A ‘new modesty’? : level shifts in survey data and the decreasing trend of ‘normal’ growth.
Date: 2018-10-18
Institutions: Directorate-General for Economic and Financial Affairs
Subjects: consumer survey, data collection, economic analysis, economic cycle, economic fluctuation, economic growth, economic model, economic recession, economic survey, euro area, long-term forecast

6

ISSN 2443-8022 (online)

A ‘New Modesty’?
Level Shifts 
in Survey Data 
and the Decreasing Trend 
of ‘Normal’ Growth

Christian Gayer and Bertrand Marc

DISCUSSION PAPER 083 | JULY 2018

EUROPEAN ECONOMY

EUROPEAN 
ECONOMY

Economic and 
Financial Affairs

European  Economy  Discussion  Papers  are  written  by  the  staff  of  the  European  Commission’s 
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European Commission 
Directorate-General for Economic and Financial Affairs 

A ‘New Modesty’? 
Level Shifts in Survey Data                                                         
and the Decreasing Trend of ‘Normal’ Growth 

Christian Gayer and Bertrand Marc 

Abstract 

While the current level of the Economic Sentiment Indicator, which is well above its long-term average, is 
compatible  with  expanding  economic  activity,  it  has  been  associated  with  lower  growth  rates  than  those 
implied in the pre-recession period. Departing from the idea that the relationship between qualitative survey 
(‘soft’)  and  quantitative  (‘hard’)  data  might  have  changed  during  the  Great  Recession  due  to  a  'new 
modesty'  of  survey  respondents,  this  paper  goes  one  step  further  and  examines  to  what  extent  this  link 
might be constantly moving over longer time periods. Using rolling regressions and time-varying parameter 
models for the euro area, the paper shows that the growth rates typically implied by given survey results did 
not only fall during the Great recession, but already decreased rather systematically  for close to 20 years 
before the crisis, i.e. since around 1990. This is true for the overall economy (measured by GDP growth), 
but  also  industrial  production,  construction  production,  and  households  consumption.  Country-specific 
results for the GDP of the four largest euro-area economies are also presented. 

Furthermore, the paper suggests that business and consumer survey results can be used, beyond their usual 
short-term  forecasting  purposes,  to  gauge  changes  in  long-term  or  potential  growth.  While  the  results 
should  be  interpreted  as  preliminary  and  experimental,  they  might  be  useful  as  a  contribution  to  the 
assessment  of  post-crisis  potential  growth,  not  least  since  they  are  less  subject  to  revisions  than 
conventional methods based on production functions. 

JEL Classification: C32, E32, O47, O52. 

Keywords: business and consumer surveys, euro area, potential output. 

Acknowledgements:  The  authors  would  like  to  thank  participants  of  the CIRET  Workshop  in  Vienna 
(October 2017), the WGGES meeting in Ljubljana (October 2017) and the Joint EC/OECD Workshop on 
Recent Developments in Business and Consumer Surveys in Brussels (November 2017) for their comments 
and useful suggestions on previous versions. The authors are also grateful to Guillaume Cléaud and Atanas 
Hristov for their thorough reviews and useful discussions. 

Contact:  Christian  Gayer,  European  Commission,  Directorate-General  for  Economic  and  Financial 
Affairs,  christian.gayer@ec.europa.eu;  Bertrand  Marc,  European  Commission,  Directorate-General  for 
Economic and Financial Affairs and INSEE, bertrand.marc@ec.europa.eu.  

EUROPEAN ECONOMY                                                                                      Discussion Paper 083 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
CONTENTS 

1. 

Introduction                                                                                                                                  5 

2. 

A pre-/post-crisis break in the link between soft and hard data?                                         5 

2.1. 

The 'new modesty' hypothesis 

2.2. 

Empirical evidence of a pre-/post-crisis break 

5 

6 

3. 

Long-term shifts in the relationship between hard and survey data: How strong is 
economic growth when survey indicators are at their historical average?                        8 

3.1. 

Euro area results for total economy 

3.2. 

Sectoral driving forces (euro area) 

3.3.  Decomposition into 'assessment' and 'change' questions 

3.4.  Country results for the largest economies of the euro area 

8 

10 

13 

13 

4. 

An alternative measure of potential GDP growth                                                                 15 

5. 

Conclusions                                                                                                                                18 

LIST OF TABLES 

2.1.   Estimated parameters depending on the estimation period                                                          8 

LIST OF GRAPHS 

2.1.   Euro area GDP growth (yoy % changes), nowcasted with the Economic sentiment indicator         6 

2.2.   Euro area private consumption (yoy % changes), nowcasted with the consumer confidence 

indicator                                                                                                                                                        6 

2.3.   Euro area value added in services (yoy % changes), nowcasted with the services confidence 

indicator                                                                                                                                                        7 

2.4.   Euro area industrial production (yoy % changes), nowcasted with the industry confidence 

indicator                                                                                                                                                        7 

3.1.   Euro area GDP annual growth corresponding to the ESI's long-term average                                    9 

3.2.   Euro area GDP annual growth corresponding to the industry confidence indicator's long term 

average                                                                                                                                                        9 

3 

 
 
 
 
 
3.3.   Euro area IP annual growth corresponding to the industry confidence indicator's long-term 

average                                                                                                                                                      11 

3.4.   Euro area construction annual growth corresponding to the construction confidence indicator's 

long-term average                                                                                                                                    11 

3.5.   Euro area retail trade annual growth corresponding to the retail trade confidence indicator's 

long-term average                                                                                                                                    12 

3.6.   Euro area private consumption annual growth corresponding to the consumer confidence's long-
term average                                                                                                                                             12 

3.7.   Euro area value added in services annual growth corresponding to the services confidence 

indicator's long-term average                                                                                                                  12 

3.8.   Euro area GDP annual growth corresponding to the ESI's long-term average                                  13 

3.9.   Germany GDP annual growth corresponding to the ESI's long-term average                                  14 

3.10.   France GDP annual growth corresponding to the ESI's long-term average                                       14 

3.11.   Italy GDP annual growth corresponding to the ESI's long-term average                                            15 

3.12.   Spain GDP annual growth corresponding to the ESI's long-term average                                         15 

4.1.  

'Normal' euro area GDP growth and potential growth                                                                         16 

4.2.  

'Normal' Germany GDP growth and potential growth                                                                          17 

4.3.  

'Normal' France GDP growth and potential growth                                                                              17 

4.4.  

'Normal' Italy GDP growth and potential growth                                                                                   18 

4.5.  

'Normal' Spain GDP growth and potential growth                                                                                 18 

REFERENCES 

ANNEX I 

ANNEX II 

ANNEX III 

4 

 
 
 
 
 
1. 

