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

1
UNECE Workshop on Short-Term Economic
Statistics (STS) and Seasonal Adjustment

• Astana, 14 17 March 2011

2
Workshop on Short-Term Statistics (STS) and
Seasonal Adjustment Workshop Purpose and Scope

• Astana, 14 17 March 2011
• Petteri Baer, Marketing Manager, Statistics
  Finland

3
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
3
Petteri Baer
4
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
4
Petteri Baer
5
Workshop II Challenges in Consumer Price
Indices

• 2011 (Istanbul, Turkey)
• Topics covered
• Calculation of elementary and higher-level
  indices
• Coverage of goods and services
• Treatment of missing prices and their
  replacements
• Seasonal items
• Adjustment for quality changes
• Will be based on the international Consumer Price
  Indices Manual

14 - 17 March 2011
5
Petteri Baer
6
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
  setting
• Support countries to establish implementation
  plans
• Development needs of both national accounts and
  the related primary statistics
• Building of networks of experts in SNA and NA

14 - 17 March 2011
6
Petteri Baer
7
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
7
Petteri Baer
8
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
  Developments
• General recommendations on STS
• Why STS matters? User view on STS

14 - 17 March 2011
8
Petteri Baer
9
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
  Adjustment
• Components of time series, seasonality and
  preconditions for seasonal adjustment
• Seasonal adjustment as a process
• 1. Exercise Getting started

14 - 17 March 2011
9
Petteri Baer
10
Agenda for Wednesday, 16 March 2011

• Session 5
• Seasonal Adjustment Practice
• Why seasonally adjust and how?
• Issues on seasonal adjustment in the EECCA
  countries
• 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
  adjustment

14 - 17 March 2011
10
Petteri Baer
11
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
  development
• How to release seasonally adjusted data
• Towards the next workshop closing

14 - 17 March 2011
11
Petteri Baer
12
Participants expectations

• Gain theoretical knowledge and practical skills
• To carry out seasonal adjustment
• To start providing users with seasonally adjusted
  data
• 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
12
Petteri Baer
13
Challenges Problems of Short-Term Statistics
(STS)
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

14
Agenda

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

15
Introduction

• 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

16
Introduction

• 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

17
Introduction

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

18
Availability of Time Series
Availability of time series with more than six
observations
19
Availability of STS on Services
20
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
  services
• Indicators do not cover the whole service sector
• Indicators limited to transport, hotels and
  restaurants

21
Availability
Availability of short-term indicators for
services (2009)
22
Publication Policy Issues

• Almost all countries publish advance release
  calendars
• 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

23
Timeliness
The average timeliness of STS indicators
24
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

25
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!

26
Methodology and Comparability
Production of STS according to international
standards (2009)
27
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
  registers?
• May provide a solution for developing new
  statistics?

28
Compilation Methodology

• Good knowledge of international standards exists
• Significant methodological differences exist
• price indices, retail trade turnover, wages and
  salaries
• 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)

29
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
  countries

30
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
31
Comparison of Series Monthly Data
Industrial Production and Production of
Electricity in Belarus
Seasonality interferes in comparing monthly data
seasonal adjustment needed
32
Time Series Methodology

• Problems with cumulative data
• International comparison and analysis not
  possible
• 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
  seasonality

33
From Cumulative to Monthly
Cumulative Industrial Production Data (estimates
of monthly values)
34
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

35
Where is the Economy Going?
Frequent changes of base year without links or
backcalculation
36
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

37
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,
  revisions)
• More detailed metadata

38
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

39
Overview

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

40

• 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

41
General Guidelines

• The Fundamental Principles of Official Statistics
  (UN)http//unstats.un.org/unsd/dnss/gp/fundprinci
  ples.aspx
• Quality of Statistics
• Data Quality Assessment Framework
  (IMF)http//www.imf.org/external/np/sta/dsbb/2003
  /eng/dqaf.htm
• ESS quality framework (EC) http//epp.eurostat.ec.
  europa.eu/portal/page/portal/quality/introduction
• OECD quality framework (OECD)http//www.oecd.org/
  document/43/0,3343,en_2649_33715_21571947_1_1_1_1,
  00.html
• Handbook of Statistical Organization, The
  Operation and Organization of a Statistical
  Agency, 2003
• http//unstats.un.org/unsd/dnss/hb/default.aspx

42
The Fundamental Principles

1. indispensable for a democratic society
2. statistical agencies decide methods and
   procedures
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

43
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
   countries
4. use of international concepts, classifications
   and methods
5. bilateral and multilateral cooperation

44
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

45
Coherence

• 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
  adopted
• Link to national accounts important

46
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
  is
• STS makes different time periods comparable (by
  correcting for mergers and splits etc)
• Further improvement of coherence needed!

