar results. The results could probably be further improved with having more detailed information about the underlying factors contributing to movements and changing seasonality in the recent periods. In particular the results for November and December 20 could be further improved with additional information. Overall upon closer inspection the seasonality in the original estimates can be observed, but appears to be changing over times. The preferred model appears to produce acceptable results. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with high correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 2000 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 1 Country Calendars YES Availability of detailed information NO Quality of SA estimates Good Suitable for publication on the Internet YES Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 0.00 % 47

48 Demetra Report for final seasonally adjusted and trend estimates 48

49 Apr- Jul- Oct- Jan-07 Apr-07 Jan- Report on the seasonal adjustment pilot project. Graph V.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan- Apr- Jul- Oct- Kazakhstan SA_TS_1R_Ho Tren_TS_1R_Ho 49

50 Graph V.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Jan-00 Mar-00 May-00 Jul-00 Sep-00 Nov-00 Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Jan- Mar- May- Jul- Sep- Nov- Jan-07 Mar-07 May-07 Kazakhstan SA_TS_6R_Ho SA_TS_1R_Ho SA_TS_0R_noHo SA_TS_7R_noHo SA_TS_Airline SA_Arim_6R SA_Arim_0R SA_Arim_Airline 50

51 Graph V.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- 07 Feb- 07 Mar- 07 Apr- 07 May- 07 Jun- 07 Kazakhstan SA_TS_6R_Ho SA_TS_1R_Ho SA_TS_0R_noHo SA_TS_7R_noHo SA_TS_Airline SA_Arim_6R SA_Arim_0R SA_Arim_Airline 51

52 VI. Kyrgyzstan Original Estimates - Comments The original total IIP timeseries shows a lot of volatility. The available series starts in January 2000 and has been produced by linking statistics from two data sources: for period January 2000 to December 2001 pm=100 from CISSTAT and from January 2002 to present smpy=100 (same month previous year = 100) from the website of the National Statistical Committee of Kyrgyz Republic. ESS does not believe that smpy=100 timeseries can be accurately converted into a fixed base index. Furthermore statistics should only publish in this format as supplementary information, provided that a complete timeseries of fixed based indices data is also available. Nevertheless, despite significant reservations about the smpy=100 statistics and the accuracy of the historical information collected from CISSTAT in absence of any fixed based indices and pm=100 timeseries, ESS made a decision to produced and publish the fixed base index using the available statistics. Users need to use extra caution when using this information for any analytical purposes. Finally the fixed base timeseries produced by ESS appears to follow a clear annual pattern. Original Estimates - Details: Source January 2000 to December pm=100 from CISSTAT January 2002 to present - smpy=100 from the website of the National Statistical Committee of Kyrgyz Republic Length of the timeseries: January 2000 to present Quality: Below average Issues: Linked series using two different data sources, very volatile, for period from January 2002 fixed based indices derived from cmpy=100 timeseries. Seasonally Adjusted Estimates - Comments: The seasonally adjusted estimates for Kyrgyzstan should be treated with a lot of caution. The quality and volatility of the original series make the seasonal adjustment challenging and the results questionable. The quality of seasonally adjusted estimates could be significantly improved with more accurate original series and detailed information about the underlying factors contributing to movements in the series. The different SA scenarios considered by ESS produced somewhat different results with significant differences being observed for the high and low points each year. Overall upon closer inspection the seasonality in the original estimates can be observed. The preferred model appears to produce below satisfactory results. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided acceptable results, with some correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 2000 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model

53 Preferred Number of Regressors 2 Country Calendars YES Availability of detailed information NO Quality of SA estimates Below average (estimates should be used with caution) Suitable for publication on the Internet NO Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 1.12 % Demetra Report for final seasonally adjusted and trend estimates 53

54 Jan- Apr- Jul- Oct- Jan-07 Apr-07 Oct- Report on the seasonal adjustment pilot project. Graph VI.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan- Apr- Jul- Kyrgyzstan SA_TS_2R_Ho Tren_TS_2R_Ho 54

55 Graph VI.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Jan-00 Mar-00 May-00 Jul-00 Sep-00 Nov-00 Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Jan- Mar- May- Jul- Sep- Nov- Jan-07 Mar-07 May-07 Kyrgyzstan SA_TS_2R_noHo SA_TS_0R_noHo SA_TS_6R_Ho SA_TS_2R_Ho SA_TS_1R_Ho SA_Arim_1R SA_Arim_0R SA_Arim_Airli 55

