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Title: UNECE%20Workshop%20on%20Short-Term%20Economic%20Statistics%20(STS)%20and%20Seasonal%20Adjustment

UNECE Workshop on Short-Term Economic
Statistics (STS) and Seasonal Adjustment
  • Astana, 14 17 March 2011

Workshop on Short-Term Statistics (STS) and
Seasonal Adjustment Workshop Purpose and Scope
  • Astana, 14 17 March 2011
  • Petteri Baer, Marketing Manager, Statistics

Capacity Building Program on Challenges in STS
  • The UNECE organizes with the financial support of
    the World Bank
  • For the Central Asian and other CIS Countries
  • The program consists of training workshops and
    study visits
  • Builds on the international recommendations
  • Training and exercises will be provided
  • Discusses problems and possible solutions
  • Exchange of experiences

14 - 17 March 2011
Petteri Baer
Workshop I STS and Seasonal Adjustment
  • 14-17 March, 2011 (Astana, Kazakhstan)
  • Topics covered
  • Why are short-term statistics important?
  • Use of multiple data sources
  • Methodology of compilation of STS
  • Dissemination
  • Seasonal adjustment in practice
  • Exercises on seasonal adjustment
  • Participants in this Workshop to participate in
    the next workshop on seasonal adjustment

14 - 17 March 2011
Petteri Baer
Workshop II Challenges in Consumer Price
  • 2011 (Istanbul, Turkey)
  • Topics covered
  • Calculation of elementary and higher-level
  • Coverage of goods and services
  • Treatment of missing prices and their
  • Seasonal items
  • Adjustment for quality changes
  • Will be based on the international Consumer Price
    Indices Manual

14 - 17 March 2011
Petteri Baer
Workshop III Implementation of the 2008 SNA
  • 2011 (Kiev, Ukraine)
  • Topics covered
  • Implementation issues of the 2008 SNA
  • Discussion of the problematic areas and priority
  • Support countries to establish implementation
  • Development needs of both national accounts and
    the related primary statistics
  • Building of networks of experts in SNA and NA

14 - 17 March 2011
Petteri Baer
Workshop IV Training in Seasonal Adjustment
  • 2012 (Istanbul, Turkey)
  • Topics covered
  • Tackle the methodological and practical issues of
    seasonal adjustment
  • Seasonal adjustment of problematic time series
  • Analyses of the quality of seasonal adjustment
  • Discuss countries experiences and problems

14 - 17 March 2011
Petteri Baer
Agenda for Monday, 14 March 2011
  • Session 1
  • Workshop Introduction
  • Welcome
  • Workshop Purpose and Scope
  • Challenges and Problems in the area of STS
  • Session 2
  • The Aim of STS User view and Current
  • General recommendations on STS
  • Why STS matters? User view on STS

14 - 17 March 2011
Petteri Baer
Agenda for Tuesday, 15 March 2011
  • Session 3
  • STS Production Methodology
  • STS compilation with multiple data sources
  • Case studies from countries
  • Session 4
  • An Introduction to Seasonality and Seasonal
  • Components of time series, seasonality and
    preconditions for seasonal adjustment
  • Seasonal adjustment as a process
  • 1. Exercise Getting started

14 - 17 March 2011
Petteri Baer
Agenda for Wednesday, 16 March 2011
  • Session 5
  • Seasonal Adjustment Practice
  • Why seasonally adjust and how?
  • Issues on seasonal adjustment in the EECCA
  • Round table Current state of seasonal adjustment
  • Session 6
  • Pre-treatment of Time Series
  • Pre-treatment practice for seasonal adjustment
    including calendar adjustment
  • 2. Exercise Examples of graphical analysis,
    outlier detection and the effect of calendar

14 - 17 March 2011
Petteri Baer
Agenda for Thursday, 17 March 2011
  • Session 7
  • Performing Seasonal Adjustment
  • Model selection, seasonal adjustment and
    analyzing results
  • 3. Exercise Step-by-step seasonal adjustment
  • Session 8
  • Going forward with seasonal adjustment
  • Disseminating statistical information on economic
  • How to release seasonally adjusted data
  • Towards the next workshop closing

14 - 17 March 2011
Petteri Baer
Participants expectations
  • Gain theoretical knowledge and practical skills
  • To carry out seasonal adjustment
  • To start providing users with seasonally adjusted
  • To get training and methodological materials
  • Exchange experiences with other countries on
    seasonal adjustment
  • Hear about international recommendations on STS
    and SA
  • Participant Presentation Round!

14 - 17 March 2011
Petteri Baer
Challenges Problems of Short-Term Statistics
UNECE Workshop on Short-Term Statistics (STS)
and Seasonal Adjustment 14 17 March 2011,
Astana, Kazakhstan
  • Based on the UNECE paper on Short-Term Economic
    Statistics in the CIS and Western Balkans
  • Carsten Boldsen Hansen
  • Economic Statistics Section, UNECE

  1. Introduction
  2. Availability of STS
  3. Publication policy
  4. Data collection and compilation of time series
  5. Seasonal adjustment
  6. Conclusions

  • A survey on seasonal adjustment in 2008
  • Challenges with STS were analyzed in 2007 and
    2009 via web sites of NSOs on
  • Consumer price index
  • Producer price index
  • Producer price index for services
  • Industrial production index
  • Retail trade turnover
  • Turnover of services
  • Volume of services production
  • Wages and salaries