INTRODUCTION 

The experience of the double-dip recession in the euro area in 2008-09 and in 2011-13 and the ensuing 
slow  recovery  may  arguably  have  led  consumers  and  business  managers  to  adjust  their  economic 
expectations to a more modest ‘new normal’. Analytically, this would imply a pre-/post-crisis break in 
the link between qualitative survey (‘soft’) and quantitative (‘hard’) data, which has traditionally been 
regarded as remarkably stable. Using pre-crisis relationships between survey indicators and underlying 
real variables could then be misleading. This paper briefly reviews to what extent survey data for the 
euro  area  lends  support  to  the  hypothesis  of  a  ‘new  modesty’  (section  2).  Using  time-varying 
regressions  as  in  European  Commission  (2011),  it  then  adopts  a  longer-term  perspective  and  finds 
evidence of a secular process of given survey levels being associated with lower growth rates, which 
started  already  long  before  the  Great  Recession  (section  3).  This  analysis  is  carried  out  both  at  the 
aggregate euro area level as well as at the sectoral and country levels. Taking the long-term averages 
of economic confidence indicators as a neutral reference point, we then use our methodological set up 
to contribute to the discussion of developments in underlying (trend or potential) growth, as perceived 
by survey respondents (section 4). We investigate how the observed secular process may be connected 
with  the  decline  in  potential  output  growth  as  measured  by  a  production  function.  It  is  shown  that 
business and consumer survey results can be used, beyond their usual short-term forecasting purposes, 
to gauge changes in long-term or potential growth. 

2.  A PRE-/POST-CRISIS BREAK IN THE LINK BETWEEN SOFT 

AND HARD DATA? 

2.1. 

THE 'NEW MODESTY' HYPOTHESIS 

Two major arguments can be brought forward to explain the ‘new modesty’ hypothesis for survey data 
(see European Commission, 2016). The first one is a technical argument related to the sampling of the 
surveys:  for  business  surveys  it  explains  a  positive  bias  in  the  results  by  the  assumption  that  the 
businesses  that  survive  the  crises  and  keep  answering  the  surveys  are  doing  better  than  the  others. 
Against this hypothesis, it can be argued that such a bias would equally apply to hard data, which are 
also  based  on  sampling.  Moreover,  there  is  no  empirical  evidence  to  support  the  hypothesis  that  a 
selection effect occurred during the crisis period (see Bruno et al., 2016). 

The  second  argument  is  of  a  psychological  or  cognitive  nature:  respondents’  answers  to  survey 
questions are relative to a ‘normal’ benchmark, or 'sufficient' level of aspiration. Under the impression 
of the severe and tenacious recession period, economic agents may have adjusted, i.e. lowered, their 
underlying  reference  standard  to  a  lower  level  of  economic  performance.  For  instance,  the  reference 
for businesses when assessing their situation is generally what they plan to produce. In this case, their 
views  on  a  ‘normal’  situation  are  necessarily  subject  to  change  when  production  adapts  to  lower 
demand. 

5 

 
 
2.2. 

EMPIRICAL EVIDENCE OF A PRE-/POST-CRISIS BREAK 

A straightforward way to check whether at the aggregate level the relationship between euro area soft 
and hard data has changed over time is to model year-on-year (yoy) GDP growth using the European 
Commission’s  Economic  Sentiment  Indicator  (ESI),  which  is  calculated  to  track  overall  economic 
activity, as an explanatory variable.1 The linear model chosen is simple and widely used: it includes 
the level of the survey and its first differe

nce.2

(cid:1851)(cid:1867)(cid:1851)((cid:1833)(cid:1830)(cid:1842))(cid:3047) = (cid:1854)(cid:2868) + (cid:1854)(cid:2869). (cid:1831)(cid:1845)(cid:1835)(cid:3047) + (cid:1854)(cid:2870). Δ(cid:1831)(cid:1845)I(cid:2930) + (cid:1873)(cid:3047) 

By estimating this model twice, once over a pre-crisis sample (from Q2-1985 to Q2-2007), and once 
over a post-crisis3 sample (from Q3-2009), it is possible to compute the GDP growth corresponding to 
a  given  level  of  the  ESI  in  a  pre-crisis  versus  a  post-crisis  world.  Graph  2.1  illustrates  that  the 
relationship  between  GDP  growth  and  the  ESI  has  changed  in  the  euro  area  around  the  2008-2009 
recession: GDP growth implied by pre-crisis coefficients is consistently higher than what is suggested 
by a post-crisis model. While the predictions based on the pre-crisis coefficients were very close to the 
actual  GDP  growth  outcomes  in  the  early  part  of  the  sample,  the  predictions  are  significantly 
overestimating actual growth after the crisis. 

Graph 2.1. Euro area GDP growth (yoy % changes), 
nowcasted with the Economic sentiment indicator 

Graph 2.2. Euro area private consumption (yoy 
% changes), nowcasted with the consumer 
confidence indicator 

Beyond  these  aggregate  results  for  the  euro  area,  there  is  evidence  of  similar  shifts  for  individual 
countries, sectors, and specific survey questions. In terms of sectors, results in European Commission 

1 The ESI summarises developments in five sectors (industry, services, retail trade, construction, and among consumers), see 
the  BCS  User  Guide  on  https://ec.europa.eu/info/business-economy-euro/indicators-statistics/economic-databases/business-
and-consumer-surveys_en for details. The ESI closely tracks euro area year-on-year GDP growth, with a correlation above 
90%. 

2 This model has been used for instance in Rioust De Largentaye and Roucher (2015) or European Commission (2011). For a 
review  of  the  use  of  bridge  models,  see  Ferrara  et  al.  (2010).  While  stability  tests  suggest  that  the  Economic  sentiment 
indicator has a constant mean, this is not the case for euro area GDP growth. This fact by itself supports the idea of a break in 
the  relationship  between  soft  and  hard  data.  As  a  consequence,  the  chosen  model  can  be  considered  to  be  misspecified. 
Because  of  its  simplicity  and  wide  use  in  forecasting,  it  is  nonetheless  used  in  this  paper  to  illustrate  a  potential  break. 
Moreover,  under  the  assumption  that  GDP  has  a  constant  mean  over  each  subsample,  the  model  does  not  suffer  from  the 
previous critic. 

3 Only the first dip of the Great Recession is excluded for the pre/post-crisis analysis, mainly to avoid an undue effect on the 
length  of  the  post-crisis  sample.  Moreover  it  is  the  steep  2008-2009  financial  crisis  that  has  arguably  introduced  non-
linearities in the link between soft and hard data. 

