Statistical analysis of cyclical fluctuations : the role of official statisticians.
Date: 2003-12-16
Institutions: Eurostat
Subjects: economic cycle, statistical method, statistics

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Statistical analysis of
cyclical fluctuations: 
the role of official 
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E U R O P E A N
C O M M I S S I O N

 
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4TH EUROSTAT AND DG ECFIN  
COLLOQUIUM ON MODERN TOOLS FOR BUSINESS CYCLE ANALYSIS 

"GROWTH AND CYCLE IN THE EURO-ZONE" 

20 TO 22 OCTOBER 2003 

Luxembourg, European Parliament 
Hémicycle, Schuman building 

Statistical analysis of cyclical fluctuations:  
the role of official statisticians 

Gianluigi Mazzi, Giovanni Savio 
Statistical Office of the European Communities,  
Eurostat, Luxembourg 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
STATISTICAL ANALYSIS OF CYCLICAL FLUCTUATIONS:
THE ROLE OF OFFICIAL STATISTICIANS

By Gianluigi Mazzi and Giovanni Savio∗
Eurostat, BECH Building, 5, rue Alphonse Weicher, L-2920 Luxembourg

Abstract

Infra-annual macro-economic statistics represent nowadays a key tool for economic
policy-making and business cycle analyses. The demand for timely and good quality
infra-annual macro-economic statistics for the Eurozone has strongly increased since
the advent of the monetary union in 1999. Although considerable progresses have
been made over recent years in this area, further improvements are needed for many
EU/Eurozone statistics as far as concerns timeliness, coverage, convergence of revision
practices and policies, and appropriate length of time series. In eﬀect, the traditional
approach based on harmonisation has serious drawbacks, at least in the short-medium
period, as it can induce breaks in the series and may cause delays in the provision of
ﬁnal estimates at the aggregated level, and diﬃculties can sharply increase in the near
future as a consequence of the enlargement process of the EU. At this stage, statisti-
cal and econometric methods can play a greater role to get timely ﬁgures, long time
series and even to construct new indicators. These methods, now an integral part
of the Euro-TREND module of Eurostat’s ﬁve annual program, can provide users
with estimates until the usual process based on harmonisation will give more reli-
able and complete ﬁgures. This paper, after a review of the institutional framework,
analyses projects and researches conducted by Eurostat in numerous ﬁelds relevant
for business cycle analyses. Firstly, it examines the results of researches - backward
calculation of time series, temporal disaggregation, nowcasting and revision analyses
- oriented towards an improvement of speciﬁc dimensions of data quality. Secondly,
it concentrates on some instruments aimed at improving the informative contents of
infra-annual statistics, such as seasonal adjustment, business cycle estimation, identi-
ﬁcation of turning points, and construction of composite indicators of business cycles.

1.

Introduction

Since its establishment in 1953, Eurostat has essentially focused its eﬀorts on the release
of structural and well-harmonised ﬁgures for the European Union. In eﬀect, for a long
time period the main statistical activity of Eurostat has consisted in the harmonisation
of national statistics through a set of legal acts covering diﬀerent areas. These acts were
essentially concerned with structural statistics, and accuracy rather than timeliness has
been the keyword of Eurostat’s actions in the various ﬁelds of interest for a long time
period. This approach was justiﬁed by the need to support relevant political decisions,
inter alia the attribution of structural funds, and regional and agricultural intervention
policies. Therefore, for a number of years short-term statistics were considered to be only
a useful complement to structural statistics.

From the end of the eighties the situation gradually started to change. Due to the
creation of a common market and the adoption of the Maastricht criteria, several legal

∗ Gianluigi Mazzi, Room B3 429, tel. (+352) 4301 34351, e-mail Gianluigi.Mazzi@cec.eu.int. Gio-

vanni Savio, Room B3 428, tel. (+352) 4301 35413, e-mail Giovanni.Savio@cec.eu.int.

acts concerning infra-annual statistics were adopted (e.g. Intrastat Directive, ESA95 and
HICP regulations).

Long pressure towards improvements of the short-term statistical information system
came from the requests of the European Central Bank (ECB), the DG ECFIN and other
institutional and non-institutional users. In the meantime, the interest of ﬁnancial market
analysts and newspapers towards the statistics released by Eurostat increased, and with
that the pressures for more timely, accurate and reliable statistics useful for short-term
analyses and interventions.

Eurostat, as other international statistical agencies, has in the last years put a great
eﬀort in the deﬁnition and implementation of a quality framework for the statistics re-
leased. While the ﬁrst step - the taxonomy of the term quality - has been completed
by a clear understanding of what the term quality actually should mean in a statistical
context, the second - the implementation of a quality framework - is still in due course,
though important improvements has been obtained over the last years. Now it is quite
common to speak about quality of statistics in terms of ﬁtness for use, and deﬁne quality
as a multi-dimensional concept (see Franchet and Grünewald 2002).

Eurostat is more and more concerned with the needs of users and it is doing a consid-
erable eﬀort to meet them. Even if the ECB and DG ECFIN are viewed by Eurostat as
privileged users for their role of making monetary policies and monitoring the European
economy, the needs of other institutional and non-institutional users are also carefully
analised.

The system of infra-annual statistics represents now one of the main Eurostat’s prior-
ities and diﬀerent actions have been taken in order to supply users with a wide range of
statistics able to give a general overview of the economic behavior of both the Eurozone
and EU-15, as well as all Member States economies.

Notwithstanding the numerous eﬀorts made, an analysis of the availability of short-
term information evidences some problems related to some speciﬁc aspects of data quality
which are relevant for infra-annual statistics, in particular timeliness, length of time series,
and availability of some key indicators for the Eurozone. Thus, in its conclusions of
October 2001 relative to the statistical requirements in the Eurozone, the Ecoﬁn Council
noted that further improvements must be achieved in terms of timeliness of key indicators.
In particular, the Ecoﬁn Council said that, within the next ﬁve years, Eurozone statistics
should attempt to emulate US standards of availability and timeliness.

Nowadays, Eurostat has a doubly challenging mission as far as concerns infra-annual
statistics. Firstly, it has to provide users with a complete set of reliable short-term in-
dicators for Member States as well as for the Eurozone and the EU. Secondly, it has to
assist and help economic policy/decision makers as well as short-term economic analysts
by focusing on main macroeconomic indicators in order to provide quasi-real time data
estimates and high quality statistical analyses. The Eurostat activities in the ﬁeld of
short-term statistics has consequently been structured in two main pillars: Euroindica-
tors services and the Eurotrend project. The objective of Euroindicators was to create,
manage and improve a website devoted to business cycle analyses. The heart of Euroindi-
cators website is Euroind database.

The objective of the Euroindicators, which represents the outcome of the Euro-SICS
project launched by Eurostat in 1999, is to provide a comprehensive and detailed portrait
of the short-term economic situation in the Eurozone, European Union, Member States
as well as Acceding Countries. Euroind database contains several monthly and quarterly
indicators and provides many breakdowns, such as by sector, product and country. As

stated in Ladiray and Mazzi (2000a) several quality problems still characterise infra-
annual statistics, so that it is essential to go over the oﬃcial statistics. At the moment,
Euroind is intensively used by a number of institutional and non-institutional users and
it is continuously improved. The Euroind quality is monitored by a number of synthetic
parameters deﬁned in the framework of a speciﬁc study made by Eurostat (see Sartori,
2001).

The Eurotrend project is explicitly dedicated to investigate topics having obvious and
useful feedbacks on Euroind and the research activity on short-term statistics in general,
such as backward calculation, nowcasting, construction of proxies for some unavailable
indicators (see Ladiray and Mazzi 2000b). Moreover Eurotrend aims at creating a per-
ceptive framework of the Eurozone economic momentum. In this light, topics such as
seasonal adjustment, business cycle estimation and turning points detection has been
extensively studied.

As such, the Eurotrend project constitutes a natural complement to traditional statis-
tical activities of data production. It represents a presidium for analyses and researches
explicitly devoted to improve the quality of basic infra-annual data series. Furthermore,
as institutional and traditional actions undertaken to improve the quality of infra-annual
statistics necessitate of a medium-long term period to be fruitful, especially in a com-
plex and integrated system, Eurotrend can provide an important complement to more
institutional actions.

The statistical actions launched by Eurostat in the last years to improve availability
and timeliness of short-term statistics will be analyzed in this paper, whose plan is as fol-
lows. The next Section describes in some details the institutional framework, whose aims
consist in putting into place measures (from more formal legal acts to simple gentleman
agreements) with Member states in order to improve data quality of short-term statistics
in various respects. Section 3 is dedicated to statistical methods which are directly used
to improve the quality of infra-annual statistics and on which Eurostat is at the moment
putting great emphasis within Eurotrend. Other mathematical and statistical methods
to improve the data-presentation of infra-annual series are analyzed in Section 4. Section
5 brieﬂy concludes.

2.

The institutional role of Eurostat in the improvement of infra-annual

statistics

Over recent years, the Commission, the Council, the ECB and the European Statisti-
cal System (ESS) as a whole have made very substantial improvements to the quality of
statistics, notably with respect to their comparability, coverage, timeliness and coherence.
In particular, since the signature of the Maastricht Treaty in 1992, several important ini-
tiatives have been undertaken to enhance European macro-economic statistics. Amongst
these, we only cite the ESA 95 regulation which, since April 1999, has compulsed Mem-
ber States to report national accounts data within a well-deﬁned time schedule on both
an annual and quarterly basis; high quality information for Harmonised Indices on Con-
sumer Prices (HICP) in Member States has been produced since January 1997; in May
1998 the Council adopted a Regulation setting up a common framework for the produc-
tion of short-term business statistics. All these legal acts are subject to a continuous
improvement process.

Further improvements for infra-annual statistics are still particularly needed with re-
spect to timeliness, frequency and coverage of data, the breakdown of statistics between
Eurozone and non-Eurozone, the further harmonisation of methodologies and the acces-

sibility of statistical information. This Section reports on recent developments achieved
on Eurozone statistics, the remaining weaknesses and the approaches taken to deal with
them.

