unece workshop on short-term economic statistics (sts) and seasonal adjustment

March 2011 UNECE Statistical Division 1

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


Petteri Baer, Marketing Manager, Statistics Finland

March 2011 UNECE Statistical Division 314 - 17 March 2011

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

3Petteri 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

4Petteri Baer


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

5Petteri 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

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

6Petteri 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

7Petteri Baer


Agenda for Monday, 14 March 2011

Session 1

Workshop IntroductionWorkshop Introduction–Welcome–Workshop Purpose and Scope –Challenges and Problems in the area of STS

Session 2

The Aim of STS – User view and Current DevelopmentsThe Aim of STS – User view and Current Developments–General recommendations on STS –Why STS matters? User view on STS

8Petteri Baer


Agenda for Tuesday, 15 March 2011

Session 3

STS Production Methodology STS Production Methodology –STS compilation with multiple data sources –Case studies from countries

Session 4

An Introduction to Seasonality and Seasonal Adjustment An Introduction to Seasonality and Seasonal Adjustment –Components of time series, seasonality and preconditions

for seasonal adjustment –Seasonal adjustment as a process –1. Exercise:1. Exercise: Getting started

9Petteri Baer


Agenda for Wednesday, 16 March 2011

Session 5

Seasonal Adjustment PracticeSeasonal 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 SeriesPre-treatment of Time Series–Pre-treatment practice for seasonal adjustment including

calendar adjustment –2. Exercise:2. Exercise: Examples of graphical analysis, outlier detection

and the effect of calendar adjustment

10Petteri Baer


Agenda for Thursday, 17 March 2011

Session 7

Performing Seasonal Adjustment Performing Seasonal Adjustment –Model selection, seasonal adjustment and analyzing results –3. Exercise:3. Exercise: Step-by-step seasonal adjustment

Session 8

Going forward with seasonal adjustmentGoing forward with seasonal adjustment–Disseminating statistical information on economic

development –How to release seasonally adjusted data –Towards the next workshop & closing

11Petteri Baer


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!

12Petteri Baer


Challenges & Problems of Challenges & Problems of Short-Term Statistics (STS)Short-Term Statistics (STS)

Based on the UNECE paper on Short-Term Economic Statistics in the CIS and Western Balkans

Carsten Boldsen Hansen Economic Statistics Section, UNECE

UNECE Workshop on Short-Term Statistics (STS) UNECE Workshop on Short-Term Statistics (STS) and Seasonal Adjustmentand Seasonal Adjustment14 – 17 March 2011, Astana, Kazakhstan


Agenda

1. Introduction

2. Availability of STS

3. Publication policy

4. Data collection and compilation of time series

5. Seasonal adjustment

6. Conclusions


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


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


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


Availability of Time Series

01

2345

678

91011

12131415

1617

Consumer PriceIndex

IndustrialProduction Index

Producer PriceIndex

Wages andSalaries

Retail TradeTurnover

Num

ber o

f cou

ntrie

s

2007

2009

Availability of time series with more than six observations


Availability of STS on Services


Share of services (incl. trade) in GDP: • 52% 1996 -> 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

Availability of STS on Services


01234

56789

101112

1314151617

Wages andSalaries

Number ofEmployees

Turnover in Services Volume of Services Price Information forServices

Num

ber

of c

ount

ries

Availability

Availability of short-term indicators for services (2009)


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


Timeliness

The average timeliness of STS indicators


Publication of Metadata

15 of 17 surveyed countries provide some methodological information in English• Countries subscribing to IMF’s 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

0123456789

1011121314151617

fixed baseindices for some

indicator

internationallycomparableclassification

seasonallyadjustedstatistics

value added taxdata used for

statistics

Nu

mb

er

of

co

un

trie

s

Production of STS according to international standards (2009)


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?

Data Collection 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)

Compilation Methodology


Time Series Methodology

Production of cumulative data Suitable for national use only Summarizes development during the current year

• When data are available for April -> information is provided from January to April

• Length of the reference period changes with each release(Jan-Feb > Jan-Mar > 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


80

85

90

95

100

105

110

115

120

Jan

Jan

-Fe

b

Jan

-Ma

r

Jan

-Ap

r

Jan

-Ma

y

Jan

-Ju

n

Jan

-Ju

l

Jan

-Au

g

Jan

-Se

p

Jan

-Oct

Jan

-No

v

Jan

-De

c

Jan

Jan

-Fe

b

Jan

-Ma

r

Jan

-Ap

r

Jan

-Ma

y

Jan

-Ju

n

Jan

-Ju

l

Jan

-Au

g

Jan

-Se

p

Jan

-Oct

Jan

-No

v

Jan

-De

c

2008 2009cu

mu

lati

ve

ind

ex

fro

m t

he

co

rre

sp

on

din

g p

eri

od

o

f th

e p

rev

iou

s y

ea

r

industry electricity

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


40

50

60

70

80

90

100

110

120

130

Jan-

08

Feb

-08

Mar

-08

Apr

-08

May

-08

Jun-

08

Jul-0

8

Aug

-08

Sep

-08

Oct

-08

Nov

-08

Dec

-08

Jan-

09

Feb

-09

Mar

-09

Apr

-09

May

-09

Jun-

09

Jul-0

9

Aug

-09

Sep

-09

Oct

-09

Nov

-09

Dec

-09

ind

ex 2

008=

100

industry electricity

Industrial Production and Production of Electricity in Belarus

Comparison of Series – Monthly Data

Seasonality interferes in comparing monthly data – seasonal adjustment needed



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


From Cumulative to Monthly

Cumulative Industrial Production Data (estimates of monthly values)