56 Graph VI.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- 07 Feb- 07 Mar- 07 Apr- 07 May- 07 Kyrgyzstan SA_TS_2R_noHo SA_TS_0R_noHo SA_TS_6R_Ho SA_TS_2R_Ho SA_TS_1R_Ho SA_Arim_1R SA_Arim_0R SA_Arim_Airli 56

57 VII. Moldova Original Estimates - Comments The original monthly total IIP timeseries has been compiled by the ESS using the information from the CISSTAT. The fixed based indices published by the ESS have been derived using pp=100 statistics published on the CISSTAT website. The monthly pp=100 statistics available from CISSTAT are not updated and do not include any historical revisions. ESS believes that these statistics are of average quality and are most likely not in line with the latest IIP statistics compiled by the National Bureau of Statistics of the Republic of Moldova. The website of Moldova s NSO contains some IIP statistics, ESS was able to recently locate the pm=100 and smpy=100 as well as cumulative statistics for the latest periods. These statistics are available from reports published by National Bureau of Statistics of Republic of Moldova. Unfortunately, ESS was unable to locate any timeseries of the monthly total IIP statistics. The National Bureau of Statistics of Republic of Moldova did not respond to ESS request for the monthly total IIP timeseries. Original Estimates - Details: Source CISSTAT Length of the timeseries: January 1997 to present Quality: Average Issues: Derived from pp=100 statistics (do not include any revisions, might be different from these produced by NSO) Seasonally Adjusted Estimates - Comments: Producing SA estimates for Moldova proved to be a significant challenge. Although the original series appear to follow some annual pattern, both TRAMO/SEATS and X12-Arima struggled with producing acceptable SA estimates. In both instances the number of regressors were reduced by the system to 0. The final seasonally adjusted estimates still show a very high level of volatility. The preferred model appears to produce acceptable results. The graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with some correlation between the smpy SA and original movements. Overall the SA estimates for Moldova are of average quality and some consideration should be given to the appropriateness and accuracy of the original series (does not include any revisions and might be different from the official IIP). Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 1997 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 0 Country Calendars YES Availability of detailed information NO 57

58 Quality of SA estimates Average Suitable for publication on the Internet YES Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 0.80 % Demetra Report for final seasonally adjusted and trend estimates 58

59 Sep- Jan- May- Sep- Jan-07 May-07 Jan- May- Sep-04 Report on the seasonal adjustment pilot project. Graph VII.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan-97 May-97 Sep-97 Jan-98 May-98 Sep-98 Jan-99 May-99 Sep-99 Jan-00 May-00 Sep-00 Jan-01 May-01 Sep-01 Jan-02 May-02 Sep-02 Jan-03 May-03 Sep-03 Jan-04 May-04 Moldova SA_TS_0R Tren_TS_0R 59

60 Graph VII.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Jan-97 Apr-97 Jul-97 Oct-97 Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan-07 Apr-07 Moldova SA_TS_0R SA_Arim_0R SA_Arim_Airline 60

61 Graph VII.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan-04 Feb-04 Mar-04 Apr-04 May-04 Jun-04 Jul-04 Aug-04 Sep-04 Oct-04 Nov-04 Dec-04 Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan-07 Feb-07 Mar-07 Apr-07 May-07 Moldova SA_TS_0R SA_Arim_0R SA_Arim_Airline 61

62 VIII. Russian Federation Original Estimates Comments The original total IIP timeseries shows some volatility. The available series starts in January The series does follow annual pattern, which appears to be changing over time. In particular the period after June 2004 appears to show a different seasonal pattern from the earlier years. The quality of the original estimates is good, as they have been provided to ESS directly by the Federal State Statistics Service (ROSSTAT). Original Estimates - Details: Source Federal State Statistics Service (ROSSTAT) Length of the timeseries: January 1999 to present Quality: Good Issues: Changing seasonality Seasonally Adjusted Estimates - Comments: The main objective of producing seasonally adjusted estimates for Russian Federation was to check the validity and appropriateness of the ESS seasonally adjusted estimates. ESS believes that countries should produce seasonally adjusted estimates themselves and that such estimates will always be of better quality then any seasonally adjusted estimates produced by international organizations. NSOs have access to the essential detailed information underlying the movements in the series and explaining significant events. This information in conjunction with excellent understanding of economic and social factors affecting the series, places NSOs in the best position to produce seasonally adjusted estimates of their statistics. Overall the results of the validation of ESS seasonally adjusted estimates are very encouraging. The SA estimates produced by ESS (using X-12-Arima Airline model Graph VIII.2) are identical to those provided to UNECE by ROSSTAT. The SA estimates chosen as the preferred set of statistics and produced using TRAMO/SEATS method (look summary below) are very similar to those provided by ROSSTAT - Graph VIII.1 The changing seasonality and lack of information for the significant movements makes the seasonal adjustment challenging. In particular the treatment of the earlier years and in particular December 1999 and April 2000 as well as May 20 would be simplified with having more detailed information about the underlying factors contributing to the unusual movements which are different from the annual pattern of the remaining years. The different SA scenarios considered by ESS produced similar results. Overall upon closer inspection the seasonality in the original estimates can be observed, but appears to be changing over time. The preferred model appears to produce good results, but the system reduced the number of regressors from 7 to 1. The graphic analysis and same period previous month comparison of the original and 62