Although the considerable progress made over recent years, many EU/Eurozone statis-
tics lack timeliness. The comparison with the best EU Member States and the United
States is particularly striking. Moreover, the quality of some of these statistics has been
criticised. In this respect, the Commission’s main objective is to accelerate the release of
a set of key EU/Eurozone infra-annual macro-economic statistics and to improve other
quality features. These notably include complete statistical coverage, consistency between
the diﬀerent sets of data, the transparency of the methods applied, sound monitoring and
detailed explanations of data revisions and the accessibility of statistical information.

To these ends, an important instrument used in the last years has been the so called
EMU Action Plan. The Action Plan covers a number of areas for short-term statistics:
quarterly national accounts, quarterly accounts for the government sector, statistics on
labour markets, short term business statistics and statistics on external trade. For each
Member State, the Action Plan has identiﬁed areas where progress is needed in the compi-
lation of national indicators. A list of action points has been established for Eurostat and
for each Member State (the National Action Plan). The Action Plans require Member
States to accelerate the production of national data series to permit timely compilation
by the Commission of reliable EU/Eurozone indicators. It also provides for improvements
in other quality aspects. Progress on implementing the Action Plan is reported regularly
to Ecoﬁn. The Action Plan has created strong momentum for statistical improvements,
and has already produced positive results, both in terms of coverage and timeliness, as
stated by the progress reports yet ﬁnalised.

In parallel to the Action Plan, an intra-EU and EU-US benchmark study was carried
out in September 2000. It conﬁrmed that the Action Plan, although very substantial,
might not be enough to match the US timeliness and best practices world-wide. A list of
more focused infra-annual macro-economic indicators (the Principal European Economic
Indicators or PEEIs) has therefore been set up by the so-called FROCH (FRiends Of
the CHair) group, coordinated jointly by Eurostat and Statistics Sweden, with more
challenging target release dates for EU/Eurozone indicators and other quality objectives
together with milestones (see Table 1 below). The list of PEEIs and the related objectives
will need reﬁning over time, notably to accommodate developments on services statistics.
The PEEIs on the list have been selected on the basis of the views of the ECB and the
DG ECFIN too, and on results from the benchmarking exercises so as to fully reﬂect
current user needs.

The Commission will compile and release PEEIs based on Member States’ contribu-
tions. This does not mean that all Member States will have to produce representative
indicators nationally with the same timeliness. But Member States, in particular those
whose economy has the most signiﬁcant impact on EU/Eurozone aggregates, are expected
to contribute the information that will be needed to produce and release timely and rep-
resentative indicators at EU/Eurozone level (the so called First for Europe principle).
Other Member States are asked to do their best to supply this information as well. All
Member States naturally remain free to release the indicators nationally, if they wish.

The Commission will disclose full information on the way in which each PEEI has been
compiled through the Euroindicators website and Euroind database and aims to release
EU/Eurozone indicators on the target dates indicated in Table 1. The achievement of
this objective is to a large extent dependent on Member States fulﬁlling the commitments

they have made voluntarily (i.e. by means of gentlemen’s agreements).

The Commission will develop a common dissemination platform with Member States
for the dissemination of PEEIs. This platform will be a speciﬁc domain accessible through
the Euroindicators website, and will cover both EU/Eurozone and national indicators.
This approach implies greater responsibility for Member States regarding the quality and
smooth functioning of the system. Member States will be directly responsible for the
release and updating of their national indicators through the site, but this will have to
be done in a way that appears consistent and co-ordinated to the users. They will have
to give explanations on the status of their national indicators and the methodologies
applied. The general aim is to permit the use of state-of-the-art technologies to reinforce
co-operation, taking account of the subsidiarity and proportionality principles.

List of PEEIs, periodicity and target and actual delays

For each PEEI, ambitious objectives have been set up regarding timeliness and other
quality aspects. Member States’ commitments within this programme include transmit-
ting contributions before speciﬁc deadlines. The information to be transmitted consists
of output data contributing to the various PEEIs, together with a qualitative assessment,
concise metadata (sources, methods and formulae) and explanations for signiﬁcant revi-
sions. Member States are also asked to participate actively in the studies and other work
concerning PEEI, and in particular to join task forces where appropriate and to con-
sider using the methodologies proposed by Eurostat where relevant. The commitments
planned under this concerted approach on PEEIs are made voluntarily. This means that
no new regulatory instruments are needed, as requests to Member States are based on
either already existing EU legislation or on voluntary arrangements (generally referred to
in the statistical ﬁeld as "gentlemen’s agreements"). Naturally, Member States will still
have to comply with EU legislation that concerns data related to the indicators. Where
the timeliness objective set by EU legislation is less stringent than the PEEI objective,
the latter should be pursued on the basis of a "gentlemen’s agreement".

Despite these strengths, several weaknesses in the system have surfaced occasionally.
These include large revisions to data for some countries, raising questions about the
quality of the data; a lack of information and transparency on some ﬁgures and trans-
actions, and the late transmission of EDP notiﬁcations by some Member States. Eﬀorts
have already been made to remedy and prevent these shortcomings, but they need to be
continued with increased vigor.

3.

Improving data quality through statistical techniques

The traditional instruments depicted above has two serious drawbacks. Firstly they can
generate breaks in series, for example, when a new country joins the European Union.
Secondly they generally cause delays as the various national institutes do not release the
basic statistics at the same time. Moreover, the compilation of a new statistical indicator
is often likely to be a long and diﬃcult task and no series are generally available for years.
On the other hand, the demand for timely and long time series is increasing greatly from
diﬀerent users. As the usual process based on harmonisation cannot quickly fulﬁll these
new needs, Eurostat is changing its strategy: statistical methods and econometric models
are now playing a greater rule to get timely ﬁgures, long time series and even to construct
new indicators. These models will provide users with estimates until the usual process,
based on harmonisation, can give more reliable ﬁgures. Moreover, some other statistical
analysis will be carried out on some important indicators to make interpretation and
analysis easier for economists.
The Eurotrend project was launched within Eurostat in 2000 in order to explore these
new directions. Its activity is mainly based on the Euroind database discussed above, but
focuses on a few important macroeconomic indicators at the Eurozone level. The idea
behind Eurotrend is to use statistical and econometric modelling techniques to arrive at
ﬁgures for Europe that can be used by short-term analysts, pending results derived from
the usual process based on harmonisation.
Ideally Euroind database aims at providing series spanning over 15 years , that is covering
at least two complete economic cycles, and real time updated. 20.4% of the active series
of the database cover 15 years or more, and 72.1% cover 10 years at least. Unfortunately
the situation is much less satisfactory for the Eurozone. The situation varies substantially
from one domain to the other. Even if it is diﬃcult to evaluate the punctuality of the series
in the Euroind database it appears clearly that there are several delays on the updating

of the database which cumulate themselves with a well known production delays (lack
of timeliness). Backward recalculation and ﬂash estimates of data for this area have
become a priority for Eurostat as discussed below. Moreover, the pressure from users
to obtain data with a higher frequency for macroeconomic indicators requires the use of
sophisticated interpolation and extrapolation techniques.

Weights of domains and frequencies of the series in the Euroind data base

Length of the series by area in the Euroind data base

Length of time series in the Euroind data base

3.1 Temporal disaggregation

It is often said that the analysis of business cycle should be conducted on time series
spanning a suﬃciently long time, with a golden rule given either by a minimum of two
complete business cycles (10 years or more of sample), or by a suﬃcient number of
observations to conduct eﬃcient statistical analyses. For the problems analysed above, in
many situations these conditions are not fulﬁlled by many series. The problem at hand can
be circumvented, at least partially, by the use of temporal disaggregation/benchmarking
techniques. Their use have been advocated in the conclusions of the Task Force on
benchmarking exercise of EU-USA statistics in order to improve, among other things,
timeliness. These techniques interpolate or distribute economic time series observed at
low frequency into compatible higher frequency data using or not related time series, and
can also be useful for extrapolation of the target series over the sample period. Temporal
disaggregation/benchmarking techniques can also increase data quality through their use
in terms of forecasting the actual series according to the evolution of the indicator series
observed at the higher frequency (Chow and Lin, 1971). Their use can be also relevant
in backcasting exercises discussed below as they provide a valid instrument for balancing
purposes.
While interpolation refers to the estimation of missing observations of stock variables,
a distribution problem occurs for ﬂow and time averages of stock variables. In the dis-
tribution case, for example, the problem concerns the estimation of intra-period values
for a given time series subject to the constraint that their sums (or averages) equal the
aggregates over the lower frequency.
The need for temporal disaggregation can stem from a number of reasons other than those
mentioned above. For example national statistical agencies, due to the high costs involved
in collecting the statistical information needed for estimating national accounts or other
statistics, could decide to conduct large sample surveys only annually. Consequently,
quarterly (or even monthly) data could be obtained through an indirect approach, that
is by using related quarterly (or monthly) time series as indicators of the short-term

dynamics of the annual (or quarterly) aggregates. As another example, econometric
modelling often implies the use of a number of time series, some of which could be
available only at lower frequencies, and therefore it could be convenient to disaggregate
these data instead of estimating, with a signiﬁcant loss of information, the complete model
at the level of the lower frequencies. Again, the construction of composite indicators of
business cycle (see below) requires the use of monthly time series, but some variables
(such as GDP) can be only available at the lower frequencies. Then, these series are
properly disaggregated before being used following the traditional approach.
Temporal disaggregation has been extensively considered in the econometric and sta-
tistical literature and numerous solutions have been proposed. Broadly speaking, two
alternative approaches have been followed:

1) methods which do not involve the use of related series but rely upon purely mathe-
matical criteria or time series models to derive a smooth path for the unobserved series;
2) methods which make use of the information obtained from related indicators ob-

served at the desired higher frequency.
The ﬁrst approach comprises, amongst others, the model-based methods (Stram and
Wei 1986; Wei and Stram 1990) relying on the ARIMA representation of the series
to be disaggregated (e.g., see Eurostat, 1999, for a survey and taxonomy of temporal
disaggregation methods).
The latter approach includes, amongst others, the adjustment procedure due to Den-
ton (1971) and the methods proposed by Chow and Lin (1971), Fernández (1981) and
Litterman (1983).
Two important extensions of traditional approaches to temporal disaggregation has re-
ceived some attention in the last years. The use of state space models for temporal
disaggregation, originally due to Harvey and Pierce (1984), has been further developed
in the framework of structural time series models by Durbin and Quenneville (1997),
Harvey (1989), Harvey and Chung (2000) and Moauro and Savio (2003). The use of a
multivariate approach (notably in the ﬁeld of structural time series models) in this con-
text allows empirical research to overcome some weaknesses of traditional approaches,
namely the hypotheses of weak exogeneity of the variable to be disaggregated and the
existence of a behavioral relation between the series. As stated by Harvey (1989), none
of these assumptions are necessarily fulﬁlled in current practices.