101

102

102

103

103

104

104

Jan Jan-Feb

Jan-Mar

Jan-Apr

Jan-May

Jan-Jun

Jan-Jul

Jan-Aug

Jan-Sep

Jan-Oct

Jan-Nov

Jan-Dec

cum

ula

tive

ch

ang

e fr

om

th

e co

rres

po

nd

ing

per

iod

o

f th

e p

revi

ou

s ye

ar

Cumulative change

0

200

400

600

800

1000

1200

Jan Jan-Feb

Jan-Mar

Jan-Apr

Jan-May

Jan-Jun

Jan-Jul Jan-Aug

Jan-Sep

Jan-Oct

Jan-Nov

Jan-Dec

mo

nth

ly v

alu

e

101

102

102

103

103

104

104

cum

ula

tive

ch

ang

e fr

om

th

e co

rres

po

nd

ing

p

erio

d o

f th

e p

revi

ou

s ye

ar

Calculated from cumulative values Cumulative change

0

200

400

600

800

1000

1200

Jan Jan-Feb

Jan-Mar

Jan-Apr

Jan-May

Jan-Jun

Jan-Jul Jan-Aug

Jan-Sep

Jan-Oct

Jan-Nov

Jan-Dec

mo

nth

ly v

alu

e

101

102

102

103

103

104

104

cum

ula

tive

ch

ang

e fr

om

th

e co

rres

po

nd

ing

p

erio

d o

f th

e p

revi

ou

s ye

ar

Calculated from cumulative values Real monthly value with revisions Cumulative change



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 backcalculation

60

70

80

90

100

110

120

130

2006

m01

2006

m04

2006

m07

2006

m10

2007

m01

2007

m04

2007

m07

2007

m10

2008

m01

2008

m04

2008

m07

2008

m10

2009

m01

2009

m04

2009

m07

2009

m10

2010

m01

2010

m04

2010

m07

2010

m10

pre

vio

us

year

= 1

00

2005=100

2006=100

2007=100

2008=100

2009=100


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

Seasonal Adjustment


Conclusions of the Assessment

1. Need for longer time series• Historical time series to be build and maintained• Backcalculation or linking in base year changes

2. Improve international comparability• Seasonally adjusted data would enable comparison• More comparable information on the service sector

3. Review data collection techniques• Introduce sampling (and allow revisions)• Use administrative sources

4. Publication policy• New release practices (SA, time series, revisions)• More detailed metadata


General Recommendations on General Recommendations on STSSTS

Carsten Boldsen Hansen

Economic Statistics Section, UNECE



Overview• 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 (UN)

http://unstats.un.org/unsd/dnss/gp/fundprinciples.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, 2003http://unstats.un.org/unsd/dnss/hb/default.aspx


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 interpretation5. statistical agencies choose the data

sources with regard to quality, timeliness, costs and burden


The Fundamental Principles

6. strictly confidentiality of individual data and use exclusively for statistical purposes

7. statistical laws, regulations and measures to be made public

8. coordination among statistical agencies within countries

9. use of international concepts, classifications and methods

10. 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


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


STS vs Structural Statistics (SBS)

STS = measures economic developmentsSBS = 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!


List of Methodology Guidelines

• Methodology of Short-Term Business Statistics (EC), 2006 http://ec.europa.eu/eurostat/ramon/statmanuals/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-IndustrialStats.pdf

• Use of Administrative Sources for Business Statistics Purposes (EC), 1999 http://ec.europa.eu/eurostat/ramon/statmanuals/files/CA-24-99-897-__-N-EN.pdf

• International Recommendations for Distributive Trade Statistics (UN), 2009http://ec.europa.eu/eurostat/ramon/statmanuals/files/Inter_Rec_for_Distribut_Trade_Stat.pdf

• Methodological guide for Producer Price Indices for Services, (EC) 2005 http://ec.europa.eu/eurostat/ramon/statmanuals/files/KS-BG-06-003-EN.pdf

• Evolution of Service Statistics, proceedings of a seminar, (EC) 2002 http://ec.europa.eu/eurostat/ramon/statmanuals/files/KS-BG-02-001-__-N-EN.pdf

• Consumer Price Index Manual, Theory and Practice, 2004 (ILO) http://www.ilo.org/public/english/bureau/stat/guides/cpi/index.htm

• Practical Guide to Producing Consumer Price Indices, 2009 (UNECE/ILO)http://www.unece.org/stats/publications/Practical_Guide_to_Producing_CPI.pdf

• Producer Price Index Manual, Theory and Practice, 2004 (IMF) http://www.imf.org/external/np/sta/tegppi/index.htm

• Export and Import Price Manual, 2008 (IMF)http://www.imf.org/external/np/sta/tegeipi/index.htm


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


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


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)


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

Indicator DaysEurostat IMF

output 40 45prices 30-45 30new orders 50sales / turnover 60 60persons employed 60hours worked 90wages and salaries 90 90


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


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

Source: Index of industrial production (UN) & some NSO’s 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 statistics

UNECE Training Workshop “Short-term statistics and seasonal adjustment”




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

59Petteri Baer


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


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

–>500 Million internet hosts in the world!

• This also increases pressure on timeliness

Internet hosts in the Worldin the beginning of each year

* Millions *

0.00002 0.03 0.716

147

433

0

50

100

150

200

250

300

350

400

450

500

1982 1987 1992 1997 2002 2007


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


All potentialusers

These guysare real users

Heavy user

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 information


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!


Especially concerning Short-term economic statistics…

• Timeliness is an issue of huge importance

• Trade-off between timeliness and accuracy - always

• Globalization• Modern communication

tools


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 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


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


The importance of turning points in the assessing

development• In assessing economic

development• In designing financial and

monetary policy decisions

• In making investment decisions


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 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


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


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


STS Compilation STS Compilation with Multiple Data Sourceswith Multiple Data Sources

Anu PeltolaEconomic Statistics Section,

UNECE



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


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

Act Plan

Check

Do

Continuous improvement Q

uality

TimeBy Deming


Production Process

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

Publication

Collection of data

Correction of systematic

errors in data

Index calculation

Check for the most important observations


Data CollectionStatistical Units

• Corner stones of business statistics– Legal unit -> enterprise (services) -> 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


Source: Statistics Finland, Strategy for economic statistics

System of Statistics


• 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

Data CollectionQuestionnaire Design


• 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

Data CollectionSampling in Practice


> Business Register to be kept up-to-date with new units

Total population of unitsin the Business Register

Large units Medium units Small units

Stratification by economic activity

Covered on a complete

enumeration basis

Covered by sampling

Covered mainly by

administrative sources

or administrative 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.

Data CollectionAdministrative Data Sources


+ Reduction of response burden

+ Reduction of costs, data collection and manual work

+ Total populations - detailed classifications/regional 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

Data CollectionPros and Cons of Admin Data?


• National ID-system for enterprises• New production methods:

– to correct for negative values and different concepts– slow accumulation > 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

Data CollectionAdministrative Data and Quality


• 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!

