Handbook on Seasonal Adjustment 2018 edition

832 Pages·2017·30.84 MB·English

Handbook on
Seasonal Adjustment
2018 edition
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MANUALS AND
GUIDELINES

Handbook on
2018 edition
Seasonal Adjustment
Printed by in Luxembourg

Manuscript completed in May 2018

Neither the European Commission nor any person acting on behalf of the Commission is responsible for
the use that might be made of the following information.

Luxembourg: Publications Office of the European Union, 2018

© European Union, 2018
Reproduction is authorised for non-commercial purposes only, provided the source is acknowledged.
The reuse policy of European Commission documents is regulated by Decision 2011/833/EU (OJ L 330,
14.12.2011, p. 39).
Copyright for the photographs: Cover © Golden House Studio/Shutterstock., chapters ©autsawin uttisin
and Golden House Studio/Shutterstock.
For any use or reproduction of photos or other material that is not under the EU copyright, permission
must be sought directly from the copyright holders.
For more information, please consult: http://ec.europa.eu/eurostat/about/policies/copyright

The opinions expressed herein are those of the author(s) only and should not be considered as representative of the
official position of the European Commission or the United Nations. Neither the European Union institutions and the
United Nations and bodies nor any person acting on their behalf may be held responsible for the use which may be
made of the information contained herein

Print ISBN 978-92-79-80169-3 ISSN 2363-197X doi: 10.2785/941452 Cat. No: KS-GQ-18-001-EN-C

PDF ISBN 978-92-79-80170-9 ISSN 2315-0815 doi: 10.2785/279605 Cat. No: KS-GQ-18-001-EN-N
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Foreword
Foreword
Since the last decade of the 20th century, with the high priority attributed to the production of infra-annual
statistics,EurostatstartedtoinvestontheenhancementoftheseasonaladjustmentprocessattheEuropean
and national levels. In a first phase the focus was put on the identification of the method(s) to be recom-
mended for the European Statistical System (ESS). After a long discussion and a detailed analysis only two
methods, and the associated software, were retained: the ARIMA model-based approach implemented in
TRAMO-SEATS and the moving average approach implemented in X-12-ARIMA with a slight preference for
the former. Then Eurostat concentrated its activity on the development of a new and user friendly software
for seasonal adjustment. The outcome of this period was the release in 1998 of Demetra, an interface to
bothTRAMO-SEATSandX-12-ARIMA. FinallyEurostatproducedasetofrecommendationstoharmonizethe
seasonal adjustment process within the European Union. The first version of these recommendations was
presented at the Seasonal Adjustment Methods Seminar (SAM-98) held in Bucharest in October 1998. A
slightly revised version of the recommendations, amended to take into accounts the specificities of national
accounts, was included as Chapter 9 of the Handbook on quarterly national accounts published in 1999 by
Eurostat.
From the early 2000’s Eurostat and the European Central Bank jointly worked to foster the harmonization of
the seasonal adjustment process with particular attention to some specific series. During this period several
task forces were organized to provide recommendations for the adjustment of key indicators like the Harmo-
nizedIndexofConsumerPrices,themonetaryaggregates,thequarterlynationalaccounts,etc. Amilestone
in this harmonization process among countries and sectors was the constitution in 2005 of the Seasonal ad-
justment steering group (now Seasonal adjustment Expert Group) co-chaired by Eurostat and the ECB. The
two main achievements of this group of high level European experts are: the European Statistical System
(ESS)Guidelinesonseasonaladjustment, firstpublishedin2009andrevisedin2015andthereleaseofthe
innovative JDemetra+ software whose version 2.0 has been officially recommended, since 2 February 2015,
tothemembersoftheESSandtheEuropeanSystemofCentralBanksassoftwareforseasonalandcalendar
adjustmentofofficialstatistics.
This Handbook is in fact the third component of a triptych of products including also the guidelines and the
JDemetra+ software. It aims to address all relevant aspects of the seasonal adjustment process also with
a forward looking spirit so that aspects considered not particularly important for the regular production of
seasonallyadjusteddatahavebeenincluded. Thisisthecaseforexampleoftheestimationofmovingtrading
days,theimprovementsofasymmetricfiltersandtheseasonaladjustmentofdailyandweeklydata.
TheHandbookisacollectionofcontributionswrittenbyhighlevelexpertsinthefieldofseasonaladjustment
fromallaroundtheworld. ThehandbookhasbenefitedofthecommentsandsuggestionsfromESSmembers
inthecontextofaconsultationorganizedbyEurostatandtheSeasonalAdjustmentExpertGroup.
EurostatwouldliketoexpressitsappreciationandgratitudetotheEditorGianLuigiMazzi(formelyEurostat)
and his co-editor Dominique Ladiray (French National Institute of Statistics and Economic Studies—INSEE)
for the time and effort they dedicated to bring this Handbook to fruition; and to all authors having contributed
tothevariouschapters. AspecialthanksgoestoDanA.Rieser,Eurostat,forhisroleastechnicaleditorand
thesupervisionoftheentirepublicationprocess.
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eurostat HandbookonSeasonalAdjustment 3