Another promising line of research in this ﬁeld consists in the introduction of dynamic
paths in the univariate econometric relation between the target and the indicator series.
These researches have received some attention in these lasts years, for example in the
works by Gregoir (2002), Salazar, Smith and Weale (1997), Di Fonzo (2002) and Santos
Silva and Cardoso (2001). Other important lines for research are non-linear transfor-
mations of the series (Pinheiro and Coimbra, 1993) and non-linear transformations and
dynamic speciﬁcation of the model (Weale et al., 1994).

It is under study the possibility to extend the program Ecotrim developed by Eurostat
to introduce in it some of the major disaggregation methods appeared recently in the
literature, notably the multivariate SUTSE approach and the dynamic regression frame-
work. This study is likely to receive support from the OECD. Empirical applications
of temporal disaggregation techniques to themes relevant for our purposes have received
attention in recent years at Eurostat and now are in the process to be ﬁnalised. We only
cite here the estimation of a monthly disaggregation for quarterly GDP at the Eurozone
level, with preliminary results discussed in Astolﬁ, Ladiray, Mazzi, Sartori and Soares
(2001).

A similar approach has been used to build a monthly indicator of Construction, but
the results are still under evaluation due to the lack of stability and timeliness of the
original series. Under this conditions, the results of simulation are dramatically aﬀected,
so that it is impossible at the present to ﬁnalise this important work.

3.2 Backward calculation

The need for economic time series homogeneous and, at the same time, deﬁned over the
longest possible period is a central issue for statisticians, econometricians and economic
analysts. Unfortunately, due to changes in deﬁnitions and/or new basic data availability,
the available series very often, show inconsistencies and breaks in their dynamic proﬁle.
In other cases the partial availability of the information could actually prevent the calcu-
lation of the series. In most of these cases, estimation techniques which make use of all
the available information to get ‘reasonable’ estimates of the series of interest can oﬀer
a valid solution, whose quality and usefulness are strictly linked to the amount of basic
information processed and to the way in which the various ‘pieces’ of information are
combined.

Eurostat is currently working with GRETA ASSOCIATI in the framework of backward
calculation for a set of relevant economic variables for the Eurozone: quarterly GDP
and its main components on the expenditure side and on the supply side; Industrial
Production Index and its disaggregation by main industrial groupings (MIGs); Industrial
turnover index; Total employment; Deﬂated retail trade index; Producer price index;
Unemployment.

As far as possible, the objective has been to carry out the retrapolations of the above
series back to 1970 with the greater level of detail. Covered periods and other information
on the series analysed are reported in the Table at the end of this Section.

yt
{

The problem of backward calculation can be formally put in the following way. Let
t=T
t=0 available from time 0 to time T , and a set of related
us consider a time series
}
t=T
t=0 available from time M > 0 to time T , which can include yt observed at
Xt
variables
}
{
diﬀerent, generally lower, frequencies (e.g. annual instead of monthly) and/or proxies for
yt, observed at various frequencies. We deﬁne back-recalculation (alternatively named as
back-calculation, back-casting, back-forecasting, retrapolation or backward calculation)
of yt the process that reconstructs yt in the range
1 using the information
contained in

M to

−

−

A number of possible alternative methodologies can be used for back-calculating a
time series, from simple univariate interpolation and/or extrapolation up to very complex
statistical models. The back-recalculation of a time series requires several steps, the ﬁrst
being the search for the relevant related information and their statistical analysis. The
other points are: choice of the retrapolation approach (and comparison of direct versus
indirect strategies); statistical testing of the available related series; model choice; and
ﬁnally the back-calculation.

Xt
{

t=T
t=0 .
}

An important point to be noted is that the statistical features of the relevant informa-
tion and the relation between the target series and the available information set drastically
aﬀect the choice of the model.

Then, we cannot suggest a general speciﬁcation to back-forecast a series but we point

only to a set of necessary steps to be considered.

For these reasons, the methodology used varies case-by-case, though the philosophy
chosen generally belongs to the regression approach, possibly without an ARIMA struc-
ture on residuals. Multiple step procedures have been used intensively by combining

direct and indirect reconstructions, together with the appropriate use of disaggregation
(balancing) techniques.
Searching for related information and statistical analysis This point involves the analysis
of the various relevant sources. Assume we are interested in the back-calculation of GDP
for the Eurozone at the quarterly level. The relevant information matrix can include: the
Eurozone GDP at an annual level; the Eurozone member countries GDP both at quarterly
and annual level; moreover, relevant information could be recovered from quarterly and
annual GDP series in ESA79 systems for both the Eurozone and the member states.

In this respect, the various statistical sources have been the Euroind and NewCronos
data-bases. Additional information have been obtained from other European Community
sources, such as DG ECFIN, and the ECB. Moreover, other international organisations
have provided relevant data, amongst them the OECD. Finally, the National Statisti-
cal Institutes (NSIs) and the National Central Banks have been considered as possible
sources of information. Additional data have been obtained from data providers, such as
Datastream.

Given the matrix of proxies obtained from alternative sources, a ﬁrst analysis should
point to any diﬀerences in the their deﬁnitions from the various sources. It may be the
case that the deﬁnition of the series used by Eurostat does not coincide with the one
of the OECD or of NSIs. The discrepancies, if any, must be identiﬁed and taken into
consideration before the choice of the model.

A descriptive, at least graphical, analysis has been carried out. It is aimed at pointing
to changes in the base years for indices and series deﬁned in real terms. Moreover,
it can highlight structural breaks or changes in the variable deﬁnitions that have not
been translated into a complete revision of the data-sets. These can inﬂuence the model
choice, discarding reconstructions on the levels in favour of back-recalculations based on
the growth rates or vice versa.

A further step involves statistical testing. In particular unit root and cointegration
It
tests have been considered. Cointegration is a desirable property in this context.
points to the reliability of the indicators and allows their use for back-recalculation. A
lack of cointegration should be considered as evidence of a biased indicator that should
not be used at all. Finally, time reversibility has been checked (Ramsey and Rothman
1996). This property is useful since it enables modelling a series reversing its time scale.
Then, as stated later, it is desirable in a back-recalculation framework.
Choosing the approach Two diﬀerent approaches should be used, the indirect approach
and the direct approach. The indirect approach requires the preliminary estimation of the
missing data for each Member State, or for most of them, and the subsequent aggregation
of the national series in order to estimate the Eurozone aggregate. The direct approach
requires the estimation of the Eurozone aggregate starting from an incomplete set of
national data and then the estimation of the entire European aggregate using the available
information.

The choice between these two approaches depends on the available information at the
national and Eurozone level. After searching for relevant information, a choice between an
indirect or a direct approach would be made. The evaluation must be considered when
the series has certain characteristics. In fact, generalising the concept, the distinction
between the direct and indirect retrapolation exists only when the series is obtained by
aggregation of at least two components. This is the case of any Eurozone variable, which
is obtained by a weighted average or a sum of member countries series. Moreover, the
distinction can be applied for the GDP of, say, Austria, which is by deﬁnition given by

M,

1)

−

−

the sum of Private consumption, Gross ﬁxed capital formation, etc.. The direct approach
should be considered whenever the information about the series are suﬃcient and reliable
enough to perform a back-calculation (as an example, the GDP of Eurozone can be
directly recovered if we consider the OECD Eurozone GDP series as a reliable indicator).
Alternatively, the indirect approach should be used (it may be the case that Austrian
series of GDP components are more reliable that the GDP series itself).

It should be noted that the indirect approach requires the use of a larger amount of
information, which gives robustness to the back-calculation. Finally, mixed approaches
can be considered:
in this case a ﬁrst step will involve a retrapolation of the relevant
components, and a second step will concern the direct reconstruction given the results of
the previous estimations*.
Model choice Here we distinguish among a set of alternative situations inﬂuencing the
set of usable models.

a) yt is not available in the range (

M,

1) at the desired frequency but at a lower one

−
and at least one indicator is available at the desired frequency in the range (

−

This case consists in a problem of constrained retrapolation in which the lower fre-
quency observations of yt act as a benchmark. The models and procedures to be applied
in this context are those considered in the time series disaggregation literature discussed
above.

b) yt is not available in (
M,
(if available) are time reversible

−

1) at any frequency, yt and the set of related indicators

−

This is an ideal situation. Time reversibility allows for the use of a number of models
in a forecasting framework. Moreover, the availability of one (or more) related indicators
provides a trajectory for the future (the past in the original series) evolution. The class
of the available models includes univariate methods like the ARMA models and a group
of speciﬁcations that can be represented by a regression approach: simple OLS with or
without ARMA residuals, ARMAX and ADL models, error correction representations. In
the following we consider the advantages and disadvantages of the various cited models.

Univariate methods (ARMA models)
Let us suppose that no additional information is available except for the data of the
(incomplete) time series of interest. In principle, provided that a good ﬁt can be ob-
tained, the estimates could be drawn from a forecast of the series modelled through an
ARMA(p, q) process ﬁtted on the reversed series*.

Regression methods
Sometimes the dynamic patterns of yt and Xt in the overlapping period show a tendency
to move together. In such cases, it is quite natural to use a linear model linking the

*The pro-rata estimation is another option at disposal, but it has to be considered only for indirect
reconstructions and in a multi-step approach. This method could be used when the coverage of the
available data at the aggregate level or the coverage obtained through the approaches mentioned above,
is quite high and no auxiliary information useful to reconstruct the remaining missing data are available.