Data CollectionLegislative Issues


CompilationCentral Role of VAT Data

Source: Statistics Finland


VATe.g. 250 000 units

• Turnover• Estimates for output

and missing data

Business Registere.g. 290 000 units

• Unit IDs• Activity code

• Location• Mergers

• LKAU (regional)

1. release

2. release

revision

optimalsampling

small & mediumenterprises

feedbackto BR

Samplee.g. 2000 units

• Turnover• Mergers

sample,basic info

updates to BRactivity of units

combining

CompilationLinking Admin and Survey Data


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


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

0

10000

20000

30000

40000

50000

60000

70000

80000

1 2 3 4 5 6 7 8 9 10 11 12Months

Previous year Current year

Change 115%


-2.40

-1.13-0.58

-2.33

-2.53

-4.1229.70

-1.20

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8Months

Index without a unit Index with a unit

CompilationImpact on the Results

index


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 changes1) Errors

2) Actual changes that are comparable

3) Actual changes that are non-comparable

UN Guide on the Impact of Globalization on National Accounts > helps with STS as well


CompilationExample of NCCs

Unit ATurnover = 100 million

Unit BTurnover = 75 million

Exchange of goods50 million

Turnover drops by one third due to a merger!No change in the level of activity!

Unit ABTurnover =

(100-50) + 75 = 125 million

Previous year Current year


CompilationAlternative Treatments of NCCs

1. All changes are recorded as they are (actual)− Contaminated with apparent, non-comparable changes− Difficult to obtain a picture of economic reality+ Simplicity

2. 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


CompilationAlternative Treatments of NCCs

3. 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

Firm X

Firm X


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, Components of Time Series, Seasonality and Pre-conditions for Seasonality and Pre-conditions for

Seasonal AdjustmentSeasonal Adjustment

Anu PeltolaEconomic Statistics Section, UNECE



Overview

• 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)

new observation

old observation

x 100


Components of Time Series

Seasonal adjustment is based on the idea that time series can be decomposed

The components are: Seasonal Irregular Trend


Trend-Cycle Component

50

60

70

80

90

100

110

120

130

Relation of ComponentsComponents of the Industrial Production Index of Kazakhstan

Ind

ex

20

05

=1

00

Original component

50

60

70

80

90

100

110

120

130

140

Jan-

00

Jan-

01

Jan-

02

Jan-

03

Jan-

04

Jan-

05

Jan-

06

Jan-

07

Jan-

08

Jan-

09

Jan-

10

Seasonal component

50

60

70

80

90

100

110

120

Jan-0

0

Jan-0

1

Jan-0

2

Jan-0

3

Jan-0

4

Jan-0

5

Jan-0

6

Jan-0

7

Jan-0

8

Jan-0

9

Jan-1

0

Irregular component

50

60

70

80

90

100

110

120

Jan-

00

Jan-

01

Jan-

02

Jan-

03

Jan-

04

Jan-

05

Jan-

06

Jan-

07

Jan-

08

Jan-

09

Jan-

10


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


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 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


IPI – KazakhstanAn Example of the Components of Time Series

Ind

ex

20

05

=1

00

50

60

70

80

90

100

110

120

130

140

Jan-

00

Jul-0

0

Jan-

01

Jul-0

1

Jan-

02

Jul-0

2

Jan-

03

Jul-0

3

Jan-

04

Jul-0

4

Jan-

05

Jul-0

5

Jan-

06

Jul-0

6

Jan-

07

Jul-0

7

Jan-

08

Jul-0

8

Jan-

09

Jul-0

9

Jan-

10

Jul-1

0

Original Seasonally adjusted Trend


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)


Seasonality

60

70

80

90

100

110

120

130

140

150

1 2 3 4 5 6 7 8 9 10 11 12

2000

2001

2002

2003

2004

2005

2006

2007

2008

Industrial production in Moldova, original series 2000-2008

months

Ind

ex

20

05

=1

00


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


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 DaysSaturday

Source: Analysis of Daily Sales Data during the Financial Panic of 2008, John B. Taylor (Target Corporation’s 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


0

5

10

15

20

25

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

avera

ge w

ork

ing

days

2009

2010

2011

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

0

5

10

15

20

25

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

aver

age

wo

rkin

g d

ays

2009

2010

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,

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


Pre-conditions for Seasonal Adjustment

1. Good quality of raw data– Strange values to be checked (zeros or outliers)– Revision of errors with new acquired data

2. Length of time series 36/12 or 16/4– At least 36 observations for monthly series and

16 observations for quarterly series needed

3. Consistent time series– To provide data according to a base year– Use of comparable definitions and classifications– Remove non-comparable changes

4. Solid structure– Presence of seasonality, moderate volatility– No major breaks in seasonal behaviour


Seasonal Adjustment Process Seasonal Adjustment Process with Demetra+with Demetra+



Overview

Seasonal adjustment process:

• Prepare and check

• Define and adjust

• Analyse and refine

• Document and publish


60

80

100

120

140

160

180

200

Prepare and check

Open Demetra+

Prepare a source file

Import data

Check the original series


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


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

1

2



• 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



Is seasonality present in the original series?


60

80

100

120

140

160

180

200

Define and adjust

Select an approach

Prepare calendars

Select regressors

Seasonally adjust


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 specifications – choose first either four (RSA4)

or five (RSA5)

Mark your specification

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

RSA4log/level, working days, Easter, outlier detection, automatic model identification

RSA5log/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 double click


Seasonally adjust

• For second adjustment of the same data, decide your update strategy

• Current adjustment = fixed forecasts

• Concurrent adjustment = nothing fixed


60

80

100

120

140

160

180

200

Analyse and refine

Visual check

Models applied

Quality Diagnostics

Refine and adjust


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 adjustment?


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 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


60

80

100

120

140

160

180

200

Document and publish

Document choices

Export data

Prepare publication

Support users


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


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 Why Seasonally Adjust and How?and How?