6 

 
 
                                                            
 
(2016)  point  to  the  strongest  shift  among  consumers  (Graph  2.2)  and,  to  a  lesser  extent,  among 
services managers (Graph 2.3).4 

Graph 2.3. Euro area value added in services (yoy 
% changes), nowcasted with the services 
confidence indicator 

Graph 2.4. Euro area industrial production (yoy % 
changes), nowcasted with the industry 
confidence indicator 

On the other hand, a systematic level shift after the crisis is not apparent in the link between industrial 
production  (IP)  and  the  industry  confidence  indicator  (Graph  2.4):  the  predictions  based  on  the  pre-
crisis estimations are still close to the IP outcomes also after the crisis.  

Yet, a level shift is not the only indicator of a change in the relationship between soft and hard data. 
Econometrical analysis shows that there was indeed a change in this relationship. Let us consider the 
following model: 

(cid:1851)(cid:1867)(cid:1851)((cid:1835)(cid:1842))(cid:3047) = (cid:1854)(cid:2868) + (cid:1854)(cid:2869). ((cid:1835)(cid:1840)(cid:1830)(cid:1847)(cid:3047) − (cid:1835)(cid:1840)(cid:1830)(cid:1847)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)) + (cid:1854)(cid:2870). ΔINDU(cid:2930) + (cid:1873)(cid:3047) 

With these notations, b(cid:2868) can be interpreted as the IP growth corresponding to the long-term average 
level  in  industrial  confidence,  b(cid:2869)  as  the  acceleration  of  IP  following  a  permanent  increase  of  1  in 
industrial  confidence,  and  b(cid:2869) + b(cid:2870)  as  the  acceleration  of  IP  following  a  temporary  increase  of  1  in 
industrial confidence. Table 2.1 shows that there was a level shift in the growth corresponding to the 
average level of industry confidence (with b(cid:2868) decreasing from 1.8 to around 1), but it appears to have 
been offset by a higher sensitivity of industrial production to changes in the survey. 

4 European Commission (2016) focused on possible effects in industry, services and among consumers; it thus excluded the 
retail trade and construction sectors.  

7 

 
 
 
 
 
 
 
 
 
 
                                                            
 
Table 2.1. Estimated parameters depending on the estimation period 

Growth with 
U IN(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364) 
IND = DU
(= (cid:1854)(cid:2868)) 

1.8 

0.7 

1.0 

until 2007Q2 

from 2009Q3 

from 2010Q1 

Acceleration from a permanent 
se of 10 in
(= 10. b(cid:2869)) 

increa

 INDU 

Acceleration from a temporary 
rease of 10 in IN
(= 10. (b(cid:2869) + b(cid:2870))) 

DU 

inc

3.0 

5.5 

3.5 

3.9 

7.1 

7.2 

There is some evidence of a pre-/post-crisis shift in the relation between soft and hard data also at the 
level  of  individual  countries.  Bruno  et  al.  (2016),  using  Italian  micro-data  on  capacity  utilisation, 
provide evidence that the level of capacity utilisation that Italian managers consider as ‘sufficient’ has 
significantly declined after the crisis, pointing to a new, lower 'normal'. 

For  the  United-Kingdom,  Leach  and  Belegratis  (2016)  showed  with  the  ITS  Answering  Practice 
Surveys conducted in 1998, 2008 and 2013 that during and after the crisis, firms have become more 
sensitive to changes in activity. The percentage of  firms  classifying movements of  more than 2% as 
'unchanged'  (rather  than  'risen')  has  decreased,  while  a  higher  percentage  of  firms  consider  that  only 
smaller changes of up to 1 or 2% can be regarded as 'unchanged'. 

3. 

LONG-TERM SHIFTS IN THE RELATIONSHIP BETWEEN 
HARD AND SURVEY DATA: HOW STRONG IS 
ECONOMIC GROWTH WHEN SURVEY INDICATORS 
ARE AT THEIR HISTORICAL AVERAGE? 

The approach described above is ‘discretionary’ in the sense that it assumes a clear distinction between 
the pre- and post-crisis implications of a given level of survey results for economic growth. Curtin and 
Dechaux (2015) take a longer-term perspective and use the level of the business and consumer surveys 
as  a  gauge  for  secular  developments  in  optimism/pessimism.  According  to  the  authors,  the 
'homeostasis'  hypothesis  (return  to  psychological  equilibrium)  implies  that  the  long-term  average  of 
business  and  consumer  confidence  stays  broadly  constant  over  time,  while  measures  of  economic 
performance such as GDP growth are trending over longer time horizons. This supports the idea that 
the link between the surveys and GDP growth might be subject to constant change, not only around 
severe crisis times. 

3.1. 

EURO AREA RESULTS FOR TOTAL ECONOMY 

To  widen  the  perspective  and  better  understand  how  the  link  between  the  surveys  and  GDP  growth 
may have changed over time, one may allow for a continuous adjustment over a large sample.5 To this 
end, two different econometric methods have been applied. The first one is based on the same linear 
model underlying the pre-/post-crisis method used in section 2.2, but instead of estimating it twice, it is 
estimated multiple times over rolling samples of 45 quarters. 

The choice of a 45-quarter (i.e. 11-year) period ensures a sample long enough for stable ordinary least 
square (OLS) estimation of the coefficients. At the same time, the model results can be considered as a 

5 Data for the euro area since 1995 were aggregated by Eurostat. GDP growth before 1995 is based on Fagan et al. (2001). 

8 

 
 
                                                            
 
45-quarter (centered) moving average of the parameters. This means that any shift in the link between 
the ESI and the GDP growth will take 45 quarters to be completely taken into account by this model.6 

Alternatively,  one  can  estimate  a  time-varying  parameter  model  (TVP),  as  suggested  in  European 
Commission (2011). Thus, departing from the same structure as above, the coefficients are allowed to 
vary over time. The advantage of the TVP model compared to the rolling regression is that it does not 
depend  on  the  choice  of  the  length  of  the  moving  average.  The  coefficients  are  estimated  using 
Kalman filtering and smoothing, in a classical state-space representation.7 

When applied to the relationship between GDP growth and the ESI for the euro area, both approaches 
(i.e. rolling regression and TVP model) provide evidence that the estimated coefficients have changed 
significantly  over  the  past  decades.  This  implies  that  for  a  given  level  of  the  ESI,  the  projected,  or 
corresponding, GDP growth did vary over time.  

A straight-forward way to visualise the changing relationship between the ESI and GDP growth over 
time is to calculate the annual GDP growth corresponding to the long-term average of the ESI (of 100), 
which should reflect survey respondents’ ‘normal’ assessment of a ‘neutral’ economic situation. 