63 SA estimates provided satisfactory results, with high correlation between the smpy SA and original movements. When 7 fixed regressors with the best TRAMO/SEATS chosen Airline model ( ) or fixed 7 regressors and the Airline model are used the produced results do not fully pass all the quality diagnostics: significant Ljung-Box on residual - showing evidence of autocorrelations. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 1999 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 1 Country Calendars YES Availability of detailed information NO Quality of SA estimates Good Suitable for publication on the Internet YES Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 3.03 % Demetra Report for final seasonally adjusted and trend estimates 63

64 Graph VIII.1 Original and final seasonally adjusted and trend estimates complete timeseries including SA estimates provided by ROSSTAT Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan-07 Russia SA_TS_1RHo Trend_TS_1RHo SA_Russia 64

65 Graph VIII.2 Original and best fitting seasonally adjusted and trend estimates complete timeseries including SA estimates provided by ROSSTAT Russia SA_Arim_Airl Trend_Arim_Airl SA_Russia 65

66 Graph VIII.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries including SA estimates provided by ROSSTAT Jan-99 Mar-99 May-99 Jul-99 Sep-99 Nov-99 Jan-00 Mar-00 May-00 Jul-00 Sep-00 Nov-00 Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Jan- Mar- May- Jul- Sep- Nov- Jan-07 Mar-07 Russia SA_Arim_7R SA_Arim_0R SA_Arim_Airl SA_TS_7RnoHo SA_TS_1RHo SA_TS_7RF_Ho_ SA_TS_7RF_Ho SA_Russia 66

67 Graph VIII.4 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years including SA estimates provided by ROSSTAT Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- 07 Feb- 07 Mar- 07 Russia SA_Arim_7R SA_Arim_0R SA_Arim_Airl SA_TS_7RnoHo SA_TS_1RHo SA_TS_7RF_Ho_ SA_TS_7RF_Ho SA_Russia 67

68 IX. Tajikistan Original Estimates - Comments The original monthly total IIP timeseries has been compiled by the ESS using the information from the CISSTAT. The fixed based indices published by the ESS have been derived using pp=100 statistics published on the CISSTAT website. The monthly pp=100 statistics available from CISSTAT are not updated and do not include any historical revisions. ESS believes that these statistics are of average quality and are most likely not in line with the latest IIP statistics compiled by the State Statistical Committee of Tajikistan. The website of Tajikistan s NSO contains industrial production statistics expressed in current prices Tajik somoni; unfortunately the statistics can t be converted into Index of Industrial Production (constant prices/volume measure). The State Statistical Committee of Tajikistan did not respond to ESS request for the monthly total IIP timeseries or to ESS enquiry about the industrial production statistics available from its website. Original Estimates - Details: Source CISSTAT Length of the timeseries: January 1997 to present Quality: Average Issues: Derived from pp=100 statistics (do not include any revisions, might be different from these produced by NSO) Seasonally Adjusted Estimates - Comments: The seasonally adjusted estimates for Tajikistan should be treated with some caution. The high volatility of the original series and lack of clear annual pattern makes the seasonal adjustment challenging. The seasonal adjustment results could be further improved with having more detailed information about the underlying factors contributing to unusual movements. The different SA scenarios considered by ESS produced relatively similar results. However the seasonally adjusted estimates appear to also be very volatile, displaying large month-to-month movements. The preferred model appears to produce acceptable results. The graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with high correlation between the smpy SA and original movements. Overall the SA estimates for Tajikistan are of average quality and some consideration should be given to the appropriateness and accuracy of the original series (does not include any revisions and might be different from the official IIP). Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 1997 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 2 68