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EditorsandAuthors
Editors and Authors
Editors
Editor: GianLuigiMazzi(formerly: Eurostat)
Co-Editor: DominiqueLadiray(INSEE)
TechnicalEditor: DanA.Rieser(Eurostat)
Contributing Authors (in alphabetical order)
RobertoAstolfi Estela(Estelle)BeeDagum
OECD UniversityofBologna
roberto.astolfi@oecd.org [email protected]
KettyAttal-Toubert OlivierDarné
INSEE UniversitédeNantes
[email protected] [email protected]
MonicaBillio DuncanElliott
Ca’FoscariUniversityofVenice ONS
[email protected] [email protected]
MarkBoxall TuckerMcElroy
ECB U.S.CensusBureau
[email protected] [email protected]
DarioBuono ThomasEvans
Eurostat BureauofLaborStatistics
[email protected] [email protected]
LeonardoCarati LaurentFerrara
GRETA BanquedeFrance
[email protected] [email protected];[email protected]
WilliamP.Cleveland DavidF.Findley
FederalReserveBoard(retired) U.S.CensusBureau(retired)
david_fi[email protected];david.fi[email protected]
(cid:4)
eurostat HandbookonSeasonalAdjustment 5
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EditorsandAuthors
SusieFortier JensMehrhoff
StatisticsCanada DeutscheBundesbank
[email protected] [email protected]
RiccardoGatto JeanPalate
Eurostat NationalBankofBelgium
[email protected] [email protected]
StefanoGrassi TommasoProietti
UniversityofKentandCREATES UniversityofRomeTorVergata
[email protected] [email protected]
MichelGrun-Rehomme BenoitQuenneville
UniversitéPanthéonAssas-ParisII(retired) StatisticsCanada(retired)
[email protected] [email protected]
FabienGuggemos MarcoRiani
INSEE UniversityofParma
[email protected] [email protected]
AndrewHarvey MarcusScheiblecker
UniversityofCambridge AustrianInstituteofEconomicResearch
[email protected] [email protected]
EnricoInfante StuartScott
Eurostat BureauofLaborStatistics(retired)
[email protected] [email protected]
RobertKirchner MarcWildi
DeutscheBundesbank ZürcherHochschulefürAngewandteWissenschaften
[email protected] [email protected]
DominiqueLadiray MarkZhang
INSEE AustralianBureauofStatistics
[email protected] [email protected]
AgustinMaravall
BankofSpain(retired)
[email protected]
MarcoMarini
IMF
[email protected]
GianLuigiMazzi
IndependentExpert
[email protected]
CraigMcLaren
ONS
[email protected]
(cid:4)
6 HandbookonSeasonalAdjustment eurostat
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Tableofcontents
Table of contents
1 Introduction
byDominiqueLadirayandGianLuigiMazzi 23
1.1 Introduction 25
1.2 Seasonaladjustmentintheproductionprocessofinfra-annualstatistics 26
1.3 Aim,scopeandlimitationsofthehandbook 27
1.4 Structureofthehandbook 28
1.5 Conclusions 29
I General Aspects 32
2 SeasonalAdjustment: Objectives,Definitions,CostsandBenefits