  • Countries included in the assessment
  • Albania
  • Armenia
  • Azerbaijan
  • Belarus
  • Bosnia and Herzegovina
  • Georgia
  • Kazakhstan
  • Kyrgyzstan
  • Republic of Moldova
  • Montenegro
  • Russian Federation
  • Serbia
  • Tajikistan
  • The former Yugoslav Republic of Macedonia
  • Turkmenistan
  • Uzbekistan
  • Ukraine

  • What constitutes international comparability in
  • Coverage
  • Classifications
  • Methodological and computational practices
  • Provision of fixed based and/or discrete time
  • Provision of long coherent time series
  • Provision of seasonal adjusted series
  • Dissemination of documentation

Availability of Time Series
Availability of time series with more than six
Availability of STS on Services
Availability of STS on Services
  • Share of services (incl. trade) in GDP
  • 52 1996 -gt 57 2008 (in the EECCA countries)
  • Lack of data for services
  • Problems for estimating GDP
  • 8/17 countries publish wages and salaries
  • Output indicators rarely produced
  • 7 countries publish turnover or volume of
  • Indicators do not cover the whole service sector
  • Indicators limited to transport, hotels and

Availability of short-term indicators for
services (2009)
Publication Policy Issues
  • Almost all countries publish advance release
  • A huge step forward in just a few years
  • Most countries archive releases to websites
  • Time series not easily accessible
  • Few countries have a published revision policy
  • Metadata have been improved
  • Timeliness of releases is often very good

The average timeliness of STS indicators
Publication of Metadata
  • 15 of 17 surveyed countries provide some
    methodological information in English
  • Countries subscribing to IMFs SDDS or GDDS have
    more comprehensive set of metadata
  • For statistics not included in SDDS/GDDS
  • Very little data for retail trade and services
  • Many details regarding production methods not
    available in English

Publication Methodology
  • Revisions are a necessary feature of STS
  • Data are rarely published in time series format
  • Instead data for a few months is published
  • Seasonally adjusted data can only be published as
    long time series
  • Only half of the countries publish indices with a
    fixed reference period
  • Change from previous period should only be
    calculated from seasonally adjusted data!

Methodology and Comparability
Production of STS according to international
standards (2009)
Data Collection Methodology
  • Many have cut-off samples or totals
  • Over sampling for some countries and indicators?
  • Some use registers to reduce sample sizes and
    increase efficiency
  • Register data requires substantial IT resources
    and implementation of new production methods
  • NSOs may have difficulties in accessing
  • May provide a solution for developing new

Compilation Methodology
  • Good knowledge of international standards exists
  • Significant methodological differences exist
  • price indices, retail trade turnover, wages and
  • Some incoherences in definitions
  • Definitions of turnover, wages and salaries
  • Treatment of VAT, subsidies and delivery costs
  • Need to standardize definitions also in the EU
  • Almost all countries use internationally
    comparable classifications for economic
    activities (ISIC/NACE)

Time Series Methodology
  • Production of cumulative data
  • Suitable for national use only
  • Summarizes development during the current year
  • When data are available for April -gt information
    is provided from January to April
  • Length of the reference period changes with each
    release(Jan-Feb gt Jan-Mar gt Jan-Apr)
  • Cumulative data are usually only additional
    information, not the only type of data
  • A huge step forward done by a majority of

Comparison of Series Cumulative Data
Industrial Production and Production of
Electricity in Belarus
Not easy to say which industry is doing better
only the change from previous year visible
Comparison of Series Monthly Data
Industrial Production and Production of
Electricity in Belarus
Seasonality interferes in comparing monthly data
seasonal adjustment needed
Time Series Methodology
  • Problems with cumulative data
  • International comparison and analysis not
  • Slow identification of turning points
  • Change from the previous period in seasonally
    adjusted data provides faster indications of
    turning points
  • User cannot derive a correct monthly time series
  • Revisions to the earlier periods cannot be
    matched to the correct periods of time
  • Time series from cumulative data have incorrect

From Cumulative to Monthly
Cumulative Industrial Production Data (estimates
of monthly values)
Time Series Methodology
  • Fixed base indices and/or absolute values for
    discrete periods are recommended
  • Time series to be linked or calculated back when
    base year is changed
  • Not to shorten the series or to leave breaks
    (the series should not start from its b.y.)
  • Previous periods need to be revised to come up
    with reliable time series
  • Currently 10 countries publish time series of
    more than 24 observations

Where is the Economy Going?
Frequent changes of base year without links or
Seasonal Adjustment
  • SA data calculated by 11/17 countries
  • Need for training, materials/guidelines and
    support on methodological and practical issues
  • Expansion of number and length of seasonally
    adjusted series
  • More metadata on SA needed for the users
  • Development of release practices of SA
  • Standardization of compilation and release
    practices would enhance quality of SA

Conclusions of the Assessment
  • Need for longer time series
  • Historical time series to be build and maintained
  • Backcalculation or linking in base year changes
  • Improve international comparability
  • Seasonally adjusted data would enable comparison
  • More comparable information on the service sector
  • Review data collection techniques
  • Introduce sampling (and allow revisions)
  • Use administrative sources
  • Publication policy
  • New release practices (SA, time series,
  • More detailed metadata