Approaches X-12-ARIMA, TRAMO/SEATS, X-13 A/S and Tools




Overview

• 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 statistics– Identify changes early, leading indicators, develop more

flash estimates -> quality vs. timeliness

• 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 Rate

Volume of Construction

-20%

-10%

0%

10%

20%

30%

40%

20

05

20

06

20

07

20

08

20

09

20

40

60

80

100

120

140

160

Change from corresponding month Trend series


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


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 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


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


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


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


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• Diagnostics of the quality and stability of the adjustments• Ability to process many series at once • Pseudo-additive and multiplicative decomposition• X-12-Graph generates graphical diagnostics


X-12-ARIMA

Source: David Findley and Deutsche Bundesbank


Model Based Methods• TRAMO/SEATS, STAMP,

”X-13-ARIMA/SEATS”• Stipulate a model for the data (V. Gómes and A.

Maravall)

• Models separately the trend, seasonal and irregular components of the time series

• Components may be modelled directly or modelling by decomposing other components from the original series

• Tailor the filter weights based on the nature of the series


TRAMO/SEATS

www.bde.es

• By Victor Gómez & Agustin Maravall, Bank of Spain• Both for in-depth analysis of a few series or for

routine applications to a large number of series• TRAMO preadjusts, SEATS adjusts• Fully model-based method for forecasting • Powerful tool for detailed analyses of series• Only proposes additive/log-additive decomposition

TRAMO = Time Series Regression with ARIMA Noise, Missing Observations and OutliersSEATS = Signal Extraction in ARIMA Time Series


DEMETRA softwarehttp://circa.europa.eu/irc/dsis/eurosam/info/data/demetra.htm

• By EUROSTAT with Jens Dossé, Servais Hoffmann, Pierre Kelsen, Christophe Planas, Raoul Depoutot

• Includes both X-12-ARIMA and TRAMO/SEATS• Modern time series techniques

to large-scale sets of time series• To ease the access of non-specialists • Automated procedure and

a detailed analysis of single time series• Recommended by Eurostat


X-12-ARIMA vs. TRAMO/SEATS

Source: Central Bank of Turkey (2002): Seasonal Adjustment in Economic Time Series.


Demetra+ software

• Users can choose: – Tramo-Seats model-based adjustments– X-12-ARIMA

• One interface• Aims to improve comparability

of the two methods • Uses a common set of diagnostics

and of presentation tools• Necmettin Alpay Koçak is

a member of the testing group


Common Guidelines

1. Use tools and software supported widely– Demetra+ will be supported by Eurostat– Methodological guidelines will be available– Results will be more comparable

2. Use your national calendars3. Dedicate enough human resources to SA4. Define a SA strategy5. Aim at a clear message to the users

– Consider which series serve the purpose of the indicator– Document all relevant choices and events


Useful references

• Eurostat is preparing a Handbook on Seasonal Adjustment

• ESS Guidelines on Seasonal Adjustment

http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-09-006/EN/KS-RA-09-006-EN.PDF

• Central Bank of the Republic of Turkey (2002). Seasonal Adjustment in Economic Time Series. http://www.tcmb.gov.tr/yeni/evds/yayin/kitaplar/seasonality.doc

• Hungarian Central Statistical Office (2007). Seasonal Adjustment Methods and Practices. www.ksh.hu/hosa

• US Census Bureau. The X-12-ARIMA Seasonal Adjustment Program. http://www.census.gov/srd/www/x12a/

• Bank of Spain. Statistics and Econometrics Software. http://www.bde.es/servicio/software/econome.htm

• Australian Bureau of Statistics (2005). Information Paper, An Introduction Course on Time Series Analysis – Electronic Delivery. 1346.0.55.001. http://www.abs.gov.au/ausstats/[email protected]/papersbycatalogue/7A71E7935D23BB17CA2570B1002A31DB?OpenDocument


Issues on Seasonal Adjustment in the Issues on Seasonal Adjustment in the EECCA countriesEECCA countries

Based on a UNECE Seasonal Adjustment Survey

Carsten Boldsen HansenEconomic Statistics Section, UNECE



Agenda

• Objective & Scope of the Survey of 2008• Survey Findings

– Current State of Seasonal Adjustment– Dissemination Policies– Applied Seasonal Adjustment Approach– Pre-Treatment of Time Series– Validation of Seasonal Adjustment– Plans for Future Development

• Recommended Future Measures


Objective & Scope of the Survey

• UNECE survey to National Statistical Offices (NSOs) and Central Banks (CBs)– Conducted in October 2008– Targeted to 17 CIS and Western Balkans countries– 11 answers were received from 10 different countries

• To obtain information on the current state in order to offer support

• Main factors identified: – Non-comparability due to lack of seasonal adjustment– Many have started seasonally adjusting– Interest is raising in order to:

• Detect turning points earlier• Enable comparison of sectors of the economy and countries


Current State of SA Practices

• 11/17 countries publish some SA data• Most common statistics seasonally adjusted are:

– GDP (6), Industrial production (3), Exports and imports (4)– NSOs adjust about 30 monthly and 30 quarterly time series

• 9 NSOs reported limited capacity in terms of SA• Unified SA procedure in 5/9 offices• All NSOs that perform SA have some methodological

descriptions about SA• Length of SA time series vary between 48 – 216

months, and quarterly between 16 – 72 quarters


Dissemination of SA Data

• Most publish raw data, SA and trend series– SA series are usually published in regular

publications and/or on the Internet – None of the countries published working day

adjusted series

• Most NSOs publish metadata on the methods of seasonal adjustment

• All eight countries that produce SA series disseminate the data also in paper publications


Seasonal Adjustment Approach

• The most commonly used SA approach is X-12-ARIMA <> TRAMO/SEATS in EU

• Some tendency to shift towards using TRAMO/SEATS and “X-13 ARIMA/SEATS”

• “X-13-ARIMA/SEATS” was expected to be the future tool for seasonal adjustment > Demetra+ software

• 4/9 NSOs currently performing SA have chosen their approaches via international orgs


Seasonal Adjustment Approach

0

2

4

6

8

10

12

14

16

TRAMO/SEATS X12-ARIMA X11-ARIMA X13-ARIMA/SEATS

No

of

co

un

trie

s

CIS and Western Balkans EU countries

Nu

mb

er o

f co

un

trie

s


Pre-Treatment of Time Series

= graphical analysis, calendar adjustment, model selection, filters, outliers and parameters

• Frequency of updating parameters varies– Some do not regularly update the parameters– Some update them, when new data appends– One country updates also in case of atypical weather conditions

• The choice of regressor differs – The most commonly used regressor in seasonal adjustment

is either working days or working days and specific holidays

• Outliers are detected in most cases by tests– Outliers are validated by experts in Armenia, Bosnia and

Herzegovina and Kazakhstan

• It is recommended to document events causing outliers!