69 Country Calendars YES Availability of detailed information NO Quality of SA estimates Average Suitable for publication on the Internet YES Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 1.63 % Demetra Report for final seasonally adjusted and trend estimates 69

70 Sep- Jan- May- Sep- Jan-07 May-07 May- Report on the seasonal adjustment pilot project. Graph XI.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan-97 May-97 Sep-97 Jan-98 May-98 Sep-98 Jan-99 May-99 Sep-99 Jan-00 May-00 Sep-00 Jan-01 May-01 Sep-01 Jan-02 May-02 Sep-02 Jan-03 May-03 Sep-03 Jan-04 May-04 Sep-04 Jan- Tajikistan SA_TS_2R_HO Tren_TS_2R_HO 70

71 Graph XI.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Jan-97 Apr-97 Jul-97 Oct-97 Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan-07 Apr-07 Tajikistan SA_TS_7R_HO SA_TS_2R_HO SA_TS_1R_HO SA_TS_0R_noHO SA_TS_7R_noHO SA_Arim_2R SA_Arim_1R SA_Arim_0R 71

72 Graph XI.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- 04 Feb- 04 Mar- 04 Apr- 04 May- 04 Jun- 04 Jul- 04 Aug- 04 Sep- 04 Oct- 04 Nov- 04 Dec- 04 Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- 07 Feb- 07 Mar- 07 Apr- 07 May- 07 Tajikistan SA_TS_7R_HO SA_TS_2R_HO SA_TS_1R_HO SA_TS_0R_noHO SA_TS_7R_noHO SA_Arim_2R SA_Arim_1R SA_Arim_0R 72

73 X. Turkmenistan - estimates not suitable for seasonal adjustment Original Estimates - Comments The original monthly total IIP timeseries has been compiled by the ESS using the information from the CISSTAT. The fixed based indices published by the ESS have been derived from cumulative statistics published on the CISSTAT website. ESS believes that these estimates are indicative only and should not be used for any analytical work. The State Committee of Turkmenistan on Statistics did not respond to ESS request for the monthly total IIP timeseries. Original Estimates - Details: Source CISSTAT Length of the timeseries: January 1993 to December 2002 Quality: Poor Issues: Derived from cumulative statistics, not suitable for further analysis Seasonally Adjusted Estimates - Comments: Original estimates not suitable for seasonal adjustment. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed NO Length of the seasonally adjusted n.a. timeseries Preferred SA Method n.a. Preferred ARIMA Model n.a. Preferred Number of Regressors n.a. Country Calendars n.a. Availability of detailed information n.a. Quality of SA estimates n.a. Suitable for publication on the Internet n.a. Model passes all diagnostic test n.a. Ljung-Box on residual n.a. Box-Pierce on residual n.a. Normality n.a. Percentage of outliers n.a. 73

74 XI. Ukraine Original Estimates Comments The original total IIP timeseries shows some volatility. The available series starts in January The series does follow annual pattern. The quality of the original estimates is good, as they have been provided to ESS directly by the State Statistics Committee of Ukraine. Original Estimates - Details: Source The State Statistics Committee of Ukraine Length of the timeseries: January 2000 to present Quality: Good Issues: Different annual pattern in the most recent periods Seasonally Adjusted Estimates - Comments: With exception of couple of months in 20 and 2007, the different SA scenarios considered by ESS produced relatively similar results. The results could probably be further improved with having more detailed information about the underlying factors contributing to movements and changing seasonality in the recent periods. Overall upon closer inspection the seasonality in the original estimates can be observed, but appears to be changing over times. The preferred model appears to produce acceptable results. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with high correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 2000 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 2 Country Calendars YES Availability of detailed information NO Quality of SA estimates Good Suitable for publication on the Internet YES Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 0.00% 74

75 Demetra Report for final seasonally adjusted and trend estimates 75

76 Graph XI.1 Original and final seasonally adjusted and trend estimates complete timeseries Ukraine SA_TS_2R_Ho Tren_TS_2R_Ho 76

77 Graph XI.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Ukraine SA_TS_2R_noHo SA_TS_0R_noHo SA_TS_2R_Ho SA_TS_1R_Ho SA_TS_Airline SA_Arim_0R SA_Arim_Airline 77