General Recommendations on STS
UNECE Workshop on Short-Term Statistics (STS)
and Seasonal Adjustment 14 17 March 2011,
Astana, Kazakhstan
  • Carsten Boldsen Hansen
  • Economic Statistics Section, UNECE

  • General guidelines and quality
  • Sources for methodology guidelines
  • Response burden
  • STS vs. SBS
  • Time series
  • Release Practices
  • Metadata
  • User consultation

  • The use by statistical agencies in each country
    of international concepts, classifications and
    methods promotes the consistency and efficiency
    of statistical systems at all official levels.
  • The ninth principle of The Fundamental
    Principles of Official Statistics in the Region
    of the Economic Commission for Europe, UNECE

General Guidelines
  • The Fundamental Principles of Official Statistics
  • Quality of Statistics
  • Data Quality Assessment Framework
  • ESS quality framework (EC) http//epp.eurostat.ec.
  • OECD quality framework (OECD)http//www.oecd.org/
  • Handbook of Statistical Organization, The
    Operation and Organization of a Statistical
    Agency, 2003
  • http//unstats.un.org/unsd/dnss/hb/default.aspx

The Fundamental Principles
  1. indispensable for a democratic society
  2. statistical agencies decide methods and
  3. present data according to scientific standards
  4. comment on erroneous interpretation
  5. statistical agencies choose the data sources with
    regard to quality, timeliness, costs and burden

The Fundamental Principles
  1. strictly confidentiality of individual data and
    use exclusively for statistical purposes
  2. statistical laws, regulations and measures to be
    made public
  3. coordination among statistical agencies within
  4. use of international concepts, classifications
    and methods
  5. bilateral and multilateral cooperation

Respondent Burden
  • Minimizing respondent burden should be an
    important objective vs. cut-off sampling
  • Coordination of data collections would help
    reducing response burden and to divide it more
    evenly among respondents
  • Existing sources of information should be used to
    the largest extent possible
  • Administrative registers
  • Commercial datasets
  • Data collected by other organizations

  • Degree to which data is logically connected and
    mutually consistent
  • Coherence within a data set
  • Coherence across data sets
  • common concepts, definitions, valuation
    principles, classifications and co-operation
  • Coherence over time
  • Coherence across countries
  • Extent to which the recommendations have been
  • Link to national accounts important

STS vs Structural Statistics (SBS)
  • STS measures economic developments
  • SBS a snapshot describing structure detail
  • STS and SBS have different data sources,
    definitions, statistical methods, timing and
    coverage (fiscal/calendar)
  • Treatment of changes in the population
  • SBS the population in the reference year as it
  • STS makes different time periods comparable (by
    correcting for mergers and splits etc)
  • Further improvement of coherence needed!

List of Methodology Guidelines
  • Methodology of Short-Term Business Statistics
    (EC), 2006 http//ec.europa.eu/eurostat/ramon/stat
  • International Recommendations for the Index of
    Industrial Production (UN), 2010
  • Use of Administrative Sources for Business
    Statistics Purposes (EC), 1999 http//ec.europa.e
  • International Recommendations for Distributive
    Trade Statistics (UN), 2009http//ec.europa.eu/eu
  • Methodological guide for Producer Price Indices
    for Services, (EC) 2005 http//ec.europa.eu/euros
  • Evolution of Service Statistics, proceedings of a
    seminar, (EC) 2002 http//ec.europa.eu/eurostat/r
  • Consumer Price Index Manual, Theory and Practice,
    2004 (ILO) http//www.ilo.org/public/english/bure
  • Practical Guide to Producing Consumer Price
    Indices, 2009 (UNECE/ILO)http//www.unece.org/sta
  • Producer Price Index Manual, Theory and Practice,
    2004 (IMF) http//www.imf.org/external/np/sta/teg
  • Export and Import Price Manual, 2008

Time Series Recommendations
  • Fixed base indices and/or absolute values for
    discrete periods to be provided
  • New series should be linked to the old series to
    produce continuous series
  • Cumulative statistics should be published only as
    additional information
  • If seasonality influences the indicator,
    seasonally adjusted and trend series to be
  • Reference period should be a year and be updated
    when the weights are updated

Importance of Long Time Series (1)
  • Long and consistent time series important for
  • International comparison
  • Analysis
  • Appraisal of business cycles
  • Current practices of countries vary significantly
  • Currently no international standards on
  • Length of time series
  • Methods for backcasting
  • Implementing changes of classifications

Importance of Long Time Series (2)
  • STS regulation (EC) requires time series from
    2000, Eurostat recommends much longer series
  • Methodology of Short-Term Business Statistics
  • To carry out statistical analysis such as
    seasonal adjustment it is generally considered
    necessary to have observations for a minimum of 5
  • for example, in the search for turning points
    it is important to be able to have data available
    for several complete cycles.