• Mostly by using graphical inspection, autocorrelation function and measures of stability over time

• Graphical inspection:– Deviation between the raw and SA series– Standard deviation relative to trend – Intuitive assessment

• Two countries mentioned analysing significant peaks of the autocorrelogram of the raw series

• One uses the Box-Pierce statistics and three use F-tests

• Use of validation measures depends on the SA software used

Validation of Seasonal Adjustment



• Residuals and fit statistics were less used• Four countries mentioned using M-Statistics as

a quality measure of the results• A set of common quality measures are being

constructed– US Census Bureau, Eurostat and the Bank of Spain – For all seasonally adjusted series to be assessed by

the same criteria

> Demetra+ includes some quality measures both from X-12-ARIMA and TRAMO/SEATS



0

1

2

3

4

5

6

7

8

Graphicalinspection

Autocorrelationfunction

Statistics onresiduals

Fit statistics Stability over time

No

ou

t o

f 9 o

rgan

izati

on

s

Used Not used

Nu

mb

er o

f co

un

trie

s


Plans for Future Development

Type of Assistance Needed

1. All countries indicated some need for assistance, because of the lack of resources and knowledge

2. Five countries reported receiving assistance from international organizations

3. Regional training or workshops proposed by most of the respondents

4. Exchange of methodological information and experiences were suggested

Training 10

Technical assistance

9

Methodological material in Russian

7

Methodological material

4

Funding 3


Future Measures

• All reported some need for assistance• UNECE will organize training workshops

in 2010 – 2012• Methodological material and practical guidelines will

be produced and published also in Russian

What are your plans now?– Will be discussed next


Pre-treatment practices forPre-treatment practices for Seasonal Adjustment Seasonal Adjustment

Including Calendar AdjustmentIncluding Calendar Adjustment

Necmettin Alpay KOÇAK

UNECE Workshop on Short-Term Statistics (STS) UNECE Workshop on Short-Term Statistics (STS)

and Seasonal Adjustmentand Seasonal Adjustment14 – 17 March 2011Astana, Kazakhstan 182


Introduction

• Seasonal adjustment is a statistical procedure with the target of removing the seasonal (and calendar) component from a time series.

• The idea behind is that a series is composed by unobserved components such as trend, cycle, seasonality, irregular

• The seasonal component disturbs short-term analysis, so it is removed from the original series to facilitate the monitoring and interpretation of the economy by analysts

183


First step: the graph of the series

184

Each series must be plotted against time to detect visually whether or not a seasonal component is present (but in some case it is not sufficient!)Example: Industrial production index - Total, Kazakhstan :

Period: 2000M1-2010M10


First step: the graph of the seriesSeasonal graphs are a special form of line graph in which you plot separate line graphs for each season in a regular frequency monthly or quarterly data.Example: Industrial production index - Total, Kazakhstan :

Period: 2000M1-2010M10


The unobserved components


Decomposition scheme

• A time series yt can be decomposed in

Yt = TCt +St+εt

The Additive model

• A time series yt can also be decomposed in

Yt = TCt×St×εt

log(Yt)=log(TCt)+log(St)+log(εt)

Multiplicative model


The REG-ARIMA model

•The REG-ARIMA model is a convenient way to represent a time series with deterministic and stochastic effects. Given the observed time series zt , it is expressed as,

zt = ytβ+xt

Φ(B)δ(B)xt=θ(B)at•where•β is a vector of regression coefficients•yt denotes n regression variables•B is the backshift operator (Bkyt = yt-k )•Φ(B), δ(B), and θ(B) are finite polynomials in B• at is assumed a normal independently identically distributed (NIID) (0,σa

2) white-noise innovation


The regression variables

• The regression variables capture the deterministic components of the series. In TS, these can be of different type:– Calendar effects

• Trading day effect• Easter effect• Leap-year effect• Holidays

– Intervention variables generated by the program– Regression variables entered by the user– Outliers


The ARIMA model

• Model-based-pre-adjustment identifies and fits an ARIMA model on the linearized series (cleaned from deterministic effects). The ARIMA model is composed of three components:– the stationary AR component (polynomial Φ(B))– the non-stationary AR component (polynomial δ(B))– the invertible MA component (polynomial θ(B))

• For seasonal time series, the polynomials are given by:• Φ(B) = (1+ Φ1B + … + ΦpBp)(1+ Φ1Bs + …+ ΦPBs×P)

• δ(B) = (1-B)d(1-Bs)D

• θ(B) = (1+ θ1B + … + θpBp)(1+ θ1Bs + …+ θPBs×P)• A seasonal ARIMA model is identied by the order of its polynomials:

(p;d;q)(P;D;Q)


TRAMO / Reg-ARIMA

• Program for estimation, forecasting, and interpolation of regression models with missing observations and ARIMA errors, with possibly several types of outliers

• The program is aimed at monthly or lower frequency data (quarterly, semester, 4-month, bimonth, semester, year)

• Performs a pretesting to decide between a log transformation and no transformation


TRAMO / Reg-ARIMA

• Identifies the ARIMA model through an Automatic Model Identification (AMI) procedure

• Interpolates missing values

• Detects outliers

• Estimates the REG-Arima model

• Computes forecasts


Automatic model identification

• The ARIMA model can be automatically identified by the program• Two steps

– Obtains the order of differencing – max order ∆2 ∆s

– Obtains the multiplicative stationary ARMA model• 0<=(p;q)<=3• 0 <=(ps;qs )<=1• Chosen with the BIC criterion, favors balanced model (similar orders

of AR and MA parts)• Otherwise, it can be input by the user (parameters P,D,Q, BP,BD,

BQ)• It works jointly with the Automatic Outlier Detection and Correction

(AODC)


Outliers

• They represent the effect on the time series of some special events (new regulation, major political or economical reform, strike, natural disaster). Three possible forms of outliers:– Additive outliers (AO)– Level Shift (LS)– Transitory Changes (TC)


Outliers


Calendar effects

• Calendar adjustment removes those non-seasonal calendar effects from the series, for which there is statistical evidence and an economic explanation. Four possibilities in TS:– Trading days (working/non-working, 6 regressors))– National and moving holidays ((provided by the user))– Leap-year (TS versus X-12-ARIMA)– Easter

• A pre-testing on the presence of these effects.• If trading days are significant, adding the holidays

variable improves significantly the results!