The pro-rata estimation strategy consists in the recursive application of two estimation steps:
1. First, for a series at the desired breakdown, the aggregate value is estimated using the available,
contemporaneous country data and the country shares calculated over the nearest time period for which
all country data (for that series) are available;

2. The basic country missing values are thus estimated as a proportion of the previously estimated

aggregate series.

*However, this simplistic solution should be avoided whenever the back-calculation range (

1)
is longer than the sum of AR and M A orders. In fact it is known that the point forecasts after p + q
steps converge, and are not signiﬁcant apart for their tendency.

M,

−

−

variables and then calculate the conditional (or linear) expectation of the unobserved
variable as a function of the available data set. The general model can be written as
yt = f (Xt) + (cid:18)t where f is a generic algebraic function and (cid:18)t is the usual white noise
term, but can also have an ARMA representation. An adequate parametrization of the
function f allows for the recasting in a regression formulation of various models.
In
fact, ARMAX and ADL models can be represented by a dynamic regression. Moreover,
recalling that the ECM models can be represented by an ADL, also the cointegration case
(if it is supported by the data) is contemplated . Finally, the set of related indicators can
also include deterministic variables such as a trend or seasonal dummies.

c) yt is not available in (

M,

1) at any frequency, yt and the set of related indicators

−

−

are time irreversible

The lack of time reversibility sensibly restricts the range of the useful models, as we
will see in a few examples.
In the following we deal with the models considered in
the previous point, highlighting the motivation for their usefulness (or uselessness) for a
backward calculation.

−

−

−

. . .

φ1F

Univariate methods (ARMA models)
The ARMA models can be expressed in the following alternative way (backward form):
φpF p)zt = (1 + ϑ1F + . . . + ϑpF q)(cid:18)t, where zt is a stationary process
(1
(obtained, for example, by a suitable transformation of yt such as ﬁrst diﬀerence, logs,. . . ).
This equation can then be used to forecast past values of the series. The parameters of
the backward ARMA form are obtained by estimating the traditional ARMA model
and then applying the coeﬃcients on the time-reversed observations. However, their use
in providing long horizon back-forecasts cannot be sustained, since the results may be
unreliable. Let us assume the model is of the form zt
1+ (cid:18)t and we reverse the
−
model obtaining zt
1 as a function of zt. Given the stationarity of the estimated AR(1)
parameter, the back-forecasts will necessarily diverge to
depending on the sign of
1
φ−
> 1) unless we estimate a non-stationary model.
1
Assume now that we ﬁtted a simple ARMA(1,1) model. We reverse again the model
1 + (cid:18)t. Besides the known divergence caused by the AR
obtaining zt
component, we must evidence that the MA part will disappear in the back-forecasts, a
fact that can be justiﬁed by the substitution of the residuals by their unconditional mean
but that add unreliability to the back-calculation. Therefore, the use of ARMA terms
should be avoided.

φ1|
1
1 zt+ ϑ1(cid:18)t
1 = φ−
−

1
1 zt
1 = φ−
−

< 1 we have that

(since

1
φ−
1

±∞

¯
¯

¯
¯

−

−

|

Regressions methods
For similar reasons the dynamic speciﬁcations discussed above should be avoided as
well. One could rule out all the models which consider a dynamic in yt, leaving only
representations that are static or that allow for lagged related indicators. These regression
models could be generalised with ARMA terms in the residuals, however this should be
carefully considered. In fact, residual correlation points to a model misspeciﬁcation, while
MA terms could be used only to improve the estimation of regression coeﬃcients.

A further remark concerns the use of the levels or of some transformation of the variable
to perform the back-recalculation. In principle, the use of the levels can be justiﬁed pro-
vided they are stationary, otherwise some diﬀerences of log-returns should be considered.
The log-transformation can be applied for two reasons: reducing the scale of the series,
or with the idea of performing the regressions on growth rates (logarithmic returns). The
problem concerns then the choice of the amplitude of the diﬀerentiation: should a ﬁrst
diﬀerence represent the best choice or could we alternatively consider a fourth diﬀerence
for quarterly series and a twelfth diﬀerence for monthly variables?

There is no clear answer. The choice must be made on a case-by-case analysis and
must be supported by unit root tests. Moreover, an economic justiﬁcation can be added:
annual growth rates could be used for series that do not show clear seasonal patters
or for cases where the annual growth rate is more signiﬁcant or has a better economic
interpretation compared to a monthly or quarterly growth rate. In addition, the over-
diﬀerentiation problem must be considered. In fact, the seasonal diﬀerence should not be
used if the diﬀerenced series will evidence moving average behaviour up to the seasonal
order.

A problem strictly connected with the choice of the correct diﬀerentiation order is
to the presence of cointegration among variables. Time irreversibility prevents the use
of ECM or ADL models. Therefore, modelling the levels, the ﬁrst diﬀerence for I(1)
series or the seasonal diﬀerence for seasonally integrated series, will necessarily produce
autocorrelated residuals. In any of these situations the eﬀects of using a miss-speciﬁed
model should be evaluated case-by-case, using the directions of the following section.

An objection to this approach may be the lack of dynamic in the model or the lack
of the error correction when we model the delta-log. In this case, the deletion of ECM
may be justiﬁed if the related coeﬃcients are small and if the long-run equation provides
small corrections (relatively to the growth rates). This behaviour can be seen as a further
justiﬁcation of the use of a static model. Whenever two or more alternative models are
available to perform the back-calculation we face the problem of determining the preferred
speciﬁcation. We suggest performing the comparison analysing the power of the various
In such a case standard comparison tests of
model within an in-sample comparison.
forecasts have been used together with a set of distance measures between the true series
and the back-forecasts. Whenever the alternative models have provided very close results
the choice has been to consider a combined back-cast.

The data backward calculated during the project and the periods for which the esti-

mations have been carried out are reported in the Table below.

Series

Freq. Actual time span Retrapolated sample

Industrial production index and MIGs M
Q
M
M
M
M
M
M

GDP, GVA and components
Industrial turnover index
Consumer prices index (HICP)
Consumer prices index (CPI)
Deﬂated retail trade index
Producer prices index
Unemployment

1985:01-present
1991:01-present
1995:01-present
1990:01-present
1960:01-present
1995:01-present
1981:01-present
1991:06-present

1970:01-1984:12
1970:01-1990:04
1975:01-1994:12
No retrap.
No retrap.
1970:01-1994:12
1970:01-1980:12
1970:01-1991:05

3.3 Nowcasting techniques and early estimation of Europen aggregates

The idea is to provide users with initial estimates before the results of the usual calcu-
lations for the indicator are available. This problem is of course similar to the problems
of retropolation, mentioned earlier, and to forecasting in general. The statistical tools
are thus the same: univariate forecasting models, dynamic regression or transfer function
models, VAR models, and non-linear models.

Eurostat is currently working with the NIESR on a comparative analysis of these
various techniques for providing quick estimates. An important step in this project
relates the Euroarea data to a US timetable. The project starts with the consideration

that data on industrial production, GDP and producer prices are published considerably
later in the Euroarea than in the United States as the following Table shows.

Release lag for some key aggregates

Country US Euroarea France Germany Italy

Aggregate
Industrial production
GDP
Producer prices

15
24
10

45
45
38

41
50
30

40
45
27

45
40
28

This project is to explore whether using auxiliary data available, would be possible to
produce estimates with the same timetable of United States. It is, in fact, always possible
to produce estimates using modelling methods of one sort or other. The key question is
whether estimates generated using plausible techniques are likely to be reliable enough
to make Eurostat comfortable releasing them and users happy of using them.

The main auxiliary variables available to the required timetable are the business survey
results produced by DG ECFIN. They are published at the end of the month to which they
relate and thus the estimates produced using them would be available with no delay. For
the production of estimates of quarterly GDP growth two months’ industrial production
ﬁgures are available ﬁfteen days after the end of the quarter. These have been included in
our data set, so that the estimates of GDP would be available with a delay of ﬁfteen days.
Use is also made of data on oil prices and the Euroarea real exchange rate available in real
time. All possible models have been explored which could be constructed using relevant
survey variables and the other auxiliary variables. The best performing model (identiﬁed
by a standard statistical criterion) has been selected and used to make a prediction of
one period ahead. The exercise has been repeated recursively so as to be able to identify
the performance of forecasts generated using the model which would have been selected
at each point in time. This provides a realistic guide to the performance of the models
without the element of hindsight so often present when models are assessed on the basis
of in-sample performance. The simulation has been conducted using the survey data as
published. They are seasonally adjusted using a programme, the DAINTIES, which is
not very well known and quite out of date. We have also looked at the performance
of our models when the data are adjusted using Tramo-Seats, a widely-used seasonal
adjustment package. The results are summarised in the Table below. It should be noted
that if, for industrial production and producer price movements, rolling quarterly growth
ﬁgures were published instead of monthly variables, then the reported standard errors
would be likely to fall to 1/√3 of the values reported. On this basis it is likely that early
estimates of producer price movements would be suitable for publication.

Variable

Original Data Tramo-Seats Original Data Tramo-Seats

Forecast errors for nowcasting

RMSE (%)

Industrial production
GDP
Producer Prices

0.61
0.33
0.22

0.61
0.27
0.22

RMSE (%) of Rolling
Quarterly Estimates
0.35
0.35
-
-
0.12
0.12

Starting from institutional framework discussed above and in the context of the gen-
eral improvement of quarterly European accounts, Eurostat has recently developed a
methodology for the compilation of a ﬂash estimate of quarterly GDP for the euro-zone
and the European Union at 45 days after the end of the reference quarter. The Eurostat
project have greatly beneﬁted from the results of a project on ﬂash estimates of quar-
terly national accounts co-ordinated and supervised by Eurostat in the framework of the
European Union Fifth Research Programme. This project have analysed feasibility and
suitable methods to produce a ﬂash estimate and proposes a methodology for compiling
ﬂash estimates at the European level. In accordance with the results of this project, the
identiﬁcation of a suitable method for the estimation of the Eurozone and the European
Union GDP went through the following steps:

deﬁnition of the target variable;

analysis of the available information;

choice of a methodology of ﬂash estimates and model building;

•

•

•

nowcasting performance analysis.