  • Data should be released as soon as possible
  • Trade off between timeliness and quality
  • Data to be released according to a set timetable
  • Confidentiality to be secured
  • Data made available to all users at the same time
  • Data to be revised as new information is
  • Data to be accompanied by explanations
  • Contact details of relevant statisticians to be

Source Index of Industrial Production (UN)
Reference Period
  • Some guidelines by IMF and Eurostat
  • Prices, output and sales
  • Monthly
  • (GDP), labour variables at least
  • Quarterly (un/employment monthly)

Timeliness Guidelines
  • Some guidelines by IMF and Eurostat

Metadata and Dissemination Guides
  • UN
  • The Common Metadata Framework
  • Making Data Meaningful 1 and 2
  • Guidelines for Statistical Metadata on the
  • Terminology on Statistical Metadata
  • OECD
  • Data and Metadata Reporting and Presentation
  • Glossary of Statistical Terms
  • Publishing Standards for Datasets and Data Tables
  • IMF
  • Guide to the Data Dissemination Standards

Metadata Management
  • data about data
  • Identify users and provide only valuable metadata
  • Ensure metadata is easily available
  • Reuse metadata for integration and efficiency
  • Preserve history (old versions) of metadata
  • Document variations from standards
  • Make metadata work an integral part of production
  • Create a coherent system for metadata
  • Ensure that the metadata for users reflects the
    real production process

Contents of Metadata
  • Purpose and main uses of the statistics
  • Definitions of the underlying economic concepts
  • Reference to the respective legislation
  • Data coverage, periodicity and timeliness
  • Self assessment of data quality
  • revision history and accuracy of concepts
  • availability, comparability and coherence
  • limitations in the use of statistics
  • Descriptions of the methodologies used
  • Index formula, weighting and frequency of
  • Frequency of re-basing and linking methods
  • Treatment of changes in commodities or quality

Source Index of industrial production (UN)
some NSOs recommendations
User Consultation
  • Create mechanisms to obtain users views on
    regular basis in order to
  • identify priority areas for improvement
  • ensure responding to user needs
  • provide users with advice on the strengths and
    weaknesses of your statistics
  • Important with key indicators (CPI, IPI, GDP...)
  • Engaging users should be an integral part of the
    work in the NSO
  • Mutual understanding and exchange of information
    (transparency) builds trust
  • Decisions will be made independently by the NSO

Source Practical Guide to Producing CPI (UN)
Why are Short-term economic statistics
important? User views on Short-term economic
  • UNECE Training Workshop Short-term statistics
    and seasonal adjustment Astana, 14 17 March
  • Petteri Baer, Marketing Manager, Statistics

Everybody acknowledges the importance of National
  • GDP- Yes, of course
  • Sure, we need something to measure the economic
    growth of the society
  • Sure, we need international comparability at the
    level of different economies
  • Sure, we need a denominator for the shares of
    different industries in the economy

14 - 17 March 2011
Petteri Baer
What about short-term statistics on economic
  • It may be another story
  • Why?
  • No real supply provided by official statistics
  • Too many disappointments, when requesting good
    AND timely statistics
  • Substitutive indicators, guesstimates in wide use
  • And if there IS some supply
  • Often not much information provided to potential
  • Misinterpretations about the consistency of the
    statistical information due to necessary revisions

Petteri Baer
More and more statistical publication takes place
on the internet
  • This is a very positive development
  • Availability and accessibility of official
    statistics has grown substantially
  • In the beginning of the year 2008
  • gt500 Million internet hosts in the world!
  • This also increases pressure on timeliness

But note There are traps on the way!
  • Just putting your information on your web site
    does not automatically mean it is utilized
  • Even though your web information is utilized, it
    does not mean that your most important users make
    use of it

14 - 17 March 2011
Traps on the way, continued
  • You may cover only a tinyshare of your
    potential users- but not recognize it!

Petteri Baer
Traps on the way, continued
  • Counting the popularity of your web site by
    hits may deceive you because
  • a substantial part of the fabulous growth comes
    from search engines checking if you have any new

14 - 17 March 2011
Very often you do not really know, who your users
are, when you provide services on the internet
Petteri Baer
A Problem- as posed in a Pretoria Expert Group
Seminar in 2003
  • Statisticians like to talk to one another in
    mysterious ways and with a time clock which runs
    10 times slower than everyone else's
  • ? We have to communicate better!
  • ? We have to imagine ourselves into the position
    of the potential users of our information!

14 - 17 March 2011
Especially concerning Short-term economic
  • Timeliness is an issue of huge importance
  • Trade-off between timeliness and accuracy -
  • Globalization
  • Modern communication tools

14 - 17 March 2011
The voice of a policy maker
  • Rob Wright, Deputy Minister of the Department of
    Finance of Canada, opening in May 2009 the
  • International Seminar on Timeliness, Methodology
    and Comparability of Rapid Estimates of Economic
    Trends in Ottawa, organized by UNSD and
    Statistics Canada

Petteri Baer
Rob Wright The role of Data in Public Policy
  • Process of policy development is lengthy
  • Research and analysis required to understand when
    and where policy change and reform are needed
  • Designing policy
  • Developing consensus (political, public)
  • We rely on high quality and timely data
    throughout the process

Petteri Baer
Rob Wright How can statisticians improve public
  • Identify and close data gaps
  • Priority to long term issues and trends
  • But there is a need to be able to adapt to
    current developments, if possible
  • Identify turning points
  • Consult users
  • Government, private sector, researchers

Petteri Baer
The importance of turning points in the assessing
  • In assessing economic development
  • In designing financial and monetary policy
  • In making investment decisions

14 - 17 March 2011
The voice of a businessman and banker
  • Don Drummond, Chief Economist of Toronto Dominion
    Bank, Canada, opening the International Seminar,
    mentioned above
  • "I would rather have no data than wrong data."