Examples of calendar effects


Trading/Working Day Adjustment

• Aims at a series independent of the length and the composition in days– Length of month, number of working days and weekend days,

composition of working days (Monday/Friday)

• Working or trading-day adjustment is recommended for series with such effects– If effects not present –Regressors should not be applied

• Compile, maintain and update national calendars!– A historical list of public holidays including information on

compensation holidays


Correction for Moving Holidays

• Occur irregularly in the course of the year• Correct for detected moving holidays in series

– Not removed by standard filters– If effects not present –Regressors should not be

applied• These effects may be partly seasonal:

– The Catholic Easter, for example, falls more often in April than in March

• Since the seasonal part is captured by seasonal adjustment filters, it should not be removed during the calendar adjustment


An illustrative example for national calendar regressor

Number of days

Number of Sunday's

Official (fixed) holidays except Sunday's

Religious (moving) holidays except Sunday's

Working days

Average working days (1975-2015)

Mean Corrected working day regressor

Jan.97 31 4 1 0 26 25.14 0.86

Feb.97 28 4 0 2 22 23.76 -1.76

Mar.97 31 5 0 0 26 26.00 0.00

Apr.97 30 4 1 3 22 24.36 -2.36

May.97 31 4 1 0 26 25.14 0.86

Jun.97 30 5 0 0 25 25.24 -0.24

Jul.97 31 4 0 0 27 25.98 1.02


Original vs. Linearized series


Model Selection, Seasonal Model Selection, Seasonal Adjustment, Analyzing ResultsAdjustment, Analyzing Results

202

Necmettin Alpay KOÇAK

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

14 – 17 March 2011

Astana, Kazakhstan

March 2011 UNECE Statistical Division 203 203

Model Selection

• Pre-treatment is the most important stage of the seasonal adjustment

• X-12-ARIMA and TRAMO&SEATS methods use very similar (nearly same) approaches to obtain the linearized (pre-treated) series.

• Both method use ARIMA model for pre-treatment.

• The most appropriate ARIMA model → Linearized series of top quality

March 2011 UNECE Statistical Division 204 204

ARIMA Model selection

• zt = ytβ+xt

• Φ(B)δ(B)xt=θ(B)at

• (p,d,q)(P,D,Q)s → Structure of ARIMA

• (0,1,1)(0,1,1)4,12

• For the model– Parsimonious– Significance of parameters– Smallest BIC or AIC

• For the residuals– Normality– Lack of auto-correlation– Linearity– Randomness


Diagnostics

• Are there really any seasonal fluctations in the series ?– Seasonality test

• If, yes– Diagnostics based on residuals are the core

of the analysis.

• If, no– No need to seasonal adjustment.


Diagnostics

• Seasonality test– Friedman test– Kruskall-wallis test

• Residual diagnostics– Normality

• Skewness• Kurtosis

– Auto-correlation• First and seasonal frequencies (4 or 12)

– Linearity• Auto-correlation in squared residuals

– Randomness• Number of sign (+) should be equal the number of sign (-) in residuals.

• Final Comment... We select the appropriate model according to the state of the diagnostics.i


Seasonal Adjustment

• 2.1 Choice of SA approach

• 2.2 Consistency between raw and SA data

• 2.3 Geographical aggregation: direct versus indirect approach

• 2.4 Sectoral aggregation: direct versus indirect approach

• (Source : ESS Guidelines)


Choice of seasonal adjustment method

• Most commonly used seasonal adjustment methods• Tramo-Seats• X12ARIMA

• Tramo-Seats: model-based approach based on Arima decomposition techniques

• X-12-ARIMA: non parametric approach based on a set of linear filters (moving averages)

• Univariate or multivariate structural time series models• (Source : ESS Guidelines)


Filtering data: Difference in methods

• X-12-ARIMA use fixed filters to obtain seasonal component in the series.• A 5-term weighted moving average (3x3 ma) is calculated for each month of the

seasonal-irregular ratios (SI) to obtain preliminary estimates of the seasonal factors

• Why is this 5-term moving average called a 3x3 moving average?

3333

1 24121212122433

tttttttttx

tSISISISISISISISISI

S


Filtering data: Difference in methods

• TRAMO&SEATS use a varying filter to obtain seasonal component in the series. This variation depends on the estimated ARIMA model of the time series.

• For example, if series follows an ARIMA model like (0,1,1)(0,1,1), it has specific filter or it follows (1,1,1)(1,1,1), it has also specific filter. Then, estimated parameters affect the filters.

• Wiener-Kolmogorov filters are used in Tramo&Seats. It fed with auto-covariance generating functions of the series. (more complicated than X-12-ARIMA)

• But, it is easily interpreted since it has statistical properties.


Consistency between raw and SA data

• We do not expect that the annual totals of raw and SA data are not equal.

• Since calendar effect exists (working days in a year)

• It is possible to force the sum (or average) of seasonally adjusted data over each year to equal the sum (or average) of the raw data, but from a theoretical point of view, there is no justification for this.– Do not impose the equality over the year to the raw

and the seasonally adjusted or the calendar adjusted data (ESS Guidelines)


Direct and indirect adjustment

• Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series. The direct and indirect issue is relevant in different cases, e.g. within a system of time series estimates at a sector level, or aggregation of similar time series estimates from different geographical entities.

Industrial Production

Index

Mining and Quarrying

Manufacturing

Electricity, Water, Natural Gas and etc.

EU-27 Aggregate

Germany

France

Spain

Romania

...


Analyzing result

• Use a detailed set of graphical, descriptive, non-parametric and parametric criteria to validate the seasonal adjustment. Particular attention must be paid to the following suitable characteristics of seasonal adjustment series:

– existence of seasonality– absence of residual seasonality– absence of residual calendar effects– absence of an over-adjustment of seasonal and calendar effects– absence of significant and positive autocorrelation for seasonal lags in the

irregular component– stability of the seasonal component

• In addition, the appropriateness of the identified model used in the complete adjustment procedure should be checked using standard diagnostics and some additional considerations. An important consideration is that the number of outliers should be relatively small, and not unduly concentrated around the same period of the year.