78 Graph XI.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan-07 Feb-07 Mar-07 Apr-07 Ukraine SA_TS_2R_noHo SA_TS_0R_noHo SA_TS_2R_Ho SA_TS_1R_Ho SA_TS_Airline SA_Arim_0R SA_Arim_Airline 78

79 XII. Uzbekistan - estimates not suitable for seasonal adjustment Original Estimates - Comments The original monthly total IIP timeseries has been compiled by the ESS using the information from the CISSTAT. The fixed based indices published by the ESS have been derived from cumulative statistics published on the CISSTAT website. ESS believes that these estimates are indicative only and should not be used for any analytical work. The State Committee of Statistics of the Republic of Uzbekistan responded to the ESS request, confirming that only cumulative IIP statistics are available for Uzbekistan. Unfortunately ESS was not provided with any statistics by the State Committee of Statistics of the Republic of Uzbekistan, hence the use of the CISSTAT data. Original Estimates - Details: Source CISSTAT Length of the timeseries: January 1993 to July 20 Quality: Poor Issues: Derived from cumulative statistics, not suitable for further analysis Seasonally Adjusted Estimates - Comments: Original estimates not suitable for seasonal adjustment. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed NO Length of the seasonally adjusted n.a. timeseries Preferred SA Method n.a. Preferred ARIMA Model n.a. Preferred Number of Regressors n.a. Country Calendars n.a. Availability of detailed information n.a. Quality of SA estimates n.a. Suitable for publication on the Internet n.a. Model passes all diagnostic test n.a. Ljung-Box on residual n.a. Box-Pierce on residual n.a. Normality n.a. Percentage of outliers n.a. 79

80 XIII. Bosnia and Herzegovina Original Estimates Comments The original total IIP timeseries shows a lot of volatility. The available series starts in January The series for the earlier years does not follow a clear annual pattern and only in the last 3 years of the timeseries some annual pattern emerges. The quality of the original estimates is average, as they have been downloaded from the website of the Federal Office of Statistics of Federation of Bosnia and Herzegovina. The website provides comprehensive statistics for the recent months, but does not include a long timeseries, therefore the timeseries maintained by ESS does not include any historical revisions. The Federal Office of Statistics of Federation of Bosnia and Herzegovina or the Agency for Statistics of Bosnia and Herzegovina did not provide the timeseries to the ESS. Original Estimates - Details: Source Federal Office of Statistics of Federation of Bosnia and Herzegovina Length of the timeseries: January 1998 to present Quality: Average Issues: Extremely volatile, difficult to observe seasonality in the earlier years Seasonally Adjusted Estimates - Comments: The seasonally adjusted estimates for Bosnia and Herzegovina should be treated with caution. The high volatility of the original series and lack of clear annual pattern makes the seasonal adjustment very challenging. The seasonal adjustment results could be further improved with having more detailed information about the underlying factors contributing to unusual movements. The different SA scenarios considered by ESS produced relatively similar results. However the seasonally adjusted estimates appear to also be very volatile, displaying large month-to-month movements. The preferred model appears to produce acceptable results. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with high correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 1998 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 6 Country Calendars YES Availability of detailed information NO Quality of SA estimates Average estimates should be used with caution Suitable for publication on the Internet YES Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 0.00 % 80

81 Demetra Report for final seasonally adjusted and trend estimates 81

82 Jan- Mar- May- Jul- Sep- Nov- Jan-07 Mar-07 May-07 Report on the seasonal adjustment pilot project. Graph XIII.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan-98 Mar-98 May-98 Jul-98 Sep-98 Nov-98 Jan-99 Mar-99 May-99 Jul-99 Sep-99 Nov-99 Jan-00 Mar-00 May-00 Jul-00 Sep-00 Nov-00 Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan- Mar- May- Jul- Sep- Nov- Bosnia and Herzegovina SA_TS_6R_HO Tren_TS_6R_HO 82

83 Graph XIII.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Bosnia and Herzegovina SA_TS_7R_HO SA_TS_6R_HO SA_TS_2R_HO SA_TS_1R_HO SA_TS_2R_noHO SA_TS_0R_noHO SA_Arim_0R SA_Arim_Airline 83

84 Graph XIII.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan-07 Feb-07 Mar-07 Apr-07 May-07 Bosnia and Herzegovina SA_TS_7R_HO SA_TS_6R_HO SA_TS_2R_HO SA_TS_1R_HO SA_TS_2R_noHO SA_TS_0R_noHO SA_Arim_0R SA_Arim_Airline 84