byEstelaBeeDagumandGianLuigiMazzi 33
2.1 Introduction 35
2.2 TheMainCharacteristicsandCostofSeasonality 36
2.3 LimitationsofSame-MonthYear-agoComparisons 38
2.4 SeasonalAdjustmentMethods 40
2.4.1 MovingAverageMethods 40
2.4.2 Model-basedSeasonalAdjustmentMethods 41
2.4.2.1 Regressionmethods 41
2.4.2.2 Stochasticmodelbasedmethods 42
2.5 TheCalendarvariations 42
2.5.1 TheMoving-HolidayComponent 42
2.5.2 TheTrading-DayComponent 44
2.5.2.1 CausesandCostsofDailyPatternsofActivity 44
2.5.2.2 AClassicalModelforTrading-DayVariations 45
3 TimeSeriesComponents
byEstelaBeeDagumandGianLuigiMazzi 49
3.1 Introduction 51
3.2 TimeSeriesDecompositionModels 51
3.3 TheSecularorLong-TermTrend 53
3.3.1 DeterministicTrendModels 54
3.3.2 StochasticTrends 54
3.4 TheBusinessCycle 55
3.4.1 DeterministicandStochasticModelsfortheBusinessCycle 56
3.5 TheSeasonalVariations 57
3.5.1 DeterministicandStochasticModelsforSeasonality 58
3.6 CalendarVariations 59
3.6.1 TheMoving-HolidayComponent 59
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Tableofcontents
3.6.2 TheTrading-DayComponent 60
3.6.3 AClassicalModelforTrading-DayVariations 62
3.7 TheIrregularComponent 63
3.7.1 RedistributionOutliersandStrikes 64
3.7.2 ModelsfortheIrregularComponentandOutliers 64
4 ABriefHistoryofSeasonalAdjustmentMethodsandSoftwareTools
byOlivierDarné,LaurentFerraraandDominiqueLadiray 67
4.1 Introduction 69
4.2 Non-parametricmethods(implicitmodels) 70
4.2.1 X-11method 70
4.2.2 X-11stylemethods 72
4.2.2.1 SEASABS 72
4.2.2.2 GLAS 72
4.2.2.3 STL 73
4.2.2.4 SABL 73
4.3 Semi-parametricmethods(hybridmodels) 73
4.3.1 X11-ARIMA 73
4.3.2 X12-ARIMA 74
4.4 Parametricmethods(explicitmodels) 74
4.4.1 Deterministmethods 74
4.4.1.1 DAINTES 75
4.4.1.2 BV4 75
4.4.2 Stochasticmethods 75
4.4.2.1 ARIMA-modelbasedapproach 75
4.4.2.2 Structuraltimeseriesapproach 77
4.5 Conclusions 79
4.6 Annex: AshortdescriptionoftheJDemetra+software 81
4.6.1 Statisticalaspects 81
4.6.1.1 Statespacemodels 81
4.6.1.2 Seasonaladjustment 81
4.6.1.3 Otherdiagnostics 82
4.6.2 Technicalaspects 82
II Pre-Treatment Methods 92
5 CalendarEffects
byDominiqueLadiray 93
5.1 Introduction 95
5.2 TheVariousCalendarEffects 95
5.2.1 Afewwordsoncalendars 95
5.2.2 TheGregorianCalendar 96
5.2.3 TheTrading-DayEffect 96
5.2.4 MovingHolidays 97
5.2.5 Impactmodels 97
5.2.6 Whyshouldwetakeintoaccounttrading-dayeffects? 97
5.3 DetectionandEstimationofTrading-DayEffectsbyRegressionAnalysis 98
5.3.1 TheBasicFlowDay-of-WeekEffectModel 98
5.3.2 TheBasicFlowWeek-DayEffectModel 100
5.4 ImprovingtheRegressionModel 101
5.4.1 TakingintoAccountNationalHolidays 101
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8 HandbookonSeasonalAdjustment eurostat