The TVP and the rolling regressions methods give similar results for ‘normal’ annual GDP growth for 
the euro area corresponding to an ESI level of 100 (Graph 3.1): such growth used to be above or close 
to  2%  before  2007,  before  falling  steeply  between  2007  and  2009.  After  a  mild  rebound  in  2010, 
'normal'  growth  appears  to  have  stabilised  in  the  post-crisis  period.  Because  the  rolling  regression 
method is closer to a centered moving average of the parameters corresponding to a period of 11 years, 
it  shows  a  smoother  decrease  than  the  TVP  method.  However,  estimates  from  both  models  confirm 
that  the  annual  GDP  growth  corresponding  to  a  level  of  100  in  ESI  is  currently  just  above  1%,  as 
opposed to close to 2% 15 years ago. 

Graph 3.1. Euro area GDP annual growth 
corresponding to the ESI's long-term average 

Graph 3.2. Euro area GDP annual growth 
corresponding to the industry confidence 
indicator's long term average 

6  The  regression  results  are  qualitatively  insensitive  to  the  chosen  window  length  and  mainly  served  to  testify  to  the 
robustness of the TVP results. 

7 For the state-space representation, see for instance Kim and Nelson (1989). The detailed setup is presented in Annex I. 

9 

 
 
 
 
 
 
                                                            
 
The  method  can  robustly  be  applied  to  other  survey  data.  For  instance,  the  Industry  confidence 
indicator  (INDU)  differs  from  the  ESI  in  that  it  is  based  only  on  the  manufacturing  sector.8  Being 
entirely  based  on  managers'  assessments  and  expectations  concerning  their  production  and  business 
situation,  INDU  may  arguably  be  more  directly  linked  to  concrete  growth  perspectives  than  the 
broader ESI, which also takes into account the consumer  confidence. The projection of annual GDP 
growth corresponding to the long-term average of INDU (Graph 3.2) gives comparable results to those 
based  on  the  ESI,  although  the  peaks  and  troughs  do  no  match  exactly.  In  both  cases,  the  estimated 
annual GDP growth is close to but below 3% around 1990, and follows a general decreasing trend up 
until the steeper fall during the Great Recession. Following the crisis period, there are clearer signs of 
a recovery in the INDU-based estimates. Nevertheless, as for the ESI-based estimates, the annual GDP 
growth  corresponding  to  the  long-term  average  estimated  with  the  TVP  model  is  just  above  1%  in 
2016. The rolling regression method confirms the general trend. 

The  marked  decrease  around  the  crisis  period  of  2008-09  provides  some  justification  for  the 
discretionary  ‘pre-/post-crisis’  view  adopted  in  European  Commission  (2016);  however,  the  graph 
shows  that,  in  addition,  there  is  also  a  more  gradual  underlying  trend  of  given  survey  levels  being 
associated with lower growth rates. 

3.2. 

SECTORAL DRIVING FORCES (EURO AREA) 

The same method can also be applied to other pairs of 'soft' and 'hard' data. In principle, this could be 
applied  to  any  economic  indicator  that  can  be  nowcasted  with  a  survey.  For  instance,  the  above-
mentioned industry confidence indicator is generally considered to be a particularly good indicator for 
industrial production growth. Graph 3.3 shows that with both the TVP and the rolling regressions9, the 
rate of annual IP growth corresponding to the long-term average of INDU shows a general decreasing 
trend  between  1990  and  2009.  The  TVP  method  gives  a  very  clear  and  smooth  result:  after  several 
years  above  2%,  the  IP  growth  prevailing  at  an  average  industry  confidence  followed  a  slowly 
decreasing  trend  from  1991  to  2009,  before  picking  up  slightly  in  2010  and  then  stabilising  around 
+0.4%. The rolling regressions are generally in line with the TVP model.  

The results are similar for construction production growth, as projected by the Construction confidence 
indicator.  The  smooth  TVP  estimates  clearly  point  to  a  general  decreasing  trend  from  1990  to  2010 
(from +7.3% to -4.2%), followed by a partial recovery after the Great Recession. The annual growth in 
construction  production  that  is  typically  observed  when  construction  confidence  is  at  its  average 
appears to be stabilising slightly above 0% since 2014. Again, the rolling regressions method gives a 
similar trend. 

8 See the BCS User Guide cited above for details. 

9 Due to the volatility of industrial production, construction production, and retail trade sales, the rolling regressions for these 
series were estimated on a sample window of 96 months (i.e. 8 years). 

10 

 
 
 
 
 
 
                                                            
 
Graph 3.3. Euro area IP annual growth 
corresponding to the industry confidence 
indicator's long-term average 

Graph 3.4. Euro area construction annual growth 
corresponding to the construction confidence 
indicator's long-term average 

Concerning  the  annual  growth  in  retail  trade  corresponding  to  the  long-term  average  of  retail  trade 
confidence,  both  methods  give  very  similar  results,  too.  Overall,  it  seems  that  retail  trade  managers 
adapt  their  perception  of  the  'normal'  growth  faster  than  in  other  sectors.  The  growth  rate  prevailing 
when retailers' confidence is at its long-term average increased sharply from 1996 to 1999 (from +1% 
to  almost  3%).  It  then  decreased  until  2009,  slowly  at  first,  and  more  sharply  around  the  Great 
Recession, down to just below 0%. It picked up in 2012 and appears to remain at around +0.5% for 
now. 

Concerning  the  annual  growth  of  private  consumption  expenditures  corresponding  to  the  long-term 
average  in  the  consumer  confidence  indicator,  the  TVP  method  indicates  a  general  decreasing  trend 
from  1987  to  2011,  with  periods  of  temporary  stabilisation  around  1996  and  2004.  Since  2011, 
'normal'  growth  in  consumption  first  remained  stable  and  then  increased  slightly  in  2015-2016,  to 
reach a rate just below 1%. As for the other sectors, the growth estimated with the rolling regressions 
method confirms the general trend estimated with the TVP. 

By contrast, in the services sector, the annual growth in value added corresponding to the long-term 
average  of  the  services  confidence  indicator  did  not  vary  much  over  the  last  20  years,  remaining  at 
around +2%. Picking up from just below 2% in the late 1990's, it reached a plateau level around +2.4% 
between 2000 and 2008. It then decreased slowly back to a rate just below +2% in 2015-2016. While 
the rolling regressions method indicates a similar rate of growth, the trend is not very clear, owing to 
data shortage.10 

10 Data about the value-added in services are only available from Eurostat since 1995. 

11 

 
 
 
 
 
 
 
 
                                                            
 
Graph 3.5. Euro area retail trade annual growth 
corresponding to the retail trade confidence 
indicator's long-term average 

Graph 3.6. Euro area private consumption annual 
growth corresponding to the consumer 
confidence's long-term average 

Altogether,  the  observation  of  a  decline  in  the  'normal'  rate  of  GDP  growth  since  around  1990, 
accelerated by the Great Recession and then partly recovered and followed by a period of stabilisation 
is evidenced also at the sectoral level. With the exception of the services sector, the observed aggregate 
pattern  is  mirrored  by  developments  in  the  manufacturing  and  construction  sectors  and  in  the 
consumption/retail  trade  sphere.11  The  signs  of  a  recovery  of  'normal'  growth  levels  after  the  crisis 
appear strongest in the construction and retail trade sectors so far.  