Petteri Baer
Statistics Canada performed a stakeholder need
assessment in 2008 (1)
  • with the central users of Short-term economic
  • A great deal of trust for Canadian Statistics was
    expressed in the services of Statistics Canada on
  • At the same time, the following improvements were
  • Better timeliness
  • Additional questions to be included in the Labour
    Force Survey
  • Better metadata and
  • Improvements on the web site and the search engine

Petteri Baer
Statistics Canada performed a stakeholder need
assessment in 2008 (2)
  • Other issues that came up in this consultation
    with the main stakeholders
  • A worry that a NSO should not rush into
    short-term data collection without the usual
    testing of the instrument
  • Short term ad hoc measures will not be useful
    unless there is time series continuity
  • The agency should continue its longer-term
    investments in quality improve-ments and new data

Petteri Baer
  • Short-term economic statistics ARE important and
    their importance is growing
  • Not only as a service item
  • But also for building up the image and the
    reputation of the statistical agency as an
    important, accurate and timely information
  • Identifying turning points in the economic cycle
    is one of the most important functions
  • Availability and comparability of long term time
    series and seasonal adjustment of fixed term
    indices are of top importance
  • Keep your users well informed about your services
  • Understand your audiences specific needs by
    better communication

Petteri Baer
STS Compilation with Multiple Data Sources
UNECE Workshop on Short-Term Statistics (STS)
and Seasonal Adjustment 14 17 March 2011,
Astana, Kazakhstan
  • Anu PeltolaEconomic Statistics Section, UNECE

  • Data collection
  • Sampling
  • Administrative data
  • Combining multiple data sources
  • Compilation of results
  • Data editing
  • Non-response and weighting
  • Treatment of non-comparable changes
  • Publication
  • Improvement

Theoretical Concept A Key to Good Quality
  • Define the purpose of an indicator
  • Links to the real world
  • What should it describe?
  • Who are the users/uses (internal/external)?
  • Possible data sources
  • Links to other statistics
  • Differences in concepts, scope, methods
  • Goal variables national accounts/SBS
  • Regular benchmarking
  • Follow-up of differences

By Deming
Production Process
  • Bring the collected data to the level of the
    intended statistical output!

Data CollectionStatistical Units
  • Corner stones of business statistics
  • Legal unit -gt enterprise (services) -gt enterprise
  • Establishment (for industry/construction)
  • Business registers are fundamentally important
  • Bridge between administrative and statistical
  • The economic activity class (ISIC/NACE)
  • Improve its comprehensiveness use as a frame
  • Examine opportunities to use administrative data
  • Interactive update with information from STS

UN International recommendations for the Index
of Industrial Production EC STS
Metholodological manual
System of Statistics
Source Statistics Finland, Strategy for economic
Data CollectionQuestionnaire Design
  • Give clear instructions
  • Explain the concepts to the respondents
  • Revisions to earlier months
  • Aim to pre-fill the questionnaire with data given
  • Leave space for reporting revisions
  • Always test changes to questionnaires
  • Inform the respondents of the use of data
  • Develop useful feedback for respondents
  • your company compared to others in the same

Data CollectionSampling in Practice
  • Many surveys are for units above a size threshold
  • Burdensome and problems with the coverage of
    small units
  • Based on business register and periodically
  • In drawing a sample, special attention to be paid
  • Level of details to be published
  • Resources available
  • Accuracy and timeliness required
  • Response burden
  • Simple/stratified sampling by activity and size

Total population of unitsin the Business Register
Stratification by economic activity
Large units
Medium units
Small units
Covered on a complete enumeration basis
Covered by sampling
Covered mainly byadministrative sources
or administrative sources
  • gt Business Register to be kept up-to-date with
    new units

Data CollectionAdministrative Data Sources
  • Administrative registers or datasets can be used
  • Single source in their own right
  • Frame for sampling via the Business Register
  • Complementary source
  • Validation
  • Data source for small enterprises
  • For STS limited administrative sources available
  • VAT (value added tax)
  • Social security data (employment and labor cost)
  • Building permits, etc.

Data CollectionPros and Cons of Admin Data?
  • Reduction of response burden
  • Reduction of costs, data collection and manual
  • Total populations - detailed classifications/reg
    ional indicators
  • Better quality and coverage (of smallest units)
  • Data content, units, concepts and definitions may
  • Dependence on few large data suppliers
  • Timeliness - may require use of estimation
  • Access and confidentiality
  • Non-observed economy unlikely to be included
  • Requires good IT capacity by the supplier and the

Data CollectionAdministrative Data and Quality
  • National ID-system for enterprises
  • New production methods
  • to correct for negative values and different
  • slow accumulation gt estimation of missing data
  • The most important units to direct collection
  • Active co-operation with large enterprises
  • Development of questionnaires
  • Simplification part of information from
  • Efficiency electronic data collection

Data CollectionLegislative Issues
  • Compulsory to use existing data (if suitable) in
    statistics production
  • Guaranteed access to administrative sources
  • State government and social security institutions
    obliged to deliver their data to the NSO
  • Free of charge or compensation of direct costs
  • Co-operation in making changes in data collection
  • To ensure data confidentiality
  • Individual data collected for statistics should
    not be handed over to any use other than
    statistics or research!