•
An indirect approach to the compilation of the European GDP has been taken into con-
sideration using Member States data as a basis of constructing indicators. Nevertheless,
the direct approach compilation has also been analysed and the results evaluated.

The target variable for the ﬂash estimate is the quarterly growth rate on the previous
quarter of GDP for the euro-zone and the EU, seasonally adjusted, constant prices 1995,
as published by Eurostat. The choice of the growth rate on the previous quarter is in line
with the objective of describing short-term movements of the economy. Furthermore, the
quarter on quarter growth rate is the main indicator on which Eurostat’s news releases
focus.

Several diﬀerent sources of basic information are available within a reasonable delay

after the end of the reference quarter. The available information is made up by:

•

•

•

the ﬁgures compiled by the National Statistical Institutes in the context of quarterly
national accounts as far as they are available 40-45 days after the end of the quarter
(countries’ ﬂash estimates);

the ﬁgures estimated by other institutes like Central Banks, short-term analysis
institutes, etc. (but well recognised at the national level);

other basic statistics normally compiled by National Statistical Institutes and usu-
ally available at higher frequency than quarterly (for example, index of industrial
production, retail sales, prices, employment, external trade, etc.).

In accordance with the Eurostat methodology for the regular estimates of the European
quarterly national accounts aggregates and on the basis of the results coming from the
"Flash Project", ﬂash estimations at country level are considered the best indicator for
the nowcasting of the Eurozone and the European Union GDP. For this reason, in the
estimation process priority is given to countries ﬂash estimates. For those big Member
States that are not producing ﬂash GDP estimates, related indicators are used. Countries
ﬂash estimates and indicators are combined for estimating GDP in the context of a
regression model based approach (similar to the approach of the ﬁrst regular estimation).

The related indicators have been chosen following a step by step process: 1) A ﬁrst
list of indicators suitable to produce the ﬂash estimate has been identiﬁed by economic
reasoning, requiring that the indicators show a close correlation to the dependent variable;
2) Selection, inside the list, of the most suitable indicators on the basis of a bivariate
pairwise analysis of correlation with the target variable.

An analysis of the indicator correlation with GDP was the leading criterion in choosing
the indicators. The analysis was performed in order to identify those indicators most
suitable for the GDP ﬂash estimation of the European aggregates, as a complement of
the GDP ﬂash estimates available at 45 days at country level. A bivariate strategy has
been applied and indicators have been sorted according to their degree of correlation with
GDP.

As previously mentioned, the indirect method of estimation of the European aggregates
(i.e., using the sum of GDP of the countries as indicator) has been chosen in building the
ﬂash estimates. This was considered the most suitable choice, ensuring the possibility
of using all the available data transmitted by Member States and the methodological
coherence with the regular quarterly national accounts estimation procedure currently
used by Eurostat. Indeed ﬂash estimates for Germany, Greece, Italy, the Netherlands
and the United Kingdom are available within 45 days. For those major countries that do
not yet produce a GDP ﬂash estimate, suitable related indicators have been used. Such
indicators were required to satisfy the following criteria: 1) availability at 40-45 after
the end of the reference quarter; 2) economic meaningfulness with respect to quarterly
GDP; 3) oﬃcial availability: preference has been given to oﬃcial statistics; 4) statistical
correlation with the target variable.

The ﬁnal model was speciﬁed according to both statistical and economic considerations.
In particular the criteria applied were the following: a) statistical meaningfulness of the
model; b) simplicity of the model; c) possible economic interpretation of the model; d)
use of all the relevant available information; e) coherence with the methodology applied
for the ﬁrst regular estimate of Eurostat.

The selection process showed that the best model for the ﬂash estimation of the GDP
growth rate (constant prices and seasonally adjusted) for the Eurozone and the European
Union, compiled according to the indirect approach, is based on the available GDP ﬂash
estimates for Germany, Italy, United Kingdom, the Netherlands and Greece (constant
prices, seasonally adjusted) and other indicators, mainly industrial production indices
(seasonally adjusted), for France and Spain (GDP ﬂash estimates are not yet available).
In order to assess the performance of the model, a simulation under real conditions has
been done. The ﬂash estimate ﬁgures for GDP of Germany, Italy, the Netherlands and
the UK (and only recently Greece), usually available within 45 days from the end of the
reference quarter, together with the indicators for France and Spain have been used to
produce a ﬂash estimate of the European GDP under real conditions. The nowcasting
performance of the models has been tested by comparing the ﬂash estimation with the
‘traditional’ Eurostat estimations over nine quarters from 2000Q4 to 2002Q4, both for
the Eurozone and the European Union. Results are quite satisfactory: as far as quarterly
growth rates are concerned (Q/Q-1), the root mean squared error (RMSE) calculated by
comparing the ﬁrst Eurostat estimate (60-70 days) with the ﬂash estimate is 0.04% both
for the euro-zone and the European Union. The success rate in predicting an accelera-
tion/slowdown of the growth is satisfactory: 8 out of 9 cases for both the euro-zone and
the European Union (compared with the 60-70 days estimate). The forecasting perfor-
mance remains almost unchanged when results are compared with the second and third

release of Eurostat estimates (100 and 120 days). The model is also good in capturing
annual growth rates (Q/Q-4): the root mean squared error calculated with respect to the
ﬁrst Eurostat estimate is around 0.10 for both European aggregates and it becomes even
smaller with respect to the second and third estimates.
3.4 The analysis of revisions and revision policies

A well known feature of most economic data released by national and international
statistical agencies is that they often undergo a process of revision once timely but initial
ﬁgures have been published. For example, preliminary data can be based on an incomplete
and/or provisional set of information, which are successively and regularly updated as
new information become available (routine revisions). Furthermore, from time to time,
additional and more unusual revisions taking place at infrequent and irregular intervals
can also occur, such as those generated by changes in the methods, classiﬁcations and
sources used (occasional revisions).

The nature and extent of data revisions have been the object of a continuous interest
by the literature. Both economists and statisticians have focused their interest on the
impacts of the revision of data on relevant issues of econometric modelling, such as
parameter estimations, simulations and forecasts.

An important line of research considers revisions as generated by measurement errors,
whose statistical characteristics would be fruitfully analysed by statistical agencies to
reduce the eﬀects of subsequent revisions. In eﬀect, such studies have pointed out that
the analysis of revisions can provide a basis both for assessing the accuracy of provisional
in relations to ﬁnal estimates, and for improving the methods of estimation used to
compile provisional data.

In recent years, revisions have become a cause of major concern for a number of reasons.
Frequent and large-size data revisions can force economists, analysts and policy makers
to continuously update their analyses and, in some cases, revise their interpretation of
economic phenomena. Even ‘noisy’ changes can be considered as disturbing, particularly
if the revision process concerns a key ﬁgure on which policy decisions, funding, and other
assistances are based. In this respect, it is important to distinguish between statistical
revisions and suspect — perhaps politically motivated — revisions in the data provided.
For international statistical agencies, the problem of revision can be even of greater
In fact, data produced can be subject to revisions not only because of
importance.
changes in the basic data obtained from member countries, but also because some data
produced — such as aggregates — requires the availability of a complete information set.
In many situations, gaps are ﬁlled in by estimation techniques, which are subsequently
updated as the ‘true’ information become available.

National and international agencies have become more sensible to the problem of data
revisions, and some important steps have been made in the central ﬁeld of harmonising
the revision process. The identiﬁcation and acceptance of a common platform for revision
or, in other words, a common revision policy, is now considered to be at the centre of the
agenda of many meetings and working groups.

One of the main points of criticism of is the high frequency of revisions in the European
aggregates and the absence of a common European revision policy, namely a guidance
for governance in oﬃcial statistics at both the national and international level. In this
respect, the Short-Term Statistics Working Party of Eurostat has proposed a number of
guidelines for the adoption of a common revision policy, indicating which data can be
subject to revisions, when corrections should in principle be done, and procedures to be
followed for normal statistical data revisions.

The FROCH group has recently put the emphasis on the necessity to establish a com-
mon set-up for revisions of the PEEIs to be adhered to by Member States, and has
indicated the urgency of a joint document by Eurostat and the ECB containing, inter
alia, an overview of Member States revision practices and their calendars for the ﬁrst
release and subsequent revisions.

The Working Party on National Accounts of Eurostat has in the last months discussed
more times about the need of a more corporate strategy for revision policy of both annual
and quarterly national accounts. A questionnaire has been sent to Member States on
practices actually followed to identify diﬀerences and commonalities.

A similar questionnaire has been ﬁnalised by our unit and is to be circulated early
within Eurostat. The questionnaire aims at providing insights into the data revision
practices and policies followed not only in Member States but also in the various Sectoral
Units of Eurostat responsible for the collection of STS and the estimation of the European
aggregates for a number of key short-term economic statistics.

Time-series of revisions must be tested in order to discover if they show any systematic
behaviour. Eurostat is attaching a great importance to this type of analysis. In chapter
15 of the Handbook of Quarterly National Accounts (Eurostat 1999) a strategy for the
analysis of revisions is described and, as far as possible, applied in the context of the
Euroind data base daily updated by Eurostat. This strategy can be generalised to all
types of short-term series. The proposed approach derives from the studies of Patterson
(1992), Di Fonzo, Pisani and Savio (2002) and many others.

The analysis of revisions is structured into two steps: a) statistical analysis; b) econo-
metric analysis. Regarding the statistical analysis of revisions, summary statistics can be
used for various comparisons between preliminary and ﬁnal estimates. In the chapter 15
of the Handbook of quarterly national accounts (Eurostat 1999) two types of errors are
considered: 1) relative errors, giving information on the accuracy of preliminary estimates
of levels; 2) - absolute errors, used to evaluate the accuracy of preliminary growth rates.
Proposed indices of accuracy of the preliminary estimate of levels are relative measures,
such as Mean relative errors and Standard deviation of the relative errors, whilst in-
dices of the accuracy of the preliminary growth rates are, e.g., Mean errors and Standard
deviation of the absolute error.