Analyzing resultsTime

Series

DeterministicComponents

StochasticComponents

Trend-Cycle Seasonal IrregularOutliersCalendarEffects

Additive Outlier(AO)

Transitory Change(TC)

Level Shift (LS)Final

Trend-CycleFinal

SeasonalFinal

Irregular

Seasonally AdjustedSeries


Revisions to seasonal adjustment

• Forward factors / current adjustment: annual analysis to determine seasonal and trading day factors– Preferable for time series with constant seasonal

factor or large irregular factor causing revision

• Concurrent adjustment: uses the data available at each reference period to re-estimate seasonal and trading day factors


Revisions to seasonal adjustment

• Forecast seasonal factors for the next year (current adjustment)

• Forecast seasonal factors for the next year, but update the forecast with new observations while the model and parameters stay the same

• Forecast seasonal factors for the next year, but re-estimate parameters of the model with new observations while the model stays the same (partial concurrent adjustment)

• Compute the optimal forecast at every period and revise the model and parameters (concurrent adjustment)

Sources: Eurostat working paper on Seasonal Adjustment Policy, ESS Guidelines on Seasonal Adjustment


Evaluation of revision alternatives

• The use of fixed seasonal factors can lead to biased results when unexpected events occur

• Re-estimation in every calculation increases accuracy but also revision

• Re-estimation once a year decreases accuracy but also revision

Re-identification usually once a year However, time series revise in every release


Disseminating statistical information on economic

development

UNECE Training Workshop “Short-term statistics and seasonal adjustment”




Information services usually provided on economic

development• Opinion surveys (e.g., business and consumer surveys,

purchasing managers surveys, bank lending survey): at least monthly with a high timeliness

• Market data (e.g. stock market data, exchange rates, yields): at least daily, frequently “tick-by-tick”

• Short-term statistics (e.g. Harmonised CPI (HICP), unemployment rate, leading indicators): monthly

• Monetary and financial statistics (e.g. MFI (bank) balance sheets & interest rates, securities, balance of payments): monthly – mainly by central banks

• National accounts (e.g. GDP, sector accounts): quarterly and annually

Petteri Baer


Operational environment of services on economic statistics

• Growing demand for fresh and timely statistical information on the economic development

–Highlighted by the financial and economic crisis–But existing in all phases of the economic cycle

• Strong influence of rapid communication tools• Growing information overflow• Importance of quality issues

–For the service products themselves–For building up the reputation of official statistics

Petteri Baer


To develop better interaction with users we need to be proactive

• Who are our present users of economic statistics?

• Who are our present users of short-term economic statistics?

• Who are our POTENTIAL users of short-term economic statistics?


Nota bene! Users are different: Tourists, Farmers, Miners…

Courtesy to Armin Grossenbacher

We will most likely have to define different ways to approach different users, if we wish to have efficient communication

222Petteri Baer


So – Who are in need of information on STS? (1)

• The easy reply– Policy makers– Business community– Media and – General public

• But note: Policy makers are much more than only ministries

– Central Bank– High level advisory groups– The district (oblast) level– The local level– Trade unions, lobbies,

NGOs…



• The business community is a much broader target group than often believed

– Banks– Insurance companies– Big corporations– Medium sized enterprises– Chambers of commerce– Branch organizations– Employers organizations– Foreign companies– Etc.



• And numerous target groups mentioned above usually employ

– Business analysts, researchers, economists

• Or make use of– Information brokers– Business intelligence

systems or – Knowledge managements

systems• Do we provide sufficient

information services to them? In proper forms?


To disseminate economic statistics efficiently: Database services!

• We produce quite a lot of statistical information• Different users have different need structures, they want

information–By industries, By enterprise sizes–By regions–Comparisons over different time periods–International comparisons–And numerous other aspects…

• PC-Axis, PX-Web… User friendly services!

226Petteri Baer


The importance of good contact information

• User lists–Existing customers and

contacts–Regular and heavy

users of economical statistics

• Contact directories• Feedback contacts Contact / Customer

database Customer Relationship

Management (CRM)


To improve user interaction we will at least need a Customer Database

• For contacting• For surveys on

satisfaction or dissatisfaction

• For presenting new targeted services

• For other forms of interaction


Marketing

Customers

Mana-gement

Adminis-tration

Sales

Product development

Developmentprojects

Customerintelligence

Project intelligence

Contactintelligence

Lead intelligence

Informationretrievals Quote

intelligence

Campaignintelligence

Customer Database

Customerservice

Functions Information providers Information users

Projectgroups 229Petteri Baer


We will profit much from active feedback

• Having more feedback will help us to develop our services

• Interaction with critical customers will help us in having a positive pressure on performing better

• A demanding customer is like a grain of sand within the mussel. It doesn’t feel good but the result may be a beautiful pearl!


Publicity may sometimes be tough!

• Statistics often tends to attract hostile media coverage…

• “Why does it take so long?”

• “My own perception is different!”

• “Lies, Big lies, Statistics!”Any votes for the Census?But - is there an alternativeto providing better publicity?


Questions to be raised

• The importance of good and timely statistical information

–How can resources be allocated if basic information on the economic development is based on guesses or too old information?

–Attracting investments, doing good business, developing economic activities needs good infrastructure – reliable official statistics is fundamental

–If the denominators of such as population statistics, GDP etc. are wrong, no real information is reliable

232Petteri Baer


Take publicity seriously

• Develop useful statistical service products• Make a good plan of what will be published

–Publication calendar• Provide regular Media/Press releases• Make use of your agency’s Press Officer

–Press Conferences from time to time–…but not too often!–Also critical media should always be invited!–Cost plan and budget for publicity activities

• Follow up on media appearance – both quantities and attitudes

233Petteri Baer


The media is your partner

• In disseminating the main results of all your hard work on statistics

• In making problematic issues known

• Help the media to be well informed!