CompilationCentral Role of VAT Data
Source Statistics Finland
CompilationLinking Admin and Survey Data
1. release
2. release
  • Business Register
  • e.g. 290 000 units
  • Unit IDs
  • Activity code
  • Location
  • Mergers
  • LKAU (regional)

small medium enterprises
  • VAT
  • e.g. 250 000 units
  • Turnover
  • Estimates for outputand missing data

optimal sampling
updates to BRactivity of units
CompilationData Control and Editing
  • Studying data to identify errors
  • Detect errors that have a significant influence
  • Check whether values are within given ranges
  • Check whether values for related variables are
  • Compare to past responses (previous months and a
    year ago)
  • Give top priority to outliers and errors that
    have the largest impact on the results
  • Outlier values require careful treatment
  • May be correct but caused by unusual

Source Methodology of Short-Term Business
Statistics, EC
CompilationTreating Non-Response
  • Controlling response burden
  • Better planning of data collection process
  • Offering various channels for respondents
  • Reducing the effect of non-response
  • Alternative source, e.g. administrative data
  • Imputation based on historical data
  • Mean value imputation, donor/nearist neighbour,
    regression of variables

CompilationComparing Unit Level Data
CompilationImpact on the Results
CompilationNon-Comparable Changes (NCCs)
  • Structural changes in the population
  • New units are set up and others stop existing
  • Units may be taken over, merged or split up
  • Units may expand, contract or change their
  • Reasons for large changes
  • Errors
  • Actual changes that are comparable
  • Actual changes that are non-comparable
  • UN Guide on the Impact of Globalization on
    National Accounts gt helps with STS as well

CompilationExample of NCCs
Unit A Turnover 100 million
Exchange of goods 50 million
Unit B Turnover 75 million
Turnover drops by one third due to a merger! No
change in the level of activity!
CompilationAlternative Treatments of NCCs
  • All changes are recorded as they are (actual)
  • Contaminated with apparent, non-comparable
  • Difficult to obtain a picture of economic reality
  • Simplicity
  • Panel method
  • Only same units in both periods are included
  • Start-ups and closures would be cancelled out
  • Seriously biased results in highly dynamic
  • Simplicity

CompilationAlternative Treatments of NCCs
  • Overlapping method
  • Actual comparable changes are not adjusted
  • Other changes are made comparable by
  • a. Collecting comparable information (largest
  • b. Replacing non-comparable figure by an estimate
  • c. Taken the unit out of calculation (no effect
    to results)
  • Requires more work
  • Results reflect actual changes in economic

CompilationConfrontation with Other Sources
  • Regular confrontation may reveal discrepancies
  • Aim at coherence value price x output
  • First at the aggregated level and where necessary
    at lower levels (largest units)
  • Knowledge of differences between statistics helps
    communication with users
  • Quality reviews of indicators to be undertaken

New Requirements for STS?
  • Globalization
  • Internationally comparable data needed
  • Treatment of more complex business activities
  • Increasing amount of services
  • Output and price measures, industrial services
  • Detection of turning points
  • Longer time series and seasonal adjustment
  • Coherence
  • Compare to National Accounts and between
    price/volume/value indicators

Components of Time Series, Seasonality and
Pre-conditions for Seasonal Adjustment
UNECE Workshop on Short-Term Statistics (STS)
and Seasonal Adjustment 14 17 March 2011,
Astana, Kazakhstan
  • Anu Peltola
  • Economic Statistics Section, UNECE

  • Basic Concepts
  • Components of Time Series
  • Seasonality
  • Pre-conditions for Seasonal Adjustment

Basic Concepts
  • Index comes from Latin and means a pointer, sign,
    indicator, list or register
  • A ratio that measures change
  • As per cent of a base value (base always 100)
  • Each observation is compared to the base value
  • Time series are a collection of observations,
    measured at equally spaced intervals
  • Stock series at a point in time (discrete)
  • Flow series period in time (continuous)

Components of Time Series
  • Seasonal adjustment is based on the idea that
    time series can be decomposed
  • The components are
  • Seasonal
  • Irregular
  • Trend

Relation of Components
Components of the Industrial Production Index of
Index 2005100
Seasonal Component
  • Depicts systematic, calendar-related movements
  • has a similar pattern from year to year
  • refers to the periodic fluctuations within a year
    that re-occur in approximately the same way
  • Is removed in seasonal adjustment

Irregular Component
  • Depicts unsystematic, short term fluctuations
  • The remaining component after the seasonal and
    trend components have been removed
  • Certain specific outliers, such as those caused
    by strikes, also belong to this component
  • Sometimes called the residual component
  • May or may not be random with random effects
    (white noise) or artifacts of non-sampling error
    (not necessarily random)

Trend Component
  • Depicts the long-term movement in a series
  • A trend series is derived by removing the
    irregular influences from the seasonally adjusted
  • A reflection of the underlying development
  • Typically due to influences such as population
    growth, technological development, inflation and
    general economic development
  • Sometimes referred to as the trend-cycle

IPI KazakhstanAn Example of the Components of
Time Series
Index 2005100
Causes of Seasonality
  • seasons e.g. holidays and consumption habits,
    which are related to the rhythm of the year
  • Warmth in summer and cold in winter BUT not
    extreme weather conditions (irregular component)
  • Seasonality reflects traditional behavior
    associated with
  • The calendar
  • Christmas and New Year
  • Social habits (the holiday season),
  • Business (quarterly provisional tax payments) and
  • Administrative procedures (tax returns)