Clearly these measures can not be considered as reliability measures of provisional
estimates. However they permit to evaluate if revisions are always (or almost always) of
the same sign. If it would be the case, a correction of preliminary estimates should be
needed.

Regarding the econometric analysis of revisions, econometric analyses and tests can
be conducted on both successive versions or vintages of data.
In our approach, it is
recommended to conduct the analysis on vintages for reasons linked to their greater
homogeneity. The last available vintage of data is assumed to be the ﬁnal one. Most
econometric techniques use tests borrowed by the rational expectation theory. In this
context preliminary estimates are considered as diﬀerent forecasts of the ﬁnal vintage,
conditional on the available information at the time they are made. Consequently, tests
of the rationality of the expectations can be used to asses the accuracy of provisional
estimates. The following test are proposed in our approach:

1) Test for lack of bias which means that the mean of revisions should be zero;
2) Test for weak eﬃciency which means that revisions should be unforecastable from
the information available at time t. This information comprises the preliminary vintage
itself and a constant term. Hence, eﬃcient use of this information would improve the

initial vintage as a predictor of the ﬁnal one. Weak eﬃciency is a suﬃcient but not a
necessary condition for unbiasedness.

3) Test of orthogonality which means that revisions should be unforecastable from all
the relevant variables available at time t. All the available information can include: past
history of the revisions process, the phase of business cycle, the dynamic of the inﬂation
process, the period in which the preliminary estimate is made, etc.

Wince orthogonality is a particular case of weak eﬃciency, it is the most restrictive
test proposed. These test permit to access the randomness of the revision process but
they can not be considered as exhaustive. In fact another relevant and advisable property
is the cointegration of successive vintages of data. The absence of cointegration means
that a non stationary variable (of a combination of these) has been omitted between the
preliminary and the successive vintage. This omission can be due to diﬀerent factors like:
measurement errors in the preliminary estimates, their accumulation over the time, use of
unrepresentative data, incomplete sampling, etc. This explains the importance of testing
the hypothesis that there exists a stationary linear combination of two vintages. At this
point it is important to clarify the relationship between cointegration and rationality.
The preliminary vintage could be a biased and ineﬃcient predictor of the ﬁnal one, but
the two could well be cointegrated. Thus, cointegration is necessary, but not suﬃcient,
for unbiasedness and eﬃciency. This is obvious because cointegration pertains with long-
term movements. Two series could be cointegrated but they could have diﬀerent business
cycle behaviours. This consideration leads as to the last important point of our scheme
for the analysis of revisions. We need to investigate the similarity of vintages in terms of
serial correlation common features (SCCF). Successive vintages will have SCCF if it is
possible to deﬁne a linear combination of their cycles which does not contain any cyclical
component. In other words we need to test if a linear combination of the ﬁrst diﬀerences
of vintages is a white noise.

4.

Improving the informative content of the data

Here we consider area on which Eurostat has dedicated eﬀorts in recent years and which
directly pertain to what can increase the informative content of the data released, most
notably in the ﬁeld of what is commonly named as ‘data presentation’. In this context,
we will refer to seasonal adjustment and business cycle estimation issues.

4.1

Seasonal adjustment

The ECB and Eurostat recently presented their recommendations on seasonal adjust-
ment (ECB, 2000 and Eurostat, 2000). The positions of the two institutes are very
similar. These points of view now has been put to the Member States, which as a rule
use diﬀerent methods, sometimes even within the same institute. Two problems should
be mentioned here:

•

Tramo-Seats and X12-Regarima are the software packages that are recommended in
the reports. Empirical studies have shown that the choice of one or other method is
not clear-cut and depends, amongst other things, on the seasonally-adjusted indica-
tor. Although Eurostat recommends using Tramo-Seats for the seasonal adjustment
of a large number of series, the performances of the two software products needs to
be evaluated and compared for the main macroeconomic indicators.

•

Seasonal adjustment of European series, calculated by aggregating the national se-
ries, can in theory be carried out in four diﬀerent ways: a) seasonal adjustment

of aggregated raw series (direct approach); b) aggregation of seasonally adjusted
national series (indirect approach); c) aggregation of national series seasonally ad-
justed by the Member States (mixed indirect approach); d) simultaneous seasonal
adjustment of national series (multivariate approach).

The diﬀerences in the four approaches, and in the derived components, correspond to
the diﬀerences in the information set which is considered in the information process (see
Campolongo and Planas, 2000, for a theoretical overview on this subject). Some empir-
ical studies have shown that diﬀerences in multivariate versus univariate approaches are
relatively small. The empirical results show that direct and indirect approaches produce
equivalent results only under very restrictive assumptions, i.e. when no trading day or
outlier correction is made, the decomposition is additive and no forecast are produced
(see Ladiray and Mazzi, 2003). In practice, such conditions are met rarely and the dif-
ferences in the series produced under the two rules can be signiﬁcant depending on the
series concerned. Some criteria to discriminate between direct and indirect approach can
be put forward : smoothness of seasonally adjusted series, minimisation of revision errors,
stability of seasonal component, quality assessment of seasonal adjustment, analysis of
the irregular component and out-of-sample forecast accuracy.

A comparison between the direct and the indirect approach has been presented by
Astolﬁ, Ladiray and Mazzi (2000) and Ladiray and Mazzi (2003) with reference to the
Eurozone GDP. The results obtained show that the direct approach produces smoother
ﬁgures than the indirect one. This comparative analysis will be extended in all relevant
areas to assess a common seasonal adjustment strategy for Eurozone ﬁgures. Among all
other theoretical considerations, the direct approach seems to be preferred, in particular
when estimation and nowcasting are carried out. The use of a direct approach implies the
lost of the additivity between national data and Eurozone totals. Users should be aware
of this situation. From the producers point of view, it is essential to verify continuously
that Eurozone seasonal adjusted ﬁgures, directly obtained, deliver the very same message.
Anyway, there is no clear choice and comparative studies still need to be done. Evalua-
tion criteria have been put forward: minimised revisions, smoothing of seasonally adjusted
series, stability of seasonal component, predictive power of seasonally adjusted series, etc..
Some countries favour the aggregation of national series that have been seasonally
adjusted by the Member States. The advantage of this method is that it retains the
additivity of seasonally adjusted series. But this is hampered by the fact that the Member
States generally do not use the same method: the seasonally adjusted series for Europe can
thus be tainted by these diﬀerent types of ﬁlter. It is highly recommended that Member
States could move towards an harmonisation of seasonal adjustment practices. These
should include not only the convergence of the methods but also several other aspects,
such as: trading day correction, revisions, consistency between seasonal adjusted and not
seasonal adjusted totals, etc.

Another important line of research is the seasonal adjustment of qualitative surveys col-
lected by the DG ECFIN within the framework of the Joint Harmonised EU Programme
In a recent project, conducted within a working
of Business and Consumer Surveys.
group composed of representatives from the Commission (DG ECFIN, Eurostat), the
ECB and the OECD and related to an assessment of seasonal adjustment methods for
business and consumer surveys, Eurostat has focused its attention on three important
aspects, namely: a) whether deterministic/stochastic seasonal components have to be
considered as common features of the time behaviour of these surveys; b) whether an
indirect or a direct approach for seasonal adjustment should be preferred when the series

contains a seasonal component; c) whether signiﬁcant diﬀerences arise in the outcomes
when diﬀerent seasonal adjustment procedures are chosen (see Mazzi, Savio and Soares,
2003).

Seasonally adjusted data for balances can be obtained by following two main ap-
proaches. The ﬁrst (direct approach) is at present used by the DG ECFIN and consists
in obtaining ﬁrst the balances in raw form, and then in applying the seasonal adjustment
procedure to each country. Aggregates for Eurozone and European Union are than ob-
tained through a weighting procedure. On the contrary, the indirect approach derives the
seasonally adjusted balances - for example in case of ’three answer’ surveys - from the
diﬀerence between seasonally adjusted positive and negative answers. In the last case,
the sum of the seasonally adjusted answers is likely to be not equal to 100. Then, for
example, the ‘plus’, ‘equal’ and ‘minus’ signs should be constrained to be equal to the
accounting identity before being used for any successive analysis.

With reference to the ﬁrst point, it can be noticed that though the respondents are
explicitly asked to abstract from seasonal movements in forming their judgements, a well
known common feature of surveys series is the presence of a seasonal component.

This evidence has been advocated in support of the notion that seasonal ﬂuctuations are
not independent of the trend and cyclical parts of the time series (see Franses 1996). The
seasonal dynamics in the qualitative surveys reﬂect the seasonality in the quantitative
series they should mimic (for example, orders, turnover and production) even though
seasonality should be, broadly speaking, weaker.

Another important point to be raised is that business surveys are, by construction,
bounded series. Then, the variances associated to the trend and seasonal components
should not, in principle, go to inﬁnity, thus eliminating a source for stochastic movements
in these components. Anyway, what we are really covering in most situations is a small
piece of the underlying true stochastic process. Then, the question naturally arises of to
what extent we can describe the surveys as being characterised by stochastic trend and
seasonal movements. In this respect, we have conducted preliminary analyses on the level
series by performing the extension to the monthly case of the test originally suggested in
a quarterly context by Hylleberg et al. (1990), namely the testing procedure developed
by Beaulieu and Miron (1993). The results obtained by applying these test to a number
of monthly series show that the data fail to reject in 68.6% of the cases a unit root at the
zero frequency, and in almost all the series we fail to reject at the 5% level a root at some
of the seasonal frequencies. Therefore, although the presence of seasonal roots seems to
be stronger than that of a root at the zero frequency, the application of the seasonal
operator
12 seems to be in almost all cases not completely justiﬁed. Furthermore, this
result seems to seriously question the rightness of the use of the so-called airline model
for the seasonal adjustment of qualitative surveys.