85 XIV. The former Yugoslav Republic of Macedonia Original Estimates Comments The original total IIP timeseries shows a lot of volatility. The available series starts in January The series does follow annual pattern, which appears to be changing over time. In particular the period after January 2004 appears to show a somewhat different seasonal pattern from the earlier years. Furthermore the magnitude of the month on month movements is quite significant. The quality of the original estimates is good, as they have been provided to ESS directly by the State Statistical Office Republic of Macedonia. Original Estimates - Details: Source State Statistical Office Republic of Macedonia Length of the timeseries: January 1993 to present Quality: Good Issues: Changing seasonality Seasonally Adjusted Estimates - Comments: The changing seasonality, high volatility and lack of information for the significant movements makes the seasonal adjustment challenging. The quality of the seasonal adjustment could be further improved with more detailed information about the underlying factors contributing to the unusual movements. The different SA scenarios considered by ESS produced similar results. Overall upon closer inspection the seasonality in the original estimates can be observed, but appears to be changing over time. The preferred model appears to produce good results. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with reasonable correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 1993 to present timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model Preferred Number of Regressors 6 Country Calendars YES Availability of detailed information NO Quality of SA estimates Good Suitable for publication on the Internet YES Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 0.60 % 85

86 Demetra Report for final seasonally adjusted and trend estimates 86

87 Jul-04 Jan- Jul- Jan- Jul- Jul-03 Jan-04 Report on the seasonal adjustment pilot project. Graph XIV.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan-93 Jul-93 Jan-94 Jul-94 Jan-95 Jul-95 Jan-96 Jul-96 Jan-97 Jul-97 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Macedonia SA-TS6RH Trend-TS6RH 87

88 Graph XIV.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Jan-93 Apr-93 Jul-93 Oct-93 Jan-94 Apr-94 Jul-94 Oct-94 Jan-95 Apr-95 Jul-95 Oct-95 Jan-96 Apr-96 Jul-96 Oct-96 Jan-97 Apr-97 Jul-97 Oct-97 Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Macedonia SA-TS0RnH SA-TS6RH SA-TSAirline SA_Arim_0R SA_Arim_6R SA_Arim_7R SA_Arim_Airline 88

89 Graph XIV.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- 04 Feb- 04 Mar- 04 Apr- 04 May- 04 Jun- 04 Jul- 04 Aug- 04 Sep- 04 Oct- 04 Nov- 04 Dec- 04 Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Macedonia SA-TS0RnH SA-TS6RH SA-TSAirline SA_Arim_0R SA_Arim_6R SA_Arim_7R SA_Arim_Airline 89

90 XV. Montenegro Original Estimates Comments The original total IIP timeseries is very short and shows a lot of volatility. The available series starts in January The series does not appear to follow any clear annual pattern. Overall the timeseries is too short for any in-depth analysis. The quality of the original estimates is average, as they have been downloaded from the website of the MONSTAT. The website provides comprehensive statistics for the recent months, but does not include a long timeseries, therefore the timeseries maintained by ESS does not include any historical revisions. MONSTAT did not respond to ESS request for the IIP statistics. Original Estimates - Details: Source MONSTAT Length of the timeseries: January 2004 to present, 2004=100 Quality: Good Issues: Very volatile, too short for any in-depth analysis Seasonally Adjusted Estimates - Comments: The available original timeseries is too short to perform a comprehensive seasonal analysis. The seasonally adjusted estimates produced by ESS are indicative only and should not be used for any analytical purposes. In addition to the short timeseries the volatility of the series and lack of information about the underlying factors contributing to the significant movement, in particular the December 2004 make the seasonal adjustment very challenging. The X-12-ARIMA did not produce any seasonally adjustment estimates for Montenegro as all options produced results which would fail the quality diagnostics. The different TRAMO/SEATS scenarios considered by ESS produced somewhat different results. Overall the preferred option produced acceptable results which should be used with a lot of caution. The situation should improve over the next 2-3 years as the available series becomes long enough for proper seasonal adjustment. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with acceptable correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 2004 to present, timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model , Preferred Number of Regressors 1 Country Calendars YES Availability of detailed information NO Quality of SA estimates Average to poor, should be used with caution, the original timeseries too short; Suitable for publication on the Internet YES with caution Model passes all diagnostic test YES Ljung-Box on residual Acceptable 90