Industrial production in Moldova, original series
Index 2005100
Seasonal Effect
  • Intra-year fluctuations in the series that
  • A seasonal effect is reasonably stable with
    respect to timing, direction and magnitude
  • The seasonal component of a time series is
    comprised of three main types of systematic
    calendar-related influences
  • Seasonal influences
  • Trading day influences
  • Moving holiday influences

Trading Day Effect
  • The impact on the series, of the number and
    type of days in a particular month
  • Different days may have a different weight
  • A calendar month comprises four weeks (28 days)
    plus extra one, two or three days
  • Rarely an issue in quarterly data, since quarters
    have 90, 91 or 92 days

Trading Days
Source Analysis of Daily Sales Data during the
Financial Panic of 2008, John B. Taylor (Target
Corporations sales)
Moving Holidays
  • The impact on the series of holidays whose
    exact timing shifts from year to year
  • Examples of moving holidays
  • Easter
  • Chinese New Year - where the exact date is
    determined by the cycles of the moon
  • Ramadan

Moving Holidays
Impact of moving holidays to the number of
working days
Ascension day
Christmas moves between weekdays and weekend
Working Days and Seasonality
Example of average working days in 2009 - 2011
Sudden Changes
  • Outliers
  • Extreme values with identifiable causes (strikes
    or extreme weather conditions)
  • Part of irregular component
  • Trend breaks (level shifts)
  • The trend component suddenly increases or
    decreases in value
  • Often caused by changes in definitions (tax rate,
  • Seasonal breaks
  • The seasonal pattern changes, e.g. due to a
    structural change caused by a crisis or
    administrative issues such as timing of invoicing

Pre-conditions for Seasonal Adjustment
  • Good quality of raw data
  • Strange values to be checked (zeros or outliers)
  • Revision of errors with new acquired data
  • Length of time series 36/12 or 16/4
  • At least 36 observations for monthly series and
    16 observations for quarterly series needed
  • Consistent time series
  • To provide data according to a base year
  • Use of comparable definitions and classifications
  • Remove non-comparable changes
  • Solid structure
  • Presence of seasonality, moderate volatility
  • No major breaks in seasonal behaviour

Seasonal Adjustment Process with Demetra
  • Anu Peltola
  • Economic Statistics Section, UNECE

  • Seasonal adjustment process
  • Prepare and check
  • Define and adjust
  • Analyse and refine
  • Document and publish

Prepare and check
Check the original series
Prepare a source file
Open Demetra
Import data
Open Demetra
  • The program can be downloaded at
  • Can be used free of charge

Prepare a source file
  • Many types of files are suitable
  • Excel file either horizontal or vertical

Import data
  • You can copy and paste
  • But you will have to do this every time
  • Dynamic updates possible
  • Next time you do not have to import again

Check the original series
  • The quality of seasonal adjustment depends on the
    quality of the raw data.
  • accuracy,
  • length of time series (36/16),
  • quality of production methods and
  • consistency of time series
  • Demetra provides many visual tools, i.e. to test
    the presence of seasonality

Check the original series
  • Is seasonality present in the original series?

Define and adjust
Seasonally adjust
Prepare calendars
Select an approach
Select regressors
Select an approach
  • Choose to process only one time series or
    multiple series
  • Select the approach (X12 or T/S)
  • Working instructions for beginning seasonal
    adjustment with Demetra written for TRAMO/SEATS
  • More instructions for the use of X-12-ARIMA in
    the Demetra Manuals

Prepare calendars
  • Demetra includes some predefined holidays, but
    not national holiday calendars of all countries
  • Collect a time series of your national holidays
  • Prepare the calendar by
  • Selecting from the pre-specified holidays
  • Defining exact dates
  • Setting a validity period

Select regressors
  • Define the specification
  • Start the analysis with the default
  • choose first either four (RSA4) or five (RSA5)

RSA0 level,airline model
RSA1 log/level,outliers detection, airline model
RSA2 log/level, working days, Easter, outlier detection, airline model
RSA3 log/level, outlier detection, automatic model identification
RSA4 log/level, working days, Easter, outlier detection, automatic model identification
RSA5 log/level, trading days, Easter, outlier detection, automatic model identification
Seasonally adjust
  • To launch adjustment
  • double click on the series or
  • select from the main menu
  • Seasonal adjustment/ Single Analysis/ New
  • Seasonal adjustment/ Multiprocessing/ New
  • SAProcessing-xx/ Run
  • Save to the workspace

Seasonally adjust
  • For second adjustment of the same data, decide
    your update strategy
  • Current adjustment fixed forecasts
  • Concurrent adjustment nothing fixed

Analyse and refine
Refine and adjust
Models applied
Visual check
Quality Diagnostics
Visual check
  • Is the seasonal component lost in the irregular?