47
List of Methodology Guidelines

• Methodology of Short-Term Business Statistics
  (EC), 2006 http//ec.europa.eu/eurostat/ramon/stat
  manuals/files/KS-BG-06-001-EN.pdf
• International Recommendations for the Index of
  Industrial Production (UN), 2010
  http//unstats.un.org/unsd/statcom/doc10/BG-Indus
  trialStats.pdf
• Use of Administrative Sources for Business
  Statistics Purposes (EC), 1999 http//ec.europa.e
  u/eurostat/ramon/statmanuals/files/CA-24-99-897-__
  -N-EN.pdf
• International Recommendations for Distributive
  Trade Statistics (UN), 2009http//ec.europa.eu/eu
  rostat/ramon/statmanuals/files/Inter_Rec_for_Distr
  ibut_Trade_Stat.pdf
• Methodological guide for Producer Price Indices
  for Services, (EC) 2005 http//ec.europa.eu/euros
  tat/ramon/statmanuals/files/KS-BG-06-003-EN.pdf
• Evolution of Service Statistics, proceedings of a
  seminar, (EC) 2002 http//ec.europa.eu/eurostat/r
  amon/statmanuals/files/KS-BG-02-001-__-N-EN.pdf
• Consumer Price Index Manual, Theory and Practice,
  2004 (ILO) http//www.ilo.org/public/english/bure
  au/stat/guides/cpi/index.htm
• Practical Guide to Producing Consumer Price
  Indices, 2009 (UNECE/ILO)http//www.unece.org/sta
  ts/publications/Practical_Guide_to_Producing_CPI.p
  df
• Producer Price Index Manual, Theory and Practice,
  2004 (IMF) http//www.imf.org/external/np/sta/teg
  ppi/index.htm
• Export and Import Price Manual, 2008
  (IMF)http//www.imf.org/external/np/sta/tegeipi/i
  ndex.htm

48
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
  published
• Reference period should be a year and be updated
  when the weights are updated

49
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

50
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
  years
• for example, in the search for turning points
  it is important to be able to have data available
  for several complete cycles.

51
Dissemination

• 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
  available
• Data to be accompanied by explanations
• Contact details of relevant statisticians to be
  given

Source Index of Industrial Production (UN)
52
Reference Period

• Some guidelines by IMF and Eurostat
• Prices, output and sales
• Monthly
• (GDP), labour variables at least
• Quarterly (un/employment monthly)

53
Timeliness Guidelines

• Some guidelines by IMF and Eurostat

54
Metadata and Dissemination Guides

• UN
• The Common Metadata Framework
• Making Data Meaningful 1 and 2
• Guidelines for Statistical Metadata on the
  Internet
• 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

55
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

56
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
  revising
• Frequency of re-basing and linking methods
• Treatment of changes in commodities or quality

Source Index of industrial production (UN)
some NSOs recommendations
57
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)
58
Why are Short-term economic statistics
important? User views on Short-term economic
statistics

• UNECE Training Workshop Short-term statistics
  and seasonal adjustment Astana, 14 17 March
  2011
• Petteri Baer, Marketing Manager, Statistics
  Finland

59
Everybody acknowledges the importance of National
Accounts

• 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
59
Petteri Baer
60
What about short-term statistics on economic
development?

• 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
  users
• Misinterpretations about the consistency of the
  statistical information due to necessary revisions

Petteri Baer
61
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

62
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
63
Traps on the way, continued

• You may cover only a tinyshare of your
  potential users- but not recognize it!

Petteri Baer
64
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
  information

14 - 17 March 2011
65
Very often you do not really know, who your users
are, when you provide services on the internet
Petteri Baer
66
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
67
Especially concerning Short-term economic
statistics

• Timeliness is an issue of huge importance
• Trade-off between timeliness and accuracy -
  always
• Globalization
• Modern communication tools

14 - 17 March 2011
68
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
69
Rob Wright The role of Data in Public Policy
Development

• 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
70
Rob Wright How can statisticians improve public
policy?

• 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
71
The importance of turning points in the assessing
development

• In assessing economic development
• In designing financial and monetary policy
  decisions
• In making investment decisions

14 - 17 March 2011
72
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
73
Statistics Canada performed a stakeholder need
assessment in 2008 (1)

• with the central users of Short-term economic
  statistics
• A great deal of trust for Canadian Statistics was
  expressed in the services of Statistics Canada on
  STS
• At the same time, the following improvements were
  required

• 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
74
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
  products

Petteri Baer
75
Conclusions

• 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
  provider
• 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
76
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

77
Overview

• 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

78
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
79
Production Process

• Bring the collected data to the level of the
  intended statistical output!

80
Data CollectionStatistical Units

• Corner stones of business statistics
• Legal unit -gt enterprise (services) -gt enterprise
  groups
• Establishment (for industry/construction)
• Business registers are fundamentally important
• Bridge between administrative and statistical
  units
• 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
81
System of Statistics
Source Statistics Finland, Strategy for economic
statistics
82
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
  earlier
• 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
  activity

83
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
  reviewed
• In drawing a sample, special attention to be paid
  to
• Level of details to be published
• Resources available
• Accuracy and timeliness required
• Response burden
• Simple/stratified sampling by activity and size

84
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

85
Data CollectionAdministrative Data Sources

• Administrative registers or datasets can be used
  as
• 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.

