Evaluating medium term forecasting methods and their implications for EU output gap calculations.
Date: 2018-05-22
Institutions: Directorate-General for Economic and Financial Affairs
Subjects: common agricultural policy, economic forecasting, economic planning, economic statistics, market economy, supply and demand

6

ISSN 2443-8022 (online)

Evaluating Medium Term 
Forecasting Methods 
& their Implications 
for EU Output Gap 
Calculations

Kieran Mc Morrow, Werner Roeger 
and Valerie Vandermeulen

DISCUSSION PAPER 070| OCTOBER 2017

EUROPEAN ECONOMY

EUROPEAN 
ECONOMY

Economic and 
Financial Affairs

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European Commission 
Directorate-General for Economic and Financial Affairs 

Evaluating Medium Term Forecasting Methods 
and their Implications for EU Output Gap 
Calculations  

Kieran Mc Morrow, Werner Roeger and Valerie Vandermeulen 

Abstract 

This paper sheds light on two specific, but interlinked, questions – firstly, how do the EU's, medium 
term actual GDP growth rate forecasts compare, in terms of accuracy and biasedness, with those of the 
EU's  Member  States,  in  their  annual  Stability  and  Convergence  Programme  (SCP)  updates;  and 
secondly,  should  medium  term  forecasts  be  allowed  to  influence  the  short  run  output  gap  and 
structural  balance  calculations  used  in  the  EU’s  fiscal  surveillance  procedures.  Regarding  the  first 
question, the paper concludes that the EU's medium term forecasts are equally as good, and arguably 
better,  than  those  of  the  SCP's  both  with  respect  to  accuracy  and  biasedness.  Regarding  the  second 
question, due to the relatively rapid loss in forecast accuracy as the time horizon lengthens; the paper 
suggests  that  using  more  forecast  information  should  be  avoided  in  the  output  gap  and  structural 
balance  calculations.  Extending  the  forecast  horizon  to  be  used  in  the  output  gap  calculations  could 
exacerbate  an  existing  optimistic  bias  with  respect  to  the  supply  side  health  of  the  EU’s  economy, 
thereby enlarging the risk of procyclicality problems, especially in the upswing phase of cycles, where 
most of the large fiscal policy errors tend to occur. 

JEL Classification: C10, E60, O10. 

Keywords: Production function methodology, output gaps. 

Acknowledgements:  The  authors  would  like  to  thank  the  members  of  the  Output  Gap  Working 
Group  (OGWG)  for  their  valuable  contributions  to  the  ongoing  debate  on  the  relative  merits  of 
medium  term  forecasting  methods.  In  addition,  the  contributions  of  Alessandro  Rossi,  Christophe 
Planas  and  Karel  Havik  are  gratefully  acknowledged.  The  authors  would  also  like  to  thank  the 
reviewers  Atanas  Hristov,  Francesca  d'Auria  and  Bozena  Bobkova  for  their  valuable  comments  and 
suggestions for improvement. 

The closing date for this document was August 2017. 

Contact:  Kieran  Mc  Morrow,  kieran.ccmorrow@ec.europa.eu,  Werner  Roeger,  werner. 
roeger@ec.europa.eu,  Valerie  Vandermeulen,  valerie.vandermeulen@ec.europa.eu;  European 
Commission, Directorate-General for Economic and Financial Affairs. 

EUROPEAN ECONOMY                                                                                      Discussion Paper 070 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
CONTENTS 

Executive Overview ................................................................................................................................ 5 

1. 

Introductory Remarks ...................................................................................................................... 8 

2.  Overview of Medium Term Forecasting Methods used by the EU's Member States ............ 9 

3.  Possible forecasting approaches and how do other international institutions deal with 

the issue of judgemental versus non-judgemental medium term forecasting methods ... 10 

4.  Comparison of the results of EU Commission versus Member State (SCP) forecasting 
methodologies, based on the available empirical evidence ....................................................... 13 

4.1. Accuracy of the medium term growth forecasts ............................................................................... 13 

4.2. Fiscal surveillance implications............................................................................................................... 18 

4.3. Impact of a longer forecast horizon ..................................................................................................... 21 

5.  Overall evaluation of the EU’s, non-judgemental, medium term forecasting methodology ..... 24 

6.  Concluding Remarks .................................................................................................................... 27 

7.  References ..................................................................................................................................... 28 

8.  Annexes .......................................................................................................................................... 30 

8.1. Actual Growth Rate Revisions : COM versus Member States (SCP updates) ................................ 30 

8.2. Potential Growth and Output Gap Revisions : COM versus Member States (SCP updates)...... 35 

8.3. A short overview of the EU's production function methodology ..................................................... 42 

3 

 
 
 
 
 
 
 
 
 
 
 
 
EXECUTIVE OVERVIEW  

This paper assesses the implications of a proposal, made by several EU Member States, to change the 
way the EU's commonly agreed Production Function (PF) methodology currently estimates the output 
gaps used in the EU's fiscal surveillance procedures. The specific proposal analysed is whether the PF 
method's, model based, medium term actual GDP growth forecasts should be replaced with medium 
term  judgemental  forecasts  from  the  EU  Commission's  (COM)  experts.  This  would  be  a significant 
methodological change since currently only COM's short run judgemental forecasts are allowed to be 
taken  into  account  in  the  output  gap  calculations.  At  the  moment,  the  medium  term  actual  GDP 
growth  forecasts  are  fully  non-judgemental,  with  the  latter  forecasts  based  on  the  PF  method's 
medium  term  potential  growth  forecasts  and  an  EPC  endorsed  output  gap  closure  rule  (which 
essentially ensures that the output gap is closed after five years). Consequently, the key question to be 
examined  is  whether  the  EU's  current  use  of  a  model  based,  medium  term,  GDP  forecasting 
methodology should be replaced by a judgemental approach1. 