Visual check
  • Check the S-I ratio for moving seasonality

Models applied
  • Information about pre-processing
  • the estimation time span used,
  • corrections for trading days and Easter,
  • type of applied ARIMA model,
  • the dates and types of outliers as well as
  • the distribution of residuals
  • Information about decomposition
  • the applied decomposition model,
  • statistical indicators to validate the model

Quality Diagnostics
  • Demetra offers a wide range of quality measures
  • Verbal description also
  • Basic checks annual totals
  • Residuals, i.e. the part of data not explained by
    modelling, should not include any information

Quality Diagnostics
  • Is there some residual seasonality after

Quality Diagnostics
  • Are there large revisions is the model stable?

Quality Diagnostics
  • Do the residuals follow the normal distribution?
  • Are they random?

Refine and adjust
  • Seasonal adjustment is an iterative process
  • After the first adjustment, you can try different
  • For example, from RSA 5 to RSA 4
  • In multiprocessing, you can edit a single series
    by double clicking its name in the results
  • You can maintain the previous adjustments for

Document and publish
Support users
Export data
Document choices
Prepare publication
Document choices
  • Document the first page of Main results,
    Pre-processing, Decomposition and Diagnostics
  • Archive the resulting time series for later
    revision analysis
  • Prepare metadata to be published to the users of
  • ESS metadata template
  • OECD Data and Metadata Reporting and Presentation

Export data
  • You can export to several kinds of outputs
  • Main menu SAProcessing-xx/Generate output
  • Copy TramoSeatsDoc-x/Copy/Results

Prepare publication
  • Redesign the content of data releases when you
    start with seasonally adjusted data
  • Consider how to explain revisions, and plan in
    advance how to revise these data
  • The press releases should be simple
  • Offer some more details on the web site, e.g.
    regional or industry level data

Support users
  • Seasonal adjustment aims at better service for
    the users of statistics
  • Define a clear framework / policy
  • choice of method and software
  • timing of reanalysis of parameters and models
  • treatment of outliers
  • practices with revision and dissemination

Why Seasonally Adjust and How?Approaches
X-12-ARIMA, TRAMO/SEATS, X-13 A/S and Tools
UNECE Workshop on Short-Term Statistics (STS)
and Seasonal Adjustment 14 17 March 2011,
Astana, Kazakhstan
  • Anu PeltolaEconomic Statistics Section, UNECE

  • What and why
  • Basic concepts
  • Methods
  • Software
  • Recommendations
  • Useful references

A Coyote Moment Did We Notice the Turning Point?
Economic Crises Statistics
  • Did we give any warnings?
  • A responsibility for the statistical offices? A
    new task?
  • Important to all users of statistics
  • Not only to politicians, but also to
    enterpreneurs and citizens
  • Statistical offices often have monopoly to
    analyze detailed data sets
  • We should not forecast, but draw attention to
  • Identify changes early, leading indicators,
    develop more flash estimates -gt quality vs.
  • Otherwise, a risk of marginalisation of NSOs

Economic Crises Conclusions
  • Some limits of official statistics were
    highlighted by the critics
  • lack of comparability among countries
  • need for more timely key indicators
  • need for statistical indicators in areas of
    particular importance for the financial and
    economic crisis

Source Status Report on Information Requirements
in EMU
Turning Points Trend vs. Year-on-Year
RateVolume of Construction
Why Seasonally Adjust?
  • Seasonal effects in raw data conceal the true
    underlying development
  • Easier to interpret, reveals long-term
  • To aid in comparing economic development
  • Including comparison of countries or economic
  • To aid economists in short-term forecasting
  • To allow series to be compared from one month to
    the next
  • Faster and easier detection of economic cycles

Why Original Data is Not Enough?
  • Comparison with the same period of last year does
    not remove moving holidays
  • If Easter falls in March (usually April) the
    level of activity can vary greatly for that month
  • Comparison ignores trading day effects, e.g.
    different amount of different weekdays
  • Contains the influence of the irregular component
  • Delay in identification of turning points

Seasonal Adjustment
  • Seasonal adjustment is an analysis technique
  • Estimates seasonal influences using procedures
    and filters
  • Removes systematic and calendar-related
  • Aims to eliminate seasonal and working day
  • No seasonal and working day effects in a
    perfectly seasonally adjusted series

Interpretation of Seasonally Adjusted Data
  • In a seasonally adjusted world
  • Temperature is exactly the same during both
    summer and winter
  • There are no holidays
  • People work every day of the week with the same
  • Source Bundesbank

Filter Based Methods
  • X-11, X-11-ARIMA, X-12-ARIMA (STL, SABL,
  • Based on the ratio to moving average described
    in 1931 by Fredrick R. Macaulay (US)
  • Estimate time series components (trend and
    seasonal factors) by application of a set of
    filters (moving averages) to the original series
  • Filter removes or reduces the strength of
    business and seasonal cycles and noise from the
    input data

X-11 and X-11-ARIMA
  • X-11
  • Developed by the US Census Bureau
  • Began operation in the US in 1965
  • Integrated into software such as SAS and
  • Uses filters to seasonally adjust data
  • X-11-ARIMA
  • Developed by Statistics Canada in 1980
  • ARIMA modelling reduces revisions in the
    seasonally adjusted series and the effect of the
    end-point problem
  • No user-defined regressors, not robust against

  • http//www.census.gov/srd/www/x12a/
  • Developed and maintained by the US Census Bureau
  • Based on a set of linear filters (moving
  • User may define prior adjustments
  • Fits a regARIMA model to the series in order to
    detect and adjust for outliers and other
    distorting effects
  • Diagnost
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