Graph 3.7. Euro area value added in services annual growth corresponding to the services confidence 
indicator's long-term average 

11 The finding of corresponding sector-specific shifts underlines that the shift in the relationship between GDP and the ESI is 
not  an  artefact  due  to  the  use  of  fixed  sectoral weights  in  the  aggregation  of  the  ESI,  while  the  share  of  the  sectors  in  the 
economy might have changed over time. 

12 

 
 
 
 
 
 
 
 
                                                            
 
3.3. 

DECOMPOSITION INTO 'ASSESSMENT' AND 'CHANGE' QUESTIONS 

The ESI and the sectoral confidence indicators are composed of two types of survey questions: those 
inquiring respondents' assessment of the economic concept at hand compared to a 'normal' reference 
situation [above normal, normal, below normal] and those asking for their judgement whether and in 
which direction the economic variable in question has changed (or will change) [increase, unchanged, 
decrease]. One may expect that the effect of a lower 'new normal' is more directly visible in responses 
to  the  first  group  of  'assessment'  questions  over  time,  tracing  the  very  effect  that  a  lower  reference 
standard might have on respondents' comparably more generous answering behaviour. However, also 
for  the  'change'  questions,  one  can  imagine  that  lower  reference  standards  or  expectations  may  lead 
respondents to consider as an 'increase' what was considered as an 'unchanged' situation before.  

Graph 3.8. Euro area GDP annual growth corresponding to the ESI's long-term average 

To  look  into  this  distinction,  Graph  3.8  shows  the  results  of  time-varying  parameter  estimations 
between  euro  area  GDP  growth  and  a  partial  ESI  based  on  (i)  'assessment'  questions  only  and  (ii) 
'change' questions only. While the downward trend in the correspondence between hard and soft data 
appears somewhat more pronounced for the indicator based on the ESI's 'assessment' questions only, 
there is clear evidence of the same pattern also for the 'change' questions driving the ESI: a longer-term 
downwards  trend,  accelerated  during  the  financial  crisis,  followed  by  a  pick-up  and  stabilisation 
thereafter.  Given  that  the  theoretical  reasoning  for  an  adjustment  of  survey  responses  over  time  to  a 
new  normal 
the 
sentiment/confidence indicators, we will continue to look at the customary composite indicators in the 
following.  

is  backed  by  empirical  evidence  for  both 

types  of  questions  entering 

3.4. 

COUNTRY RESULTS FOR THE LARGEST ECONOMIES OF THE EURO AREA 

To test the robustness of the approach and findings, the methodology can also be applied to individual 
countries.12 Graph 3.9 shows the results for Germany. The trend estimated by the TVP method shows 
some specific features compared to the euro area. After a sharp decrease from almost 3% in 1992, the 
growth corresponding to the ESI's long-term average decreased to slightly less than 1% in 1998. It then 
picked up to reach almost 2% in 2002, and remained at this level until 2007. With the Great Recession, 
'normal' growth decreased sharply until 2009 and, interrupted by a short-lived recovery in 2010, kept 

12 For presentational reasons, the focus here is on estimating the annual GDP growth corresponding to the ESI's long-term 
average, but in principle the same could also be done for individual sectors, including linking the INDU confidence indicator 
instead of the ESI to overall GDP growth (see results in section 4). 

13 

 
 
 
                                                            
 
decreasing thereafter. In 2015-16 there are signs of stabilisation at just below +0.5%. Keeping in mind 
that  the  rolling  regressions  method  represents  a  45-quarters  moving  average  of  the  parameters,  the 
results are broadly in line for both methods. 

The annual GDP growth corresponding to the ESI's long term average for France parallels that of the 
euro  area.  It  decreased  slowly  from  3%  in  1985  to  0.5%  in  2009,  and  then  picked  up  and  remained 
above but close to 1%. Once again, the estimation using the rolling regressions is generally in line with 
that from the TVP method. 

Graph 3.9. Germany GDP annual growth 
corresponding to the ESI's long-term average 

Graph 3.10. France GDP annual growth 
corresponding to the ESI's long-term average 

In Italy, the annual GDP growth corresponding to the ESI's long term average estimated with the TVP 
method  shows  a  trend  similar  to  that  of  France  and  the  euro  area.  It  decreased  from  almost  4%  in 
198513 to around -0.8% in 2009 and then picked up in the aftermath of the Great Recession to 0.6% in 
2011. However it fell back to around 0% in 2015 and 2016. The rolling regressions method indicates a 
rate of growth similar to that of the TVP method. 

Concerning  Spain,  the  annual  GDP  growth  corresponding  to  the  ESI's  long  term  average  estimated 
with the TVP method is quite smooth. It decreased from above 4% in 1987 to just above 2% in 1998, 
and then increased again to almost 3% in 2006. It then fell sharply in the crisis to around 0.5% in 2012. 
Since  then,  'normal'  growth  according  to  the  ESI  has  recovered  to  almost  2%  in  2016.  The  rolling 
regressions method broadly confirms the general decreasing trend of 'normal' growth in Spain. 

13 GDP growth in Italy and Spain before 1995 was downloaded from the OECD website. 

14 

 
 
 
 
 
 
 
  
                                                            
 
Graph 3.11. Italy GDP annual growth corresponding 
to the ESI's long-term average 

Graph 3.12. Spain GDP annual growth 
corresponding to the ESI's long-term average 

All  in  all,  the  results  presented  in  this  section  clearly  indicate  that  the  link  between  the  surveys  and 
GDP growth is subject to constant change, and not only around severe crisis times. Two methods were 
used  in  this  section:  firstly,  the  conceptually  simple  but  robust  rolling  regressions  method,  and 
secondly, the technically more sophisticated TVP method. Since both methods give similar results, this 
suggests that also the TVP method can be considered robust. In addition, the TVP method has a clear 
advantage in terms of timeliness compared to the rolling regressions and will therefore be preferred in 
the following section. 

4.  AN ALTERNATIVE MEASURE OF POTENTIAL GDP 

GROWTH 

We have argued that the long-term averages of economic confidence indicators describe a neutral or 
normal assessment of the economic situation by economic agents. Growth rates corresponding to this 
neutral reference point can arguably be interpreted as the ‘underlying’ trend, or potential, growth, as 
perceived  by  survey  respondents.  The  results  are  consistent  with  the  hypothesis  that  the  level  of 
economic activity which people consider as ‘normal’ changes over time: they adapt their views to the 
average growth experienced, or perceived, over the mid- to long-term. From a more technical point of 
view,  these  estimates  can  be  interpreted  as  a  measure  of  GDP  growth  adjusted  for  the  cycle  (i.e. 
potential  growth),  where  the  position  in  the  cycle  is  determined  on  the  basis  of  the  widely  used 
business and consumer survey results. 