86
Data CollectionPros and Cons of Admin Data?

• Reduction of response burden
• Reduction of costs, data collection and manual
  work
• Total populations - detailed classifications/reg
  ional indicators
• Better quality and coverage (of smallest units)
• Data content, units, concepts and definitions may
  differ
• 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
  NSO

87
Data CollectionAdministrative Data and Quality

• National ID-system for enterprises
• New production methods
• to correct for negative values and different
  concepts
• 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
  registers
• Efficiency electronic data collection

88
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!

89
CompilationCentral Role of VAT Data
1
(
1
)
Source Statistics Finland
90
CompilationLinking Admin and Survey Data
1. release
revision
2. release

• Business Register
• e.g. 290 000 units
• Unit IDs
• Activity code
• Location
• Mergers
• LKAU (regional)

combining
small medium enterprises

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

optimal sampling
updates to BRactivity of units
91
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
  coherent
• 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
  circumstances

Source Methodology of Short-Term Business
Statistics, EC
92
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

93
CompilationComparing Unit Level Data
94
CompilationImpact on the Results
index
95
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
  activities
• 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

96
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!
97
CompilationAlternative Treatments of NCCs

• All changes are recorded as they are (actual)
• Contaminated with apparent, non-comparable
  changes
• 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
  populations
• Simplicity

98
CompilationAlternative Treatments of NCCs

• Overlapping method
• Actual comparable changes are not adjusted
• Other changes are made comparable by
• a. Collecting comparable information (largest
  units)
• 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
  activity

99
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

100
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

101
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

102
Overview

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

103
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)

104
Components of Time Series

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

105
Relation of Components
Components of the Industrial Production Index of
Kazakhstan
Index 2005100
106
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
  annually
• Is removed in seasonal adjustment

107
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)

108
Trend Component

• Depicts the long-term movement in a series
• A trend series is derived by removing the
  irregular influences from the seasonally adjusted
  series
• 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

109
IPI KazakhstanAn Example of the Components of
Time Series
Index 2005100
110
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)

111
Seasonality
Industrial production in Moldova, original series
2000-2008
Index 2005100
months
112
Seasonal Effect

• Intra-year fluctuations in the series that
  repeat
• 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

113
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

114
Trading Days
Saturday
Source Analysis of Daily Sales Data during the
Financial Panic of 2008, John B. Taylor (Target
Corporations sales)
115
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

116
Moving Holidays
Impact of moving holidays to the number of
working days
Ascension day
Christmas moves between weekdays and weekend
117
Working Days and Seasonality
Example of average working days in 2009 - 2011
118
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,
  reclassification)
• 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

119
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

120
Seasonal Adjustment Process with Demetra

• Anu Peltola
• Economic Statistics Section, UNECE

121
Overview

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

122
Prepare and check
Check the original series
Prepare a source file
Open Demetra
Import data
123
Open Demetra

• The program can be downloaded at
  http//circa.europa.eu/irc/dsis/eurosam/info/data/
  demetra.htm
• Can be used free of charge

124
Prepare a source file

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

125
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

126
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

127
Check the original series

• Is seasonality present in the original series?

128
Define and adjust
Seasonally adjust
Prepare calendars
Select an approach
Select regressors
129
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

130
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

131
Select regressors

• Define the specification
• Start the analysis with the default
  specifications
• 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
132
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

133
Seasonally adjust

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

134
Analyse and refine
Refine and adjust
Models applied
Visual check
Quality Diagnostics
135
Visual check

• Is the seasonal component lost in the irregular?

136
Visual check

• Check the S-I ratio for moving seasonality

137
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

138
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

139
Quality Diagnostics

• Is there some residual seasonality after
  adjustment?

140
Quality Diagnostics

• Are there large revisions is the model stable?

141
Quality Diagnostics

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

142
Refine and adjust

• Seasonal adjustment is an iterative process
• After the first adjustment, you can try different
  specifications
• 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
  comparison

143
Document and publish
Support users
Export data
Document choices
Prepare publication
144
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
  statistics
• ESS metadata template
• OECD Data and Metadata Reporting and Presentation
  Handbook

145
Export data

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

146
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

147
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

148
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

149
Overview

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

150
A Coyote Moment Did We Notice the Turning Point?
151
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
  statistics
• Identify changes early, leading indicators,
  develop more flash estimates -gt quality vs.
  timeliness
• Otherwise, a risk of marginalisation of NSOs

152
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
153
Turning Points Trend vs. Year-on-Year
RateVolume of Construction
154
Why Seasonally Adjust?

• Seasonal effects in raw data conceal the true
  underlying development
• Easier to interpret, reveals long-term
  development
• To aid in comparing economic development
• Including comparison of countries or economic
  activities
• 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

155
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

156
Seasonal Adjustment

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

157
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
  intensity
• Source Bundesbank

158
Filter Based Methods

• X-11, X-11-ARIMA, X-12-ARIMA (STL, SABL,
  SEASABS)
• 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

159
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
  STATISTICA
• 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
  outliers

160
X-12-ARIMA

• http//www.census.gov/srd/www/x12a/
• Developed and maintained by the US Census Bureau
• Based on a set of linear filters (moving
  averages)
• 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