Concerning the other two questions, it has been found that the use of a direct approach
seems to give quite reliable results in terms of stability of estimates, smoothness, con-
cordance of components and data revisions. Of the two software for seasonal adjustment
analysed, the Tramo-Seats package gives, on average, more reliable results in terms of
roughness, concordance and quality of the seasonal adjustment, while the X12-Regarima
performs better in terms of data revisions. The information lost due to aggregation fol-
lowing the direct approach is, in most cases, negligible. That is likely to be due to the
similarities of the seasonal components among the diﬀerent answers given by respondents.
Given the results obtained, the use of an indirect approach to seasonal adjustment can
be equally followed. In that case, the greater information given to users has to be bal-

anced with the necessity to fulﬁll the usual aggregation constraints. These can be easily
introduced into the estimates by using standard multivariate disaggregation techniques
well know in the literature.

Another important line of research is the role of seasonal adjustment in the measure-
ment of business cycle both from the classical and the deviation cycle perspective. For
the former, one could aim at evaluating the uncertainty in the detection of turning points
arising from the application of diﬀerent seasonal adjustment techniques and, possibly, dif-
ferent aggregation procedures. For the latter one investigates ways of assessing how and
how much diﬀerent seasonal adjustments aﬀect the level (reliability in terms of revision
errors) of output gap estimates.

Preliminary results obtained within studies conducted by the Eurotrend project seem
to conﬁrm that, regarding the Eurozone quarterly GDP spanning the sample from 1980
to 2003, the impact of seasonal adjustment can be substantial in the identiﬁcation of
turning points, as evidenced in the Table reported below. In particular, a complete cycle
seems to be missing if one uses Tramo-Seats as seasonal adjustment package instead of the
X12-Regarima software, and some turning points are shifted using the indirect instead of
the direct seasonal adjustment aggregation scheme.

Turning points in the quarterly GDP for
the Euroarea, classical cycle

Tramo-Seats

X12-Regarima

Direct
Through 1981q1
1992q3
Through 1993q1

Peak

Peak
Through

-
-

Indirect Direct
1981q1
1981q1
1992q1
1992q1
1993q1
1993q1
2001q1
-
2001q3
-

Indirect
1981q1
1992q1
1993q1
2001q1
2001q4

Another important aspect of seasonal adjustment, namely the performance and the
quality of the diﬀerent adjusting procedures when the series span a short time period,
is at the core of another project developed by Eurostat.
In eﬀect, most of the ﬁlters
actually used for seasonal adjustment are asymmetric. Under these circumstances, quality
statistics tend to be ﬂawed by the use of too few observations, i.e. revisions are bigger
when new data are progressively added to the time series, the presence of outliers is
likely to greatly worsen the overall performance of the econometric models, etc.. In this
respect it is widely recognised that seasonal adjustment should be performed using a
sample period of at least 5 years. Unfortunately, that is not always the case in current
practices. In the context of the statistical activities carried out by Eurostat, in a number
of situations the infra-annual statistics for the most scrutinised aggregates by users and
policy makers — i.e. the EU and Euroarea aggregates — seldom span a sample of more than
10 years. That situation can arise for a number of reasons, amongst them we cite breaks
in the component (country-speciﬁc) series, changes in the base year for constant price
evaluations, changes in the deﬁnitions, sources and methods used for the compilation of
the micro-aggregates. In the next future another important element will have an impact
on the problem here at hand, namely the enlargement of the EU for the entrance of the
new 10 Acceding Countries.

This project tries to shed some lights on the issue of the dependency of the results
of seasonal adjustment on the length of infra-annual time series. The issue seasonal

adjustment/length of time series is studied with reference to both real (external trade
statistics, national accounts, industrial production indices) and simulated data sets. The
performances of the two programs Tramo/Seats and X12-Regarima have been analysed
and compared by considering appropriate quality statistics indicators, such as revision
errors and sliding spans, residual whiteness, M-statistics, stability of the ﬁnal models and
results.

Our preliminary conclusions are that both programs suﬀer for a deterioration of qual-
ity indicators for the seasonal adjustment of progressively shorter time series, but that
this deterioration is more pervasive with the Tramo-Seats program rather than with
X12-Regarima program. A theoretical explanation of this evidence could be found in
the fact that model-based approaches, such as Tramo-Seats, suﬀers of model instability,
which worsen revision errors and general indicators of performance of the locally preferred
model.

4.2 Business cycle estimation, dating and detection of turning points

The business cycle has an importance in the popular debate which can tend to run
ahead of the problems in measuring it. For example, before discussing whether there
is a European business cycle, or whether output is above or below its trend, we must
confront the logically prior step of the appropriate way of measuring the business cycle
or, equivalently, the trend level of output.

Business cycle analysis has thus developed into a prominent sub-discipline and, accord-
ingly, there is a vast literature on the subject. Among the variety of diﬀerent questions
that have been treated in this context two important ones stand out, namely how a cy-
cle should be extracted from a given data series and, more generally, how the turning
points of the cycle should be identiﬁed. Many diﬀerent measures of the business cycle
have been proposed in the literature. Indeed there is a fundamental dichotomy between
so-called “growth” and “classical” business cycles. This distinction reﬂects the funda-
mental dichotomy between so-called “growth” and “classical” business cycles. Eurostat,
in conjunction with the NIESR, recently turned its attention to two questions: a) Is
there a European business cycle, and how do national business cycles relate to this? b)
How should we measure the ‘output-gap’, i.e. the diﬀerence between actual and potential
output?

Synchronisation of business cycles In this project an eclectic approach has been taken
within the current debate considering a range of diﬀerent measures of the business cycle.
These measures are assumed to be complementary, in the sense that they may all provide
relevant information about the state of the business cycle. A comparison has been carried
out among the complementary measures of the business cycle in an empirical application
to the Eurozone business cycle. It is important to reconsider the question of Eurozone
convergence as the literature has not yet reached a consensus on whether Eurozone busi-
ness cycles have converged. Diﬀerences are explained in part by the use of diﬀerent data.
Other reasons, however, include the use of diﬀerent methods of both identifying business
cycles and gauging convergence.

The starting point is the controversy initiated by Artis and Zhang (1997, 1999) and
Inklaar and de Haan (2001). While Artis and Zhang (1997, 1999) conclude that Euro-
pean business cycles have become more synchronised, Inklaar and de Haan (2001), using
the same but updated data, reach the opposite conclusion. The evidence that sparked
this controversy has been re-considered and in so doing some important stylised facts
about the nature of the Eurozone business cycle have been assessed. The work has been

characterised by the following ﬁve developments.

(a) Using the same raw data as Artis-Zhang and Inklaar-de Haan, again appropriately
updated, one can identify business cycles using a range of trend-cycle decompositions, as
well as by parametric and nonparametric turning point rules. This is in contrast to the
selective de-trending methods considered by Artis-Zhang and Inklaar-de Haan. This lets
us ascertain whether inference on convergence is contingent on the measure of the cycle.
(b) In order to test whether Eurozone business cycles actually have converged, and
there is evidence for a common cycle, the distribution of bivariate correlation coeﬃcients
between the 12 countries’ business cycles has been analysed. This extends previous
work that has tested for convergence, in a similar manner by focusing on correlation,
but has not considered the entire distribution, instead focusing on the mean correlation
coeﬃcient or particular bivariate correlation coeﬃcients. The distribution based measure
of convergence is related to the economic growth literature, speciﬁcally the concepts of β
and σ convergence; see Quah (1993, 1996).

(c) Since one can imagine the situation where Eurozone business cycles, for example, are
uncorrelated but are moving ‘closer’ together, due to less pronounced cyclical volatility,
the further step consisted in the examination of whether Eurozone business cycles have
moved closer together over time. Speciﬁcally, a measure of closeness the root mean
squared diﬀerence between the Eurozone growth business cycles is proposed, expressed
as a percentage of potential or trend output.

(d) The evolution of the estimates over time using a series of rolling windows has been
considered, rather than just two or four windows of ﬁxed width as in Artis-Zhang and
Inklaar-de Haan.

(e) A measure of uncertainty associated with our estimates has been provided by esti-

mating the correlation coeﬃcients using a generalised method of moments estimator.

The empirical ﬁndings about the Eurozone are that, although empirical inference about
individual Eurozone business cycles is found to be sensitive to the measure of the business
cycle considered, the proposed measure of convergence between the Eurozone business
cycles exhibits common features across the alternative measures of the business cycle.
Interestingly, it is found that there have been periods of convergence, identiﬁed by the
distribution tending to unity, and periods of divergence. Although further data are re-
quired to corroborate the story, there is evidence to suggest that the Eurozone has entered
a period of convergence after the clear period of divergence in the early 1990s in the af-
termath of German uniﬁcation and at the time of the currency crises in Europe. This is
encouraging for the successful operation of a common monetary policy in the Eurozone.
In a rather diﬀerent approach, a similar topic has been addressed by Ladiray and Mazzi
(2001), Ladiray and Soares (2001), and Astolﬁ and Mazzi (2002). The results obtained
in all these three studies using Business and consumer surveys, GDP and Industrial
production index, seem to conﬁrm that convergency is improving in the last periods.