The most commonly used methodologies to estimate potential output and the deviations of measured 
output  therefrom  are  based  on  production  functions.14 These  more  sophisticated methodologies  have 
the  practical  drawback  that  the  results  can  be  heavily  revised,  especially  towards  the  end  of  the 

14 On the methodology for estimating potential growth in the European Commission, see Havik et al. (2014). While based on 
a much more sophisticated methodology distinguishing between supply and demand side effects, the estimation based on a 
production function also uses results from business surveys to characterise cyclical deviations and extract them from observed 
total factor productivity growth. Contrary to the methodology presented in this paper, the cycles in manufacturing industry are 
identified using a surveyed capacity utilisation indicator, which might be a more accurate measure than synthetic confidence 
indicators in conceptual terms. 

15 

 
 
 
 
 
                                                            
 
sample.15  The  idea  to  revert  to  qualitative  BCS  data  to  overcome  this  issue  of  strong  revisions  was 
already used in ECB (2015). Our approach, based on synthetic confidence indicators rather than survey 
information on 'factors limiting production', can be considered as an alternative.  

Graph 4.1 shows the ‘normal’ euro area annual GDP growth estimated with the TVP model using the 
ESI and the industry confidence indicator (INDU), compared with the potential growth estimated by 
DG ECFIN using a production function. Despite the different underlying methodologies, the similarity 
between the curves is striking, especially for the INDU-based estimate. While showing some specific 
features especially in 1998-2002 and 2011-14, also the 'normal' growth estimated using the ESI shows 
a general trend similar to the two other series. This suggests that business and consumer survey results 
can  be  used,  beyond  their  usual  short-term  forecasting  purposes,  to  gauge  changes  in  long-term  or 
potential growth. As it is based on the perceived economic confidence or sentiment of managers and 
consumers, this survey-based measure could be called 'perceived potential growth'. The graphs in the 
annex show that potential growth estimates by other institutions (IMF, OECD) have been closer to our 
'perceived' estimates in the aftermath of the crisis.  

Graph A.6 in Annex III shows the revisions in the estimates of 'normal' growth according to a pseudo 
real time exercise between 2007Q1 and 2017 Q3. While the results are not immune against revisions 
of past GDP growth itself, the revisions towards the end of the sample due to the methodology of time-
varying  parameter  estimation  appear  rather  limited.  In  addition,  Graphs  A.7  and  A.8  in  Annex  III 
compare the estimations derived from a Kalman filter to those from a Kalman smoother; as the former 
is  not  revised  when  subsequent  observations  become  available,  the  strong  similarity  between  the 
estimates underlines that the revisions due to the use of the proposed methodology are limited. 

Graph 4.1. 'Normal' euro area GDP growth and potential growth 

In  Germany,  although  all  estimations  show  a  relatively  similar  decreasing  trend  from  around  3%  in 
1992 to 1% in 2012, the year-on-year changes and even the rates of growth estimated seem to depend 
strongly  on  the  methodology.  Even  using  a  similar  methodology,  estimations  based  on  production 
functions  (from  DG  ECFIN,  IMF  or  OECD)  can  differ  noticeably  (see  annex).  The  estimates  show 
markedly  different  trends  since  2013;  while  potential  growth  estimated  by  DG  ECFIN's  production 
function  increased  to  reach  almost  2%  in  2016,  the  'perceived'  potential  growth  remained  virtually 
unchanged around 1% (INDU) or decreased to below 0.5% (ESI). Estimations by the IMF and OECD 
(see annex) indicate a virtually unchanged potential growth since 2013, though at a higher level than 
our estimate using INDU.  

There  are  two  main  explanations  for  this  discrepancy.  First,  it  is  possible  that  potential  growth 
perceived by German economic agents is indeed below the actual potential growth, possibly induced 

15  On  the  revisions  and  uncertainty  of  estimates  of  potential  growth  and  the  output  gap,  see  for  instance  Anderton  et  al. 
(2014), ECB (2011) or Marcellino and Musso (2010). 

16 

 
 
                                                            
 
by long-lasting legacy effects of the crisis weighing on expectations. Given that estimates  for recent 
years  are  often  subject  to  strong  revisions,  it  is  also  possible  that  current  potential  growth  could  be 
somewhat overstated by the production function approaches. 16  

Graph 4.2. 'Normal' Germany GDP growth and 
potential growth 

Graph 4.3. 'Normal' France GDP growth and 
potential growth 

In  France,  the  potential  growth  estimated  using  a  production  function  shows  a  similar  overall  trend 
compared to the estimates of 'perceived' potential growth. All three decreased from almost 3% in 1989 
to  around  1%  in  2009,  and  all  estimates  for  2016  are  consistent  at  again  around  1%.  Nevertheless, 
there are some differences between the three estimations. First, the potential growth estimated with a 
production  function  was  2%  in  1985,  while  the  'perceived'  potential  growth  was  closer  to  3%.  In 
addition,  while  the  latter  decreased  rather  steadily  from  1996  to  2008  and  remained  unchanged  in 
2009, the former decreased only mildly and irregularly between 1993 and 2008, before falling sharply 
to  a  growth  rate  close  to  1%  in  2009.  Furthermore,  both  ECFIN's  potential  growth  estimate  and  the 
INDU-based  perceived  potential  growth  remained  virtually  unchanged  since  2009,  while  the  ESI-
based potential growth picked up to 1.5% in 2013 and then decreased back to around 1% in 2015 and 
2016.  Overall,  the  INDU-based  estimate  for  France  appears  smoother  and  closer  to  the  production-
function based estimates.  

Also in Italy, overall trends are similar for the production-function based and time-varying parameter 
methods. According to the three estimates, potential growth decreased from  around 2.5% in 1988 to 
between 0% and -0.5% in 2009, and is currently close to 0%. However, there are several differences 
between the estimates in particular in the aftermath of the Great Recession. On the one hand, the ESI-
based perceived potential growth picked up in 2010 and 2011 to close to 1%, and then decreased to 
close to zero in 2015 and 2016; the INDU-based perceived potential growth shows a slight increase in 
2016. By contrast, the potential growth estimated with a production function remained around -0.5%, 
apart from a kink in 2011-2012, and is still estimated at -0.3% in 2016. The estimates by the IMF and 
OECD are somewhat closer to the two 'perceived' potential growth estimates (see graph in Annex II). 