Statistical literacy

• Beyond students or school pupils• Special information seminars, breakfast sessions or other

kinds of light information meetings for selected target groups would be advisable

• The web site of the International Statistical Literacy Project of IASE could be helpful in planning

–http://www.stat.auckland.ac.nz/~iase/islp/countries

235Petteri Baer


Presentation of statistical information

on Short-term statistics• KISS – Keep It Short and Simple• Storytelling approach

–UNECE’s “Making Data Meaningful” materials 1 - 3–Available on the web, also in Russian, so far only 1 – 2

• Focus on turning points• Omit accidental events and “noise”

–The importance of seasonal adjustment

236Petteri Baer


Conclusions – Five items of great importance

• Existence of good STS services• Packaging the STS services into for the different user

categories relevant service products• Maintaining good accessibility on the web• Obtaining, updating and increasing contact information on

users• Meeting and discussing with and learning from main users

237Petteri Baer


The efforts you will make will be rewarded, because…

• Only used statistical information is useful statistical information!

• Thank you for your attention!

• [email protected]


How to Release Seasonally How to Release Seasonally Adjusted DataAdjusted Data ? ?

(Examples of release practices, metadata, maintenance)

Carsten Boldsen HansenEconomic Statistics Section, UNECE



Overview

• Quality of SA

• Revision policy

• Release practices

• Metadata

• How to get started?


Quality of SA

• Release SA data after you are convinced about the quality

• Explain possible quality issues to users– Quality of original data, length of time-series– Presence of strange features, outliers and

volatility

• Use more time with key indicators• Release documentation of all relevant

seasonal adjustment steps


Timing of Revisions

• SA data usually revised due to– Corrections and accumulation of raw data– Better estimates for the seasonal pattern

• Revisions are welcomed – They derive from improved information set– Forecasts are replaced with new observations in SA

• In SA one new observation can revise the past• Trade off between precision in SA data and

stability of seasonal adjustment pattern• Revision should be scheduled in a regular way


Nature of SA RevisionsIndustrial Production Index, Original Series

Release 9/2008

Release 11/2008

Release 12/2008

Release 10/2008



Nature of SA RevisionsIndustrial Production Index, Seasonally Adjusted

Release 12/2008

Release 11/2008

Release 9/2008

Release 10/2008



Features of the Trend Series

• End of the trend series may change direction• Problems with defining trends

– How smooth should it be vs. identification of turning points?

– Should it include economic ‘cycles’ or just long-term structural effects?

• Trend can have different annual totals from the original data

• Trend is a good visual tool


50.0

60.0

70.0

80.0

90.0

100.0

110.0

120.0

130.0

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

Published 05/2008

End-point ProblemTurnover in Advertising

50.0

60.0

70.0

80.0

90.0

100.0

110.0

120.0

130.0

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Published 05/2008 Published 03/2009


Releasing Time Series

A. Publish raw and some adjusted data, e.g. one of the following: SA series, SA plus WDA series, Trend-cycle series

B. Include only raw data in press releasesToo limited approach!

C. Present only levels or valuesToo limited approach!


Recommended Release Practices

• Publish rather index numbers than monetary valuesor both

• Month-on-month and change from the same month one year earlier are both useful

• A reference period needs to be determined• Provide long an coherent time series• Present the main contributors to change

– Present products / enterprise groups / industries that are primarily responsible for the monthly movement

Source: Index of Industrial Production (UN)


Revision Policy

• Revisions are inevitable to the quality of data• Be informative about the reasons for revisions

– Methodological, accumulation, errors, changes to classifications– Users should be reminded of the size of the likely revisions

• Correct errors as soon as possible • Revision policy be formulated: regular timing• Revisions to be carried back in time to maintain

consistent time series• Normal revisions: no explicit info & monthly release• Prior information of large scale revisionsSource: Common Revision Policy for STS (EC) & Index of industrial production (UN)


Advance Release Calendars

• Use of advance release calendars is recommended widely

• Reduces the chances of external interference with the release of statistics

• IMF requires the countries that subscribe to the SDDS (but not GDDS) to provide advance release information

• Statistics to be released as soon as the data becomes available and has been processed


Proposed Release Practice

1. Include both raw and SA data in the release, details on the web site– Time series (raw, SA, WDA, trend)

2. Avoid annualized or cumulative growth rates as the only indicator

3. Avoid presentation of trend data in press releases– Trend series are good in graphs!

4. Release several growth rates– “Period on period” growth to be computed on SA data!– Annual growth to be computed on non-adjusted data


Effect of Moving Holidays

1.2

8.1

5.1

0

1

2

3

4

5

6

7

8

9

Seasonally adjusted, from March 2008

Original data, from April 2007

Working day adjusted, from April 2007

Output of the national economy grew in AprilSeasonally adjusted output rose by 1.2 per cent in April from the month before. Year-on-year the increase amounted to 8.1 per cent according to the original series. April 2008 included three working days more than the comparison month of the previous year. Adjusted for working days, the year-on-year growth was 5.1 per cent.


Comparison of CountriesChange from Previous Period

Source: OECD statistical news release


Detecting Turning PointsChange from the Previous Period

Source: UNECE weekly

Seasonally adjusted industrial production growth rates from the previous quarter

-12%

-10%

-8%

-6%

-4%

-2%

0%

2%

4%

20

05

Q1

Q2

Q3

Q4

20

06

Q1

Q2

Q3

Q4

20

07

Q1

Q2

Q3

Q4

20

08

Q1

Q2

Q3

Q4

20

09

Q1

Q2

Q3

Q4

20

10

Q1

Q2

% f

rom

pre

vio

us

qu

art

er

CIS countries ¹ ² EU27 countries North America ²


Source: NSO of Tajikistan

Smoothening of DevelopmentWhat is the direction of development?

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

20

05

20

06

20

07

20

08

20

09

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

month-on-month

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

2005

2006

2007

2008

2009

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

month-on-month 3 months' moving average


Source:ONS


1. Non-technical explanation of SA2. Enough metadata for assessment of reliability3. Metadata to enable repetition of SA:

– Method and software used – Decision rules, aggregation policy– Outlier detection and correction methods– Revision policy– Description of working day adjustment– Contact information

A metadata template is annexed to ESS guidelines on seasonal adjustment!

Metadata for Different Users

Source: OECD data and metadata reporting and presentation handbook


Suggestions for Starting with SA

• Assess your and users’ needs• Define a clear SA policy, covering:

– Method, software, reanalysis, outliers, revision etc.– Choose simple and reliable method and software

• Allocate sufficient resources and time• Train staff• Inform users:

– about major events affecting seasonal adjustment– easy access to all relevant metadata

• Do not publish until confident with the results

unece workshop on short-term economic statistics (sts) and seasonal adjustment

Documents