91 Box-Pierce on residual n.a. Normality Acceptable Percentage of outliers 0.00 % Demetra Report for final seasonally adjusted and trend estimates 91

92 Graph XV.1 Original and final seasonally adjusted and trend estimates complete timeseries Jan- 04 Feb- 04 Mar- 04 Apr- 04 May- 04 Jun- 04 Jul- 04 Aug- 04 Sep- 04 Oct- 04 Nov- 04 Dec- 04 Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- 07 Feb- 07 Mar- 07 Apr- 07 May- 07 Montenegro_2004=100 SA_TS_1R_HO Tren_TS_1R_HO 92

93

94 Graph XV.2 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models complete timeseries Jan- 04 Feb- 04 Mar- 04 Apr- 04 May- 04 Jun- 04 Jul- 04 Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May Montenegro_2004=100 SA_TS_Airline SA_TS_0R_noHO SA_TS_1R_HO SA_TS_2R_HO 94

95 Graph XV.3 Original and seasonally adjusted series produced using: both TRAMO/SEATS and X-12-ARIMA methods, varying number of regressors and different ARIMA models recent years Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- 07 Feb- 07 Mar- 07 Apr- 07 May- 07 Montenegro_2004=100 SA_TS_Airline SA_TS_0R_noHO SA_TS_1R_HO 95

96 XVI. Serbia Original Estimates Comments The original total IIP timeseries is extremely volatile and does not show any significant growth during the period from January 1999 to present. The original series for Serbia has been produced by linking two separate data sources for January 1999 to December py=100 (average previous year equals 100) from the Statistical Office of the Republic of Serbia; and for January 20 to present fixed base index available from the National Bank of Serbia. For the years 20 and 20 the two sources publish almost identical results, however due to better timeliness and inclusion of revisions the CB was chosen as the main source. For the purpose of this exercise only data from January 2000 to present was used. For more details look below. The series does follow annual pattern, which appears to be changing over time and appears to be much more evident after January Furthermore the magnitude of the month on month movements is quite significant and there are number of very significant movements that are difficult to explain without more detailed information. The quality of the original estimates is good, as they have been downloaded from previously mentioned sources. Neither the Statistical Office of the Republic of Serbia or the National Bank of Serbia responded to ESS request for the IIP statistics. Original Estimates - Details: Source January 1999 to December py=100 from the Statistical Office of the Republic of Serbia; January 20 to present - fixed base index available from available from the National Bank of Serbia (almost identical to statistics from Statistical Office of Republic of Serbia, but include recent revisions and more up-to-date); Length of the timeseries: January 1999 to present but only data from 2000 to present used for the SA pilot project Quality: Good Issues: Extremely volatile, no growth Seasonally Adjusted Estimates - Comments: Producing seasonally adjusted estimates for Serbia proved to be extremely challenging. Initially, as with all the other countries different scenarios were applied to the original data from January 1999 to present. However in majority of instance both methods could not produce satisfactory results, as they struggled with extreme values in Not having the underlying information that could be used to treat correctly the extreme values, a decision was made to shorten the timeseries to The timeseries from 2000 although still extremely volatile is significantly more stable then the data for The original timeseries from 2000 to present is still extremely volatile and includes number of extreme values which could be seasonally adjusted more correctly have there been more detailed information explaining the underlying causes. In particular the treatment of extreme values for February 2000, October 2000 and February 2003 could be improved with more detailed information. Overall upon closer inspection the seasonality in the original estimates can be observed, but appears to be changing over 96

97 time and becomes much more evident after January The different SA scenarios considered by ESS produced somewhat different results. The preferred scenario appears to produce acceptable results; nevertheless they should be treated with caution. Finally the graphic analysis and same period previous month comparison of the original and SA estimates provided satisfactory results, with acceptable correlation between the smpy SA and original movements. Seasonally Adjusted Estimates - Details Seasonal Adjustment Completed YES Length of the seasonally adjusted January 2004 to present, timeseries Preferred SA Method TRAMO/SEATS Preferred ARIMA Model , Preferred Number of Regressors 6 Country Calendars YES Availability of detailed information NO Quality of SA estimates Average, should be used with caution; Suitable for publication on the Internet YES with caution Model passes all diagnostic test YES Ljung-Box on residual Acceptable Box-Pierce on residual Acceptable Normality Acceptable Percentage of outliers 3.45 % Demetra Report for final seasonally adjusted and trend estimates 97