16 If this were the case, GDP in Germany might already be close to or above potential output, consistent with the historically 
low rate of unemployment. While inflation clearly is still low in Germany, the link between the economic cycle and wages or 
core inflation (as postulated by the Phillips curve) has been found loosening in advanced economies in recent years, see e.g. 
Albuquerque and Baumann (2017) for a recent discussion. 

17 

 
 
 
 
 
                                                            
 
Graph 4.4. 'Normal' Italy GDP growth and potential 
growth  

Graph 4.5. 'Normal' Spain GDP growth and 
potential growth 

Finally, for Spain the variation in the potential growth estimated by the production function approach 
is much larger than that in 'perceived' potential growth. From around 4% in 1989, the potential growth 
estimated by DG ECFIN's production function decreased to below 2½% in 1996 and then increased to 
close to 4% before the crisis hit. It then plunged to close to -1% in 2013 and picked up slowly to reach 
+0.5%  in  2016.  While  the  profile  of  the  ESI-based  perceived  potential  growth  is  quite  similar,  the 
estimated  rates  were  significantly  lower  before  the  crisis  and  significantly  higher  since  2012.  The 
INDU-based  perceived  potential  growth  shows  a  much  smoother  decline  between  2000  and  2012, 
remaining in positive territory, and points to a modest rebound in potential growth since 2013. Since 
1992  it  closely  parallels  the  potential  growth  estimates  by  the  OECD  and  the  IMF  (see  Annex  II), 
including for the recent post-crisis period. 

5.  CONCLUSIONS 

Overall,  the  presented  results  call  for  caution  when  mechanically  mapping  survey  data  levels  into 
actual  economic  growth  rates.  While  the  current  level  of  the  ESI,  which  is  well  above  its  long-term 
average, is compatible with expanding economic activity, it also hints at lower growth rates than those 
implied in the pre-recession period. Using rolling regressions and time-varying parameters models, this 
paper shows that the growth rates implied by euro-area survey results did not only fall during the Great 
recession,  but  had  decreased  already  since  around  1990.  This  is  true  for  the  overall  economy 
(measured  by  GDP  growth),  but  also  industrial  production,  construction  production,  retail  trade  and 
household consumption. 

Furthermore, this paper suggests that business and consumer survey results can be used, beyond their 
usual short-term forecasting purposes, to gauge changes in long-term or potential growth. Despite the 
completely  different  underlying  methodologies,  the  similarity  between  the  results  presented  here, 
called  'perceived  potential  growth',  and  potential  output  growth  estimates  based  on  production 
functions  is  striking.  The  results  of  this  alternative  method  are  also  presented  for  the  four  largest 
economies of the euro area, giving support to the hypothesis of a ‘new modesty’ connected with the 
decline in potential output growth observed in recent years. 

The  'perceived'  potential  growth  estimates  presented  in  the  paper  might  be  useful  in  complementing 
the  assessment  of  long-term  growth  prospects.  The  empirical  applications  show  a  high  degree  of 

18 

 
 
 
 
 
similarity  with  established  potential  growth  estimates  based  on  production  functions,  but  also  some 
differences.  The  latter  concern  in  particular  Germany,  where  the  estimates  derived  from  survey 
respondents'  perceptions  point  to  lower  potential  growth  than  indicated  by  production  functions  in 
recent years, and Spain, where, on the contrary, the time-varying regressions using survey data point to 
higher 'perceived' long-term growth than current potential growth estimates. While the estimates lack 
the  foundation  of  the  production-function  based  estimates  in  economic  theory,  they  have  a  clear 
conceptual  interpretation  of  GDP  growth  adjusted  for  the  cycle  (i.e.  potential  growth),  where  the 
position in the cycle is based on the widely used business and consumer survey results. Moreover, they 
have the advantage of being available  in real time  and do not depend on assumptions on the factors 
driving potential growth, which are often subject to later revisions. This being said, the results are not 
immune against revisions of past GDP growth itself. 

19 

 
 
REFERENCES 

Albuquerque, B. and Baumann, U., 2017, "Will US inflation awake from the dead? The role of slack 
and non-linearities in the Phillips curve", ECB Working Paper series No. 2001. 

Anderton, R., Aranki, T. Dieppe, A., Elding, C., Haroutunian S., Jacquinot, P., Jarvis, V., Labhard, V., 
Rusinova, D. and Szörfi, B., 2014, "Potential output from a euro area perspective", Occasional Paper 
Series, No 156, ECB, November 2014 

Bruno,  G.,  Crosilla,  L.  and  Margani,  P.,  2016,  "Inspecting  the  relationship  between  business 
confidence  and  industrial  production:  evidence  based  on  Italian  survey  data",  paper  presented  at  the 
2016 CIRET conference 

Curtin, R. and Dechaux, P., 2015, "Measuring and Interpreting Economic Expectations", presented at 
the 2015 Joint EC/OECD Workshop, November 2015 

ECB,  2011,  "Recent  evidence  on  the  uncertainty  surrounding  real-time  estimates  of  the  euro  area 
output gap", Monthly Bulletin, November 2011 

ECB, 2015, "A survey-based measure of slack for the euro area", ECB Economic Bulletin, Issue 6 / 
2015 – Box 6. 

European Commission, 2011, "Is there a decoupling between soft and hard data", European Business 
Cycle Indicators, July 2011. 

European  Commission,  2016,  'New  normal'?  -  The  impact  of  the  financial  crisis  on  business  and 
consumer survey data, European Business Cycle Indicators, 3rd quarter 2016. 

Fagan,  G.,  Henry,  J.  and  Mestre,  R.,  2001,  "An  area-wide  model  (AWM)  for  the  euro  area",  ECB 
Working Paper No. 42, January 2001 

Ferrara,  L.,  Guégan,  D.  and  Rakotomarolahy,  P.,  2010,  "GDP  nowcasting  with  ragged-edge  data:  A 
semi-parametric modelling", Journal of Forecasting, 29, 186-199. 

Havik K., Mc Morrow K., Orlandi F., Planas C., Raciborski R., Röger W., Rossi A., Thum-Thysen A. 
and  Vandermeulen  V.  (2014),  “The  Production  Function  Methodology  for  Calculating  Potential 
Growth Rates & Output Gaps”, European Economy, Economic Papers 535. 

Leach, A. and Belegratis, A., 2016, "Evaluating the performance of aggregate survey-based indicators 
in times of crisis" 

Marcellino, M. and Musso, A., 2010, "Real time estimates of the euro area output gap", ECB Working 
paper, No. 1157, February 2010 

Nelson, C. and Kim, C.-J., 1988, "The Time-Varying-Parameter Model as an Alternative to ARCH for 
Modeling Changing Conditional Variance: The Case of Lucas Hypothesis", NBER Technical Working 
Paper No. 70 

Rioust  De  Largentaye,  T.  and  Roucher,  D.,  2015,  "How  closely  do  business  confidence  indicators 
correlate with actual growth?", TRÉSOR-ECONOMICS No. 151 – August 2015. 