In another recent study on business cycles in the Eurozone (Ladiray and Mazzi, 2001),
the authors have found a strong evidence of cyclical synchronisation in the Eurozone,
with ten countries out of twelve presenting a correlation greater than 0.9 with the overall
cycle. Further, a cluster analysis on the 13 estimated cycles has revealed 4 diﬀerent
groups, two of these for Finland and Ireland separately, and two groups for the remaining
countries, the ﬁrst composed by France, Greece, Italy and Luxembourg, the second one
by Austria, Belgium, Germany, Spain, the Netherlands and Portugal.
Output gap measurement and turning points chronology Regarding the second question,
namely how should we measure the ‘output-gap’, i.e. the diﬀerence between actual and

potential output, both multivariate and univariate measures of the output gap have been
considered. It is widely recognised that multivariate estimators oﬀer a more economic
interpretation to the output gap as they essentially combine the estimators of the growth
cycle with additional economic information. The Phillips Curve, for example, suggests
that inﬂation data contain information about the output gap while Okun’s Law suggests
unemployment is important. These economic variables may contain useful information
about the supply side of the economy and the stage of the business cycle. Three multivari-
ate models have been analysed: (i) unobserved components models, (ii) Hodrick-Prescott
models, and (iii) structural VAR models. A simulated out-of-sample experiment has been
used in this context to analyse the real-time performance of these alternative output gap
estimators, as well as some representative univariate estimators. The real-time behaviour
of output gap estimates is of importance given that policy-makers require estimates in
real-time. Across a range of widely used univariate and multivariate estimators of the
output gap we we have found signiﬁcant diﬀerences between real-time and ﬁnal output
gap estimates for the Eurozone - real-time estimates are unreliable. As the future be-
comes the present, output gap estimates are revised. It is not just in the US that real-time
measurement of the output gap is diﬃcult. Nevertheless, the change from univariate to
multivariate measures of the output gap does lead to real time estimates better correlated
with the ﬁnal estimates. Adding ‘economic information’ appears to help. The correlation
of two univariate and ﬁve multivariate estimators of the output gap computed in real-
time against the ﬁnal estimates is summarised in the Table below. Note that even for a
given measure the correlation is sensitive to the speciﬁcation chosen. This is illustrated
for the SVAR with no cointegration where two numbers are reported, the ﬁrst for a VAR
with lag order one, the second for a higher order VAR: SVAR estimates of the output
gap are sensitive to the lag order chosen. An important ﬁnding is that a low lag order,
as is typically selected by information criteria such as the BIC, can lead to implausible
looking output gap estimates. Given the unreliability of real-time output gap estimates
for the Eurozone, this begs the question, should we be surprised by this unreliability? As
forecasters know, the fact that forecasts are wrong does not mean they are misleading
or useless. If within the bounds of what was expected the forecast can remain useful,
similarly for output gap estimates which we view analogously to a forecast.

To capture fully the uncertainty associated with the real-time estimates, or forecasts,
of the output gap, density forecasts have been constructed. Density forecasts of the
realisation of a random variable at some future point in time provide an estimate of the
probability distribution of the possible future values of that variable. Two approaches
have been used to construct density estimates of the output gap in real time. The
ﬁrst relies on a state-space representation for the output gap estimator while the second
quantiﬁes the degree of uncertainty through forecasting. One important ﬁnding is that the
real-time measures of uncertainty do not prove to oﬀer reliable indications of the degree
of uncertainty associated with real-time estimates. This provides a serious challenge to
users of output gap estimates.

Finally, given that output gap estimates are frequently used to forecast inﬂation, one
could examine whether the unreliability of output gap estimates in real-time impacts
upon the quality of forecasts. In this respect, it has been found that, although real-time
output gap estimates often have little forecasting power over inﬂation relative to simple
autoregressive alternatives, this does not appear to be due to the unreliability of output
gap estimates but rather the diﬃculties of forecasting inﬂation per se.

The correlation of real-time estimates with ﬁnal estimates

Model

Hodrick-Prescott
Univariate unobserved components
Bivariate unobserved components
Bivariate Hodrick-Prescott
Trivariate unobserved components
SVAR - no cointegration
SVAR - cointegration

Correlation
0.273
0.179
0.457
0.688
0.455
0.901 and 0.153
0.575

An application of the multivariate Hodrick-Prescott (HPMV) ﬁlter to the estimation
of the Euro area output gap has been carried out in a joint project with the OFCE.
The analysis investigates the impact of using alternative economic relationships on the
output gap estimates of the Euroarea. The comparison with the univariate HP ﬁlter
have shown that this can signiﬁcantly modify the appreciation of the output gap level of
the Euroarea in some speciﬁc periods. The study proposes also to estimate the HPMV
models with the methodology adopted for the estimation of state-space models. This
strategy provides an alternative to the calibration of the parameters and allows to assess
the reliability of the HPMV output gap estimates. Estimated weights associated to the
economic relationships in the optimisation program of the HPMV are generally coherent
with the calibrated values usually retained in the literature. The assessment of the
reliability of the alternative output gaps and of the revision properties have shown a
substantial superiority of some HPMV models over the univariate HP ﬁlter. Another
important ﬁnding of this project is that the integration of macro-economic information
generally improves the accuracy of the inﬂation forecasts.

In another project with OFCE, an analytical framework for the estimation of poten-
tial output and output gaps for the Euroarea is used by combining multivariate ﬁlter-
ing techniques with the production function approach. The potential advantage of this
methodology consists in combining a model-based approach to estimate potential output
with explicit statistical assumptions concerning the estimation of the potential values of
the components of the production function.

The need for a cycle turning point chronology is now widely recognised by experts
and practitioners of economic analysis. As an example of application, it may help to
compare the cycles among countries or to point out the links between the cycles and
diverse macroeconomic aggregates. However, it turns out that the most important use of
the turning point chronology consists in establishing a reference cycle dating for a given
country or an economic area. Regarding the US, the NBER’s Business Cycle Dating
Committee is widely recognised as the authority for determining peaks and troughs in
the classical business cycle. In other countries, in our context in the Eurozone, there is no
oﬃcial dating of business cycles and therefore reference dates, which could be considered
as a benchmark, are not available for theoretical and empirical studies.

The deﬁnition of a turning point chronology is now a central theme for Eurostat, which
started some years ago a project with GRETA ASSOCIATI and COE aimed at identifying
turning points for a number of key indicators of economic activity.

During the project a comparison has been made among diﬀerent methods for identiﬁ-
cation of turning points, namely univariate versus multivariate methods and parametric
versus non parametric approaches. Further, the eﬀects of aggregation and seasonal ad-
justment and the detrending methods used have been studied in great detail by applying

the various procedures to the Industrial Production, GDP and Employment data. The
ﬁnal chronology, extended to both classical and growth cycles, is synthesized for the
Eurozone in the following Table.

Turning points chronology

Phase

Industrial production

GDP

Class. cycle (M) Growth cycle (M) Class. cycle (Q) Growth cycle (Q)

Trough
Peak
Trough
Peak
Trough
Peak
Trough
Peak
Peak
Trough
Trough
Peak
Trough
Trough
Peak
Peak
Trough
Peak
Trough
Peak
Trough
Peak
Trough
Peak
Trough
Peak
Trough
Peak
Trough
Trough

1974:04
1975:05

1980:02
1981:01
1981:10

1971:11
1974:01
1975:06
1976:11
1978:03
1980:02
1981:01
1981:10

1982:12

1982:12

1982:02

1983:02

1985:11

1987:10

1981:01
1981:04

1982:04

1985:04

1987:02

1992:01

1992:01

1992:01

1992:01

1993:05

1993:06

1993:01

1993:03

1995:02

1996:10
1998:02
1999:04
2000:11

2002:02

2000:12
2001:12

1995:01

1996:04
1998:01
1999:01
2000:03

5. Conclusions

The availability of good quality Eurozone statistics is essential for the co-ordination of
economic policies, the assessment of convergence and the conduct of monetary and ﬁscal
policies. This particularly applies to infra-annual macro-economic statistics, which repre-
sent a key instrument for business cycle analyses and short-term economic interventions.
Over the last years, the Commission, the Council, the ECB and the ESS as a whole have
made substantial improvements to the quality of statistics, notably with respect to the
comparability, coverage, timeliness and coherence.

Notwithstanding these eﬀorts, the limitations of the current procedures for calculating
European aggregates are evident, especially where data freshness and revisions are con-
cerned. There is a risk that the imminent enlargement of the European Union and the

Eurozone could aggravate these problems. The use of econometric models in response to
economists’ needs for business cycle analyses oﬀers an interesting and promising alterna-
tive.

The quality of the Euroind database has shown a marked improvement thanks to the
help of many users and researchers. At the moment, tremendous eﬀorts are being made
to cut the time it takes to supply national and European data. The Eurotrend project is
still in its start-up phase, but concrete results have yet been achieved and now wait for
a routine application to everyday statistical life.

Undoubtedly, one of its tasks will be to inculcate a new sense of short-term analysis
within Eurostat and to promote at the international level researches and activities on
infra-annual statistics. The organisation of an annual Colloquium on business cycle is
just posed in this context. Several studies have been launched in various relevant ﬁelds.
Eurostat plans to make rapid progresses on the various subjects referred to in this paper,
and to do so with an open mind and with a genuine desire for transparency.

Several of the results presented in the paper, such as back recalculation and ﬂash
estimates as well as estimates if high frequency data, could become with a short delay an
integral part of the Eurostat production system and of its dissemination portal for infra-
annual statistics, the Euroindicators website. Such results need to be oﬃcially approved
by Eurostat before being disseminated.

Other results, such as estimation of output gaps and the turning points chronology,
can represent a very useful starting point in an open and constructive discussion with
our economic partners, such as DG ECFIN and ECB. Their dissemination could be also
envisaged in the future, but for it we need not only the agreement of Eurostat but also
the agreement of the above mentioned partners.

Several projects need to be further developed, namely the deﬁnition of harmonised
policies for seasonal adjustment, revisions and estimation techniques for the aggregates.
More sophisticated researches - estimation of multivariate leading indicators for the most
relevant real economic variables, construction of a multivariate chronology of turning
points, real-time detection of turning points - need further developments. The extension
of convergence and synchronization studies also represents an activity to which Eurostat
is giving high priority. Most of the tools developed in the context of the Eurotrend
project could be fruitfully used by Member States to improve their statistical production
and to enhance the informative content of short-term statistics.
In this perspective,
Eurostat is going to elaborate several tool boxes, freely available for oﬃcial statisticians
and researchers, in important area of business cycle research.

Users will have a major role to play in improving our European system of infra-annual
indicators. The success of all these approaches will depend to a large extent on the
full cooperation and active participation of the Member States, research centers and
In the medium-long term period, for the European Statistical System to
academics.
become a centre of excellence a quantum leap is needed.

This will be best achieved, inter alia, through implementing the First for Europe Prin-
ciple with Member States focusing on the provision of the information required for com-
piling timely and high quality European statistics.

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