The  opinions  amongst  EU  policy  makers  on  the  extent  to  which  it  is  prudent  to  use  judgemental 
medium  term,  actual  GDP,  forecasts,  range  across  a  wide  spectrum.  At  one  extreme  are  those  who 
would  be  in  favour  of  excluding  even  COM's  short  run  judgemental  forecasts  from  the  output  gap 
calculations  due  to  their  persistent  optimistic  bias.  At  the  other  extreme  are  those  who  argue  for 
including medium term judgemental forecasts from COM to be taken into account in the calculations. 
The  PF  method  is  currently  in  the  middle  of  this  range  of  options,  and  therefore  it  is  important  to 
assess the theoretical and empirical evidence on this issue to see if the current status quo is supported 
or whether a change to a longer or shorter judgemental forecasting horizon should be considered.  

In examining the case for allowing medium term actual GDP growth forecasts from COM to be used 
in the EU's output gap (and consequently the structural balance) calculations, the key questions to be 
addressed are:  

  Firstly,  how  do  the  EU's  medium  term  actual  GDP  growth  rate  forecasts  (currently  100% 
model  driven)  compare,  in  terms  of  accuracy  and  biasedness,  with  those  of  the  Member 
States, in their annual Stability and Convergence Programme (SCP) updates and would it be 
economically justified for the EU to replace its model based, medium term, methodology with 
a more judgement driven forecasting methodology?; and  

  Secondly, and most importantly, what are the implications of allowing medium term forecasts 
to directly influence the short run output gap and structural balance estimates used in the EU's 
fiscal surveillance procedures?  

In  relation  to  the  first  question,  this  paper  shows  that  the  EU's,  100%  model  driven,  medium  term 
actual GDP growth forecasts compare favourably with those  of the Member States' SCP updates, in 
terms  of  accuracy  and  biasedness,  outperforming  the  SCP  forecasts  in  2  of  the  3  forecast  vintages 
which were analysed  and having an average forecast bias over the 3  vintages which is significantly 
smaller  than  that  of  the  SCP's.  In  addition,  the  EU's  model  based  approach  to  medium  term 
forecasting appears preferable to more judgement-driven approaches given the empirical evidence in 
the  literature  which  shows  that  the  value  added  of  judgemental  forecasts  beyond  a  few  quarters  is 
highly questionable. Most economic commentators support the view that, for current year forecasts, 
there is a lot more information on economic developments (than a model can provide) which allows a 
superior judgemental forecast compared to a model based projection. However, this is a much more 

1 "Judgemental" forecasting methods rely heavily on the subjective expertise of experienced economists based on a wide set 
of information & incorporating intuitive judgement, opinions & subjective probability assessments. "Rules" / model based 
methods,  on  the  other  hand,  keep  the  degree  of  expert  judgement  to  an  absolute  minimum,  relying  instead  on  codified, 
model-based, calculations. 

5 

 
 
                                                            
 
problematic  proposition  beyond  the  first  year2,  and  especially  over  a  medium  term  time  horizon, 
where  the  absence  of  high-frequency  economic  indicators,  as  well  as  the  growing  importance  of 
structural  growth  determinants  and  dynamic  interactions,  necessitate  the  use  of  some  sort  of 
modelling approach3.  

In terms of trying to  understand  the possible sources  of forecast biasedness, the paper suggests that 
judgemental  forecasts  are  often  plagued  by  the  difficulties  forecasters  have  in  predicting  crises  and 
large  recessions.  For  behavioural  reasons,  a  short  run  optimistic  bias  is  likely  to  increase  with  the 
forecast  horizon  since  forecasters  often  find  it  "easier"  (for  expectational,  confidence  reasons)  to 
project  an  acceleration  in  growth  in  the  outer  forecast  year(s)  rather  than  a  deceleration.  Although 
recessions are relatively common, COM, OECD and IMF forecasters have practically never forecast a 
negative growth rate in the second year of their short term forecasts, leading to a positive bias in the 
EU's  output  gap  estimations  (since  they  are  based  on  COM's  short  term  forecasts).  Adding  more 
forecast  years,  for  example  to  cover  the  medium  term,  would  increase  the  statistical  likelihood  of 
negative  growth  occurring  over  this  longer  forecast  horizon,  but  not  the  likelihood  of  it  being 
correctly projected. The current EU method is therefore not bias-free, as such a bias exists in the short 
term, but  this behavioural rationale for the existence  of a bias is at least  excluded  over the medium 
term, as the latter is strictly model-based. 

In addition to the accuracy / bias criterion, the EU's medium term, model based, forecasting approach 
is also assessed using  several other evaluation criteria. Firstly, we find that  the EU's method can be 
considered as being consistent with "best practice", with a broad consensus in the literature supporting 
the  EU's  use  of,  model  based,  medium  term  forecasting  methods  which  exploit  both  supply  and 
demand  side  developments.  Secondly,  the  EU's  method  is  considered  to  be  transparent  and  to 
guarantee  equal  treatment,  which  is  a  crucial  criterion  for  the  EU's  fiscal  surveillance  framework 
since,  unlike  the  situation  with  other  international  organisations,  the  EU's  output  gap  estimates  are 
used  in  legally  binding  fiscal  policy  decisions.  This  differentiation  in  the  constraints  facing  the 
different international organisations is vital in understanding the specificities of their respective fiscal 
surveillance frameworks. Like the EU, the IMF and the OECD both use a model based, medium term, 
actual GDP growth, forecasting approach, where the actual GDP forecast is guided by the underlying 
supply  side  fundamentals  (i.e.  potential  output)  and the  economy's  cyclical  position  (i.e.  the  current 
level  of  the  output  gap  –  anchored  on  the  expected  potential  output  path).  They  consider  this 
combined  supply  and  demand  side  guided  approach  to  be  the  most  effective  way  of  handling  the 
additional uncertainty linked to the longer forecast horizon. Unlike the EU, however, ultimately it is 
the  judgement  of  the  IMF  or  OECD  expert  which  determines  the  medium  term  forecast  –  they  are 
only guided by, not controlled by, the model based approach. In the case of the EU, the Stability and 
Growth  Pact  (SGP)  principles  of  transparency  and  equal  treatment  dictate  that  the  EU's  PF  model 
based forecasts cannot be overruled by COM’s experts. 

One  final  point  in  relation  to  question  one,  whilst  the  EU's  current,  model  based,  medium  term 
forecasting approach compares favourably with those of the Member States' SCP's, and is consistent 
with the forecasting methodology used by other international organisations, it is clear from the results 
of the present paper that further improvements are possible: 

  Firstly, improvements are possible with respect to the EU's medium term potential output path 
(which  economies  tend  to  revert  back  towards)  where  work  is  still  needed  to  produce  an 
unbiased, no policy change, baseline; and  

2 Uncertainty levels naturally grow with respect to developments which are further away in time. 

3  Beyond  the  short  run,  structural,  supply  side  factors  become  more  important  in  driving  economic  developments  and 
consequently, like the OECD and the IMF, the EU believes that having an unbiased estimate of the level of potential output 
is a crucial first step in the process of obtaining credible medium term projections. In this context, the EU’s PF method uses 
an  augmented  Solow  growth  model,  with  macro  variables,  such  as  potential  GDP  (using  information  about  trends  for 
structural growth determinants, including labour, capital and total factor productivity) and the output gap (using information 
about capacity utilisation and the Phillips curve), all modelled using a parsimonious economic framework (annex 8.3). 

6 

 
                                                            
 
  Secondly, by improving the current set of EPC approved output gap closure rules4 (to avoid 
breaks  in  the  forecasts  between  the  short  and  medium  term;  to  ensure  symmetry;  and  to 
produce  forecasts  for  most  of  the  demand  side  variables  which  Member  States  are  asked  to 
submit in their annual SCP updates)5. 

Regarding the second question, the official methodology's filtering and smoothing procedures need to 
be  kept  in  mind  when  assessing  the  impact  on  output  gap  and  structural  balance  calculations  from 
allowing medium term forecasts to directly influence the short run calculations6. If we were to change 
from only allowing COM's short run forecasts to be used in the potential growth rate calculations, to 
also include medium term forecasts, this change would result in non-negligible backward revisions to 
the  short  run  output  gap  and  structural  balance  estimates.  This  is  due  to  the  fact  that  the  EU's  PF 
method smoothes in any changes over both historical and future years to avoid an abrupt break in the 
potential  output  (and  consequently  in  the  output  gap  /  structural  balance)  series.  Consequently,  if  a 
country, in their SCP updates, uses a judgemental forecast with a time horizon beyond COM's short 
run forecasts, they will end up producing very different potential output and output gap paths to those 
produced  by  the  official  EU  methodology.  The  evidence  in  this  paper  suggests  that  there  is  a 
significant  risk  of  introducing  an  additional  bias  in  the  structural  balance  calculations  from 
lengthening the forecast horizon permitted to be taken into account in the calculations. Currently, due 
to the optimistic bias in the second year of COM's short term forecasts, the EU's PF methodology is 
producing  slightly  optimistic  structural  balance  estimates  on  a  persistent  basis.  This  bias  would 
increase dramatically (it could potentially more than double) if the PF method also included medium 
term actual GDP growth forecasts.  

4 The 20 May 2011 EPC meeting endorsed the following operational rules for closing the output gap. Firstly, the default rule 
is that the output gap is closed at the end of the medium term. Secondly, in circumstances where the output gap is small at 
the end of the short term forecasts, the gap could be closed by 0.5 percentage points a year until the gap is closed. Finally, 
when an output gap is particularly large (i.e. more than double the EU average), a longer period of closure could be allowed, 
up to a maximum of two additional years. 

5 Improving the output gap closure rules would lead to a more sensible cyclical pattern of actual GDP growth rate forecasts 
over the medium term. Since we are in effect talking about a cyclical pattern around a potential output line, it is somewhat 
surprising that the current rules only allow the gap to be closed. It should be possible to forecast a positive or negative output 
gap depending on where a country is in the cycle. The data should be allowed to decide the path. In addition, no matter what 
output gap closure rule determined path for actual GDP is finally agreed upon, it would not have any knock-on effects on the 
short term potential output forecasts since it would be 100% driven by the cycle / by the demand side. 

6 As stated in the main text, the choice to use a longer judgemental forecast horizon for actual output as an input into the 
potential output and output gap calculations, not only affects the potential output results at the end of the sample, but also the 
estimates for T and T+1, due to the backward smoothing of the series.  

7 

 
 
 
 
 
                                                            
 
 
 
1.  INTRODUCTORY REMARKS  

Whilst  conscious  of  the  specificities  of  the  EU's,  rules  based,  fiscal  policy  surveillance  framework, 
this paper tries to examine two questions of a more general nature– firstly, how does the EU's model 
based, medium term, actual GDP growth forecasting methodology compare, in terms of accuracy and 
biasedness,  with  the  medium  term  forecasting  methodologies  used  by  the  EU's  member  states  and 
secondly should medium term actual GDP growth forecasts be allowed to be included in the potential 
growth, output gap and structural balance calculations used in the EU's fiscal surveillance procedures. 
In answering these questions, the following two-pronged strategy was followed:  

  Firstly,  a  survey  was  carried  out  of  the  medium  term  forecasting  methods  used  by  the  28 
Member States in generating their annual Stability and Convergence Programme (SCP) T+4 
projections  (specifically  the  medium  term  actual  GDP  growth  forecasts  for  T+3  and  T+4) 
which are submitted to the Commission as part of their SCP updates7. Countries were asked 
to  categorise  the  forecasting  methods  used  in  generating  these  medium  term,  actual  GDP 
growth, forecasts as either being similar to the rules based PF method (i.e. where forecasts are 
100%  model  generated,  drawing  on  both  supply  side  (potential  growth)  and  demand  side 
(output  gap  closure  rules)  developments  in  each  EU  economy))  or  as  being  generated  by 
“expert judgement” dominated forecasting methods. 

  Secondly, assessing the theoretical and empirical evidence to see if the EU should consider 
replacing  its  model  driven,  medium  term,  forecasting  methodology  with  a  more  judgement 
driven approach. In this context, the paper provides a short review of the literature and of the 
approach  taken  by  other  international  institutions.  This  is  followed  by  an  empirical 
assessment  of  the  accuracy  and  biasedness  of  the  PF’s,  model  based,  medium  term  actual 
GDP  growth  forecasts  compared  with  the  SCP  forecasts  from  the  Member  States,  and  the 
knock-on implications if these medium term forecasts were allowed to be used in calculating 
the output gap and structural balance estimates used in fiscal surveillance. In the final section, 
on the basis of an OGWG endorsed set of evaluation criteria, an attempt is made to reach an 
overall assessment as to whether it is preferable that the EU continues to calculate its output 
gap  and  structural  balance  estimates  using  only  the  Commission's  short  run,  judgemental, 
forecasts  or  whether  the  fiscal  surveillance  process  would  be  enhanced,  or  undermined,  by 
allowing  medium  term  judgemental  forecasts  to  be  taken  into  consideration  in  the 
calculations8. 

7 The 2017 edition of the Vade Mecum on the Stability and Growth Pact explains that, in accordance with Regulation (EC) 
1466/97, Member States are required to submit, annually, SCPs to the Council and the Commission in April of each year. 
The  function  of  the  SCPs  is  to  allow  the  Commission  and  the  Council  to  assess  compliance  with  the  MTO  and  the 
adjustment  path  towards  it,  including  compliance  with  the  expenditure  benchmark.  In  order  for  such  an  assessment  to  be 
made, a range of economic and budgetary data must be included in the SCPs, as set out in the tables annexed to the Code of 
Conduct on the SGP, which have been jointly agreed by the Member States and the Commission in Council committees. The 
forecasts contained in the SCPs must be prepared in a sound and realistic manner, consistent with Directive 2011/85/EU on 
the requirements for budgetary frameworks of the Member States, and should therefore be based on the most likely macro-
fiscal  scenario  or  a  more  prudent  one.  As  a  result  of  the  Two  Pack,  euro  area  Member  States  must  base  their  Stability 
Programmes  on  macroeconomic  forecasts  produced  or  endorsed  by  an  independent  body.  For  all  countries,  as  part  of  the 
SCPs,  both  the  macroeconomic  and  budgetary  forecasts  must  be  compared  with  the  most  recently  available  Commission 
forecasts  and,  if  appropriate,  those  of  other  independent  bodies.  In  addition,  the  output  gap  and  potential  growth  rate 
estimates  are  calculated  according  to  the  agreed  methodologies.  Following  the  ECOFIN  Council  meetings  of  July  2002  / 
May 2004, the production function (PF) approach for the estimation of output gaps constitutes the reference method. 

8 Essentially the question to be answered is whether there is support for such a change firstly from the literature and secondly 
from the empirical evidence, in terms of accuracy / biasedness, and from the additional information content, if any, of having 
an extended forecast horizon. 

8 

 
 
 
 
                                                            
 
 
2.  OVERVIEW OF THE MEDIUM TERM FORECASTING 
METHODS USED BY THE EU'S MEMBER STATES  

A survey of the EU Member States9 was initiated with the objective of obtaining a description of the 
forecasting methods which the 28 Member States use for the set of economic projections which they 
submit annually to the Commission as part of their SCP updates. The survey focussed in particular on 
understanding the differences between the common methodology's, model based, T+3 and T+4 actual 
GDP  growth  forecasts  and  the  equivalent  T+3  and  T+4  forecasts  in  the  SCP's.  Member  State 
representatives  were  asked  to  answer  the  following  two  questions  regarding  the  specific 
methodologies they employed: 

  Firstly,  in  overall  terms,  which  of  the  following  three  broad  categories  would  best  describe 

the forecasting methodology used? : 

a)  Similar to the approach in the Common methodology. 
b)  Expert  and  judgemental  forecasts  for  GDP  growth  and  its  components  up  to  and 

including T+4. 
c)  Other approach. 

  Secondly,  once  the  broad  category  choice  of  a),  b)  or  c)  was  made,  OGWG  members  were 
asked  to  provide  a  few  lines  of  description  of  any  specifically  noteworthy  features  of  the 
broad approach taken.  

The Common Methodology is a hybrid procedure involving expert judgement and models (see Annex 
8.3  and  Havik  et  al.,  2014  for  details).  For  the  short  term  forecast  (two  years),  expert  forecasts  for 
GDP growth are allowed. After that the Production Function model calculates the medium term actual 
GDP  growth  forecasts  in  three  steps.  In  the  first  step,  historical  data  and  the  short  term  (t,  t+1) 
judgemental GDP growth forecasts are used to calculate the potential growth and output gap estimates 
until  the  end  of  the  second  year  using  the  commonly  agreed  PF  methodology.  In  the  second  step, 
based on the estimated trends for the drivers of potential output, a projection for a, no-policy change, 
potential output baseline path up until year 5 is produced by the PF model. In the third and final step, 
the EU's method applies the Economic Policy Committee (EPC) approved rules for closing the output 
gap over the medium term, which when combined with the PF's potential growth projections produce 
the medium term projections for actual GDP growth.  

The Commission’s short and medium term output gap forecasts are 100% determined by the common 
PF methodology – in practice, this means that in order to ensure equal treatment and transparency, it 
is  not  possible  for  a  Commission  expert  to  overrule  the  output  gap  results  produced  by  the  agreed 
rules based framework. 

Table 2.1 gives an overview of the responses which were received from all 28 of the EU's Member 
States and show that about 1/3 of countries were able to make a clear categorisation of their approach 
as  being  consistent  with  the  EU’s  commonly  agreed  approach.  The  remaining  2/3  of  countries  had 
difficulty  in  deciding  if  their  medium  term  forecasting  methods  should  be  categorised  as  either 
judgemental (option  B) or model based but judgement dominated (option  C). The key focus for the 
rest of this paper is to assess, both theoretically  and empirically, the performance of the EU’s rules 
based medium term forecasting methodology relative to the combined performance of the wide range 
of forecasting methodologies employed by the EU's Member States in their SCP updates.  

9  The  survey  of  the  forecasting  methods  used  by  the  28  EU  Member  States  was  carried  out  by  the  EPC's  Output  Gap 
Working Group (OGWG). 

9 

 
 
 
 
                                                            
Table 2.1:   Overview of the Medium Term Forecasting Methods used by the Member States for the 

economic projections underlying their SCP Updates 

Which of the following three broad categories would best describe the 

forecasting methodology used to carry out your T+3 and T+4 actual GDP 

Number of 

Countries 

% of all 28 

Countries 

growth forecasts? 

Option  A:  Commonly  Agreed  Production  Function  Model  :  a  non-

10 

36% 

judgemental approach in which actual GDP growth is 100% determined 

by the potential output path and EPC agreed output gap closure rules 

Option B : Judgemental : Expert and judgemental forecasts for GDP 

growth and its components up to and including T+4 

Option C : Hybrid Methods : a combination of model based forecasts 

(not based on the Commonly Agreed production function method) in 

combination with expert judgement (results are overruled on the basis of 

expert judgement) 

6 

12 

21% 

43% 

3.  POSSIBLE FORECASTING APPROACHES AND HOW DO 
OTHER INTERNATIONAL INSTITUTIONS DEAL WITH THE 
ISSUE OF JUDGEMENTAL VERSUS NON-JUDGEMENTAL 
MEDIUM TERM FORECASTING METHODS   

The  objective  of  this  section  is  to  look  at  the  possible  forecasting  approaches  and  how  other 
international  institutions  deal  with  the  issue  of  judgemental  versus  non-judgemental  medium  term 
forecasting methods and to draw a number of broad conclusions, based on a non-exhaustive review of 
the literature, on the various forecasting approaches: 

  Firstly, several authors argue that expert based judgemental forecasts, which rely heavily on a 
large amount of survey information, are only really helpful for now-casting and for short term 
forecasting  exercises  (e.g.  Gayer  (2006),  Chauvet  and  Potter  (2013),  Stark  (2011)).  They 
accept that the information provided by surveys does not improve forecasts beyond a horizon 
of  1  year  (see,  for  example  Gayer  (2006)).  This  is  also  confirmed  by  forecast  comparison 
exercises,  which  generally  conclude  that  judgemental  forecasts  are  outperformed  by  time 
series and model based approaches after a horizon of two to three quarters (see, for example 
Chauvet  and  Potter  (2013)),  although  it  must  also  be  admitted  that  the  improvement  in 
forecasting performance is often marginal. Stark (2011), in an evaluation of the judgemental 
forecasts  from  the  Federal  Reserve  Bank  of  Philadelphia’s  Survey  of  Professional 
Forecasters, using a sample stretching over 20 years (quarterly data between 1985 and 2007), 
concluded  that  the  survey  based  forecasts  did  “quite  well  at  short  horizons,  and  often 
outperformed  the  forecasts  of  time  series  models…..However,  forecast  accuracy  often 
deteriorates  dramatically  as  the  horizon  lengthens…  with  a  large  degree  of  uncertainty 
surrounding forecasts at long horizons”.  

  Secondly, a pure time series based analysis of GDP growth also seems to be of limited value. 
For  example,  Galbraith  (2003)  finds  that  there  is  no  valuable  information  in  US  GDP  data 

10 

 
 
 
 
 
after  2  quarters.  Even  though  Diebold  et  al  (1997)  claim  that  time  series  methods  provide 
information for longer, several other studies have concluded that time series approaches and 
VAR models are not really suitable for the medium term. For example, Edge and Gürkaynak 
(2011) compared the forecasting performance of time series based methods (VAR) to model 
based methods (Smets Wouters DSGE model) without judgemental interventions, based on a 
horizon of up to 8 quarters using US GDP data. They found that the DSGE model performed 
slightly better10 in the second year. 

  Thirdly, most institutions which regularly carry out medium term projections concentrate on 
model based approaches. As documented by Hofer et al (2010), the models are 'usually based 
on a Keynesian neoclassical synthesis type with a strong emphasis on supply determinants for 
potential output'. Often a two-step procedure is applied. In the first step, the potential output 
path is determined using the supply side of the model (using a PF approach). In a second step, 
demand side developments are driven by an assumption regarding the speed of the closure of 
the  output  gap,  with  this  output  gap  closure  rule  determined  by  either  mechanical  rules  or 
models of the cycle. This two-step approach is currently pursued by the OECD (Turner 2016) 
and in the EU's commonly agreed production function methodology. 

  Finally, one can find literature arguing for a more mixed approach in which several methods 
are  combined.  Some  argue  for  judgemental  adjustments  of  model  based  forecasts,  in  which 
the judgement is often less based on surveys but more on information known to governments, 
central  banks  or  ministries,  which  might  be  hidden  to  the  public.  For  instance  Fildes  and 
Steckler (2002) argue that models may have to be adjusted for a number of related reasons: 
structural  breaks  might  cause  changes  to  the  sample  data  on  which  the  model  was  based, 
changes  in  the  institutional  framework  can  affect  the  parameter  estimates,  revisions  to  data 
can  lead  to  measurement  errors…  They  argue  that  current  information,  outside  the  sources 
used by the model, can give insights into possible inadequacies in the model-based forecast, 
and  should  therefore  be  taken  into  account.  More  recently  Jos  Jansen  et  al.  (2016)  have 
written  that  "judgmental  forecasts  by  professional  analysts  often  embody  valuable 
information  that  could  be  used  to  enhance  the  forecasts  derived  from  purely  mechanical 
procedures".  Others  argue  for  a  combination  of  time  series  and  structural  models,  believing 
that the combination can increase the accuracy of the forecast (e.g. Genre et al. (2013)). Elliot 
and  Timmerman  (2016)  report  in  their  review  on  forecasting  in  economics  and  finance  that 
because  each  model  or  forecasting  method  is  just  an  approximation  of  a  complex  and 
evolving  reality,  it  is  impossible  for  one  method  to  dominate.  According  to  Elliot  and 
Timmerman  this  explains  the  success  of  what  they  call  forecast  combinations.  This  mixed 
approach is also used by the IMF (see below). 

When it comes to other international institutions we can distinguish between the OECD and the IMF: 

  A  2016  review  of  the  OECD’s  forecasting  track  record  (Turner  2016),  explained  that  their 
short  term  forecasts  “rely  heavily  on  expert  judgement  which  is  informed  by  inputs  from  a 
range  of  different  models,  with  forecasts  subjected  to  repeated  peer  review”.  Whilst  this 
approach ensures that the OECD’s “current year GDP growth forecasts exhibit a number of 
desirable  features  including  that  they  are  unbiased,  outperform  naïve  forecasts  (such  as 
sample  means)  and  mostly  identify  turning  points”  (with  the  use  of  high  frequency 
nowcasting indicator models helping to produce a trend improvement in the OECD’s current-
year  forecasting  performance),  the  opposite  conclusion  applied  to  the  OECD’s  one-year-
ahead  forecasts  -  “such  forecasts  are  biased,  often  little  better  than  naïve  forecasts  and  are 
poor  at  anticipating  downturns”.  The  OECD  analysis  concludes  that  “these  weaknesses  in 

10 They find that both approaches outperform judgemental forecasts (the so called Greenbook forecast of the FED staff after 
a forecasting horizon of two quarters). 

11 

 
 
 
 
                                                            
 
forecasting performance beyond the current year underline the importance of increased efforts 
to use models to characterise the risk distribution around the baseline forecast”. 

  Whilst  the  IMF  has  in  the  past  allowed  its  experts  to  use  a  mixture  of  model  based  and 
judgemental  approaches  for  their  medium  term  projections,  more  recently  there  have  been 
attempts to impose a greater degree of cross country comparability by moving towards a more 
model based approach, using the OECD's PF method as a broad guide. This shift was initiated 
by two reports published by the IMF's Independent Evaluation Office (IEO, 2014). Regarding 
medium  term  GDP  growth  forecasts,  the  IEO  states  that  "the  tendency  to  overpredict  GDP 
growth (i.e. an optimistic bias), previously found in other studies, exists for several countries 
in  all  IMF  area  departments  and  regardless  of  development  stage  and  IMF  program 
participation status". The paper argues that "more attention should be placed on constructing a 
unified  view  about  medium-term  growth  potential  in  major  regions  and  countries  to  guide 
desk  economists  in  their  forecasts".  The  main  evaluation  paper  also  stressed  that  the  IMF's 
experts were strongly of the view that "having an estimate of potential output is a critical step 
in  the  process  of  obtaining  medium-term  forecasts  of  GDP  growth  and  other  variables". 
Following publication of these reports, the IMF started to move towards the approach used in 
other international organisations of having centralised processes in place for coordinating the 
medium-term forecasts of its experts in order to provide a model consistent view of potential 
output developments.  

  The IMF's more coordinated approach was introduced for the first time in Spring 2015 when 
the  results  of  a  new  model  based  approach  (which  is  based  on  the  OECD's  Cobb-Douglas 
method)  were  published  in  the  World  Economic  Outlook  (IMF,  2015).  This  was  a  first 
attempt to establish a top-down potential output framework for the IMF's experts. It was not 
intended  to  be  strictly  imposed  on  experts  since  it  is  accepted  that  imposing  one  specific 
method (the approach followed by the OECD and the EU) is much more difficult for the IMF 
to do given its much larger, and more economically heterogeneous, country membership. The 
current  situation  is  that  the  centralised  method  for  calculating  potential  output  is  still  only 
used  as  guidance  for  experts11.  Since  this  common  modelling  approach  is  not  imposed, 
currently IMF teams use a combination of filters and production function models to calculate 
potential growth rates up to T+5. The IMF experts are then expected to close the output gap 
over  the  course  of  the  total  forecast  horizon  (i.e.  by  T+5).  Again,  however,  this  output  gap 
closure rule in T+5 is not applied strictly – the experts are allowed to use their judgement with 
respect to the closure path (teams can use discretion to assume a slower or faster closure) and 
an output gap  close to zero  in T+5  would  be  acceptable (i.e.  within  +/-0.5 percent of  GDP 
growth). However, if the output gap is very different from zero, the expert would be expected 
to provide a justification.  

11 If a similar approach was adopted in the EU (i.e. the results of the PF method would no longer be imposed on ECFIN's 
experts  but  would  simply  be  used  as  guidance  for  their  judgemental  projections),  given  the  equal  treatment  principle 
underlying  the  EU's  common  methodology,  would  additional  rules  /  principles  have  to  be  agreed  to  restrain  the  extent  to 
which ECFIN's experts could deviate from the path implied by the PF's potential output path (i.e. when judging the impact of 
structural  reforms)  or  from  the  commonly  agreed  output  gap  closure  rules  when  assessing  the  likely  demand  side 
developments over the medium term ? In addition, would this degree of discretion be accorded to all EU countries or could it 
only be justified in exceptional circumstances? 

12 

 
 
 
 
 
 
 
 
                                                            
 
4.  COMPARISON OF THE RESULTS OF EU COMMISSION 
VERSUS MEMBER STATE (SCP) FORECASTING 
METHODOLOGIES, BASED ON THE AVAILABLE 
EMPIRICAL EVIDENCE  

This section examines the empirical evidence with respect to the performance of different forecasting 
approaches. It does this on  the basis of an analysis  carried  out to evaluate the properties (accuracy; 
biasedness; volatility) of the EU Commission's T to T+3 actual GDP growth forecasts (with the T+2 
and  T+3  Commission  forecasts  based  on  the  EU's  commonly  agreed  non-judgemental  forecasting 
approach),  compared  with  the  equivalent  T  to  T+3  forecasts  from  the  Member  States'  SCP's.  More 
specifically, this section tries to answer the following three questions:  

  Firstly,  what  is  the  relative  accuracy  and  degree  of  biasedness  of  the  Commission's  actual 

GDP growth forecasts compared with those in the SCP updates? (section 4.1);  

  Secondly, since the ultimate objective of carrying out these Commission and SCP forecasts is 
to enhance the EU's fiscal surveillance process, sub-section 4.2 looks at the forecast bias issue 
and the knock-on effects of the bias on the output gap and structural balance forecasts used in 
the various fiscal surveillance exercises linked to the European Semester process;  

  Finally, sub-section 4.3 examines the question as to whether there are any stability gains from 

extending judgemental forecasts beyond the short run to also cover the medium term. 

4.1   ACCURACY OF THE MEDIUM TERM GROWTH FORECASTS 

How  does  the  accuracy  of  the  Commission's  medium  term  actual  GDP  growth  forecasts  (produced 
with the EU's non-judgemental common methodology) compare with those produced by the Member 
States in their SCP's? Using the Spring 2016 actual GDP growth outturns, for the years 2011-201612, 
as  the  reference,  table  4.1  compares  the  actual  GDP  growth  forecasts  from  three  vintages  of  the 
Commission's  Spring  forecasts  (Spring  2011,  Spring  2012  and  Spring  2013),  with  the  broadly 
equivalent  forecasts  from  the  Member  States  submitted  to  the  Commission  as  part  of  their  SCP 
updates13. Using such a limited number of vintages is clearly an important caveat to bear in mind in 
interpreting the results. Since we base our comparison exercise on the Commission's  (COM) Spring 
forecast,  the  following  notation  applies  in  Table  4.1:  the  current  year  (T)  is  the  first  year  for  the 
forecast;  the  (two-year)  short  term  forecast  covers  the  years  T  and  T+1;  and  the  four  year  forecast 
covers the years T to T+3. Remember that the short term forecast is expert based and created by the 
Commission's  experts,  whilst  the  medium  term  forecast  (T+2  and  T+3)  is  judgement-free  model 
based. 

12 Spring 2016 was available when the research for this note was first produced. Data on actual GDP up until 2015 come 
from Eurostat. For the Spring 2013 vintage, the Commission's Spring 2017 forecasts were used for the actual GDP outturn 
for 2016, which were added at a later stage. 

13 Please note that there can be some differences between the timing of the Commission's Spring forecasts (with the 2011, 
2012 and 2013 forecasts always published in the first two weeks of May) and the submission periods for the SCP's (the 2011 
vintage of SCP's were submitted between March 2011 and July 2011; for the 2012 vintage, all the SCP's were submitted in 
April 2012; for the 2013 vintage, the SCP's were submitted over the period April to May 2013). Please also note that our 
analysis of the SCP forecasts could not go back before 2011 because firstly there are very large gaps in the SCP database 
(including the fact that due to a change to the procedure, no SCP's were submitted in the year 2010) and secondly the fact 
that the submission periods were focussed more on the end of the year, rather than the Spring period generally applied to the 
vintages from 2011 onwards.  

13 

 
 
 
 
                                                            
 
 
Table 4.1: Accuracy of Non-Judgemental vs Judgemental Medium Term (T+2 and T+3) Actual GDP 

growth Forecasts (2011, 2012 and 2013 Forecast Vintages vs Actual Outturns) (EU Weighted 
Average)  

Note:  All  Short  Term forecasts  (T  and  T+1) in  table are based on judgemental forecasting methods. Descriptions of 

options A, B & C are provided in the earlier Table 2.1. The "vintages" referred to in the Table refer to the Commission's 

Spring forecasts from the years 2011, 2012 & 2013 respectively. 

Note:  Data  is  collected  on  actual  GDP  growth  rates  for  individual  countries.  Using  nominal GDP  levels,  the  growth 

rates can be weighted and an average can be calculated. The averages are done on a "like for like" basis –i.e. if a 

country is not available for a specific year in a vintage, it is not included in the weighted average for that specific 

year; if it is available for other years, it is included in the weighted average calculation. This "like for like" rule means 

that particular caution is needed in interpreting the weighted averages for Vintage 2011, since SCP data for some 

years for Germany, France, the Netherlands and Portugal are missing due to gaps in the SCP database. This is less of 

a problem for the other two vintages where only Greece is missing from the SCP database. Croatia only joined the 

EU on 1 July 2013 and consequently is excluded from the analysis. Similarly, the weighted average for each option is 

based on the individual member state data of those member states having stated that they follow option A, B or C 

(see table 2.1 for explanation of the options). 

* For vintage 2013, the outturn for 2016 (i.e. t+3) is taken from the Commission's final Spring 2017  forecasts. This part of 

the  table  had  been  left  blank  for  the  June  2016  note  to  the  OGWG  since  the  outturn  for  2016  wasn't  available  at 

that time. Note also that t = 2013; t+1=2014; t+2=2015; and t+3=2016. 

14 

SCP'sCOMActual Outturn (Spring 2016)SCP's minus OutturnCOM minus OutturnSCP'sCOMActual Outturn (Spring 2016)SCP's minus OutturnCOM minus OutturnSCP'sCOMActual Outturn (Spring 2016)SCP's minus OutturnCOM minus OutturnEU weighted