20 

 
 
 
ANNEX I 

Time-varying parameter models estimation 

Departing  from 
results. 

a  base OLS  odel,  whe e  the  growth  is  measured  y ar-on-yea  

m

r to  smoothen  the 

e

r

(cid:1877)(cid:1867)(cid:1877)((cid:1851)(cid:3047)) = (cid:1854)(cid:2868) + (cid:1854)(cid:2869). (cid:3435)(cid:1831)(cid:1845)(cid:1835)(cid:3047) − (cid:1831)(cid:1845)(cid:1835)(cid:3047)(cid:3439) + (cid:1854)(cid:2870). Δ(cid:2872)(cid:1831)(cid:1845)I(cid:2930) + (cid:1873)(cid:3047), (cid:1873)(cid:3047) ∼ (cid:1840)(0, (cid:2026)(cid:3048)

(cid:2870)) 

The following m

i

odel in a class cal state space representation: 
(cid:1877)(cid:1867)(cid:1877)((cid:1851)(cid:3047)) = (cid:1854)

(cid:2868),(cid:3047) + (cid:1854)(cid:2869),(cid:3047). (cid:3435)(cid:1831)(cid:1845)(cid:1835)(cid:3047) − (cid:1831)(cid:1845)(cid:1835) (cid:3439) + (cid:1854)(cid:2870),(cid:3047). Δ(cid:2872)(cid:1831)(cid:1845)I(cid:2930) + (cid:2021)(cid:3047), (cid:2021)(cid:3047)
(cid:3047)
(cid:2870)(cid:3439), (cid:1861) ∈ (cid:4668)0,1,2(cid:4669) 
(cid:1854)(cid:3036),(cid:3047)(cid:2878)(cid:2869) = (cid:1854)(cid:3036),(cid:3047) + (cid:2013)(cid:3036),(cid:3047), (cid:2013)(cid:3036),(cid:3047) ∼ (cid:1840)(cid:3435)0, (cid:2026)(cid:3036)

∼ (cid:1840)(0, (cid:2026)(cid:3092)

(cid:2870)) 

Or in case (cid:1854)(cid:2870) is not sig

nificantly different from 0 in the OLS model:
(cid:1877)(cid:1867)(cid:1877)((cid:1851)

(cid:3047)) = (cid:1854)(cid:2868),(cid:3047) + (cid:1854)(cid:2869),(cid:3047). (cid:3435)(cid:1831)(cid:1845)(cid:1835)(cid:3047)
(cid:1854)(cid:3036),(cid:3047)(cid:2878)(cid:2869) = (cid:1854)(cid:3036),(cid:3047) + (cid:2013)(cid:3036),(cid:3047), (cid:2013)(cid:3036),(cid:3047) ∼ (cid:1840)(cid:3435)0, (cid:2026)(cid:3036)

(cid:1845)(cid:1835)(cid:3047)(cid:3439) + (cid:2021)(cid:3047), (cid:2021)(cid:3047) ∼ (cid:1840)(
(cid:2870)(cid:3439), (cid:1861) ∈ (cid:4668)0,1(cid:4669) 

− (cid:1831)

0, (cid:2026)(cid:3092)

(cid:2870)) 

Here, (cid:1854)(cid:2868),(cid:3047) is the annual growth corresponding to the ESI's long term average (100). 
This time-varying parameter model is estimated with a Kalman filter and smoother, with the following 
initial conditions: 

•  variance of the observation noise (cid:2026)(cid:3092) = 0.5	(cid:2026)(cid:3048) 
•  diagonal elements of the variance matrix of the system noise 

o  (cid:2026)(cid:3036) = (cid:2009)	(cid:2026)(cid:3016)(cid:3013)(cid:3020), where (cid:2009) is a smoothness parameter to be determined later. 

(cid:3036)

• 

• 

the expec d

te  value o

f th p - m le state vector 

e  re sa p

o 

 (cid:1865)(cid:2868) = (cid:3429)

(cid:1854)(cid:2868)
(cid:1854)(cid:2869)(cid:3433) or (cid:1865)(cid:2868) = (cid:3428)
(cid:1854)(cid:2870)

(cid:1854)(cid:2868)
(cid:1854)(cid:2869)

(cid:3432) 

the variance matrix of the pre-sam l s

p e  tate

 ve o  
ct r

o 

	(cid:1829)(cid:2868) = (cid:3429)

50
0
0

0
50
0

0
0
50

(cid:3433) or (cid:1829)(cid:2868) = (cid:4674)

50
0

0
50

(cid:4675) 

To estimate (cid:2009), which can be considered as a smoothness parameter, the idea is to choose the optimal 
fit in the following model: 

(cid:1869)(cid:1867)(cid:1869)((cid:1851)(cid:3047)) = (cid:2010)(cid:2868). (cid:1854)(cid:2868),(cid:3047)

((cid:2009)) + (cid:2010)(cid:2869). (cid:3435)(cid:1831)(cid:1845)(cid:1835)(cid:3047) − (cid:1831)(cid:1845)(cid:1835)(cid:3047)
The  rationale  behind  this  choice  for  (cid:2009)  is  to  improve  the  constant  in  a  forecasting  setup.  And  then 
compare if this model is a better fit than the same one with a constant instead of (cid:1854)(cid:2868),(cid:3047)(cid:2879)(cid:2869)((cid:2009)). 

(cid:3439) + (cid:2010)(cid:2870). Δ(cid:1831)(cid:1845)I(cid:2930) + (cid:2013)(cid:3047) 

(cid:2879)(cid:2869)

21 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ANNEX II 

'Normal'  growth  and  other  estimations  of  potential  growth  based 
on a production function (from OECD and IMF) 

Graph A.1. 'Normal' euro area GDP growth and potential growth 

Graph A.2. 'Normal' Germany GDP growth and potential growth 

22 

 
 
 
 
 
 
 
 
 
 
Graph A.3. 'Normal' France GDP growth and potential growth 

Graph A.4. 'Normal' Italy GDP growth and potential growth 

Graph A.5. 'Normal' Spain GDP growth and potential growth 

23 

 
 
 
 
 
 
 
 
ANNEX III 

Revisions in 'normal' growth 

Graph A.6. Revisions in 'normal' growth in pseudo real time between 2007Q1 and 2017Q3 

Graph A.7. Estimations of euro area annual GDP growth corresponding to the ESI's long-term average, 
based on a Kalman filter and a smoother 

24 

 
 
 
 
 
 
 
Graph  A.8.  Estimations  of  euro  area  annual  GDP  growth  corresponding  to  the  industry  confidence 
indicator's long-term average, based on a Kalman filter and a smoother 

25 

 
 
 
 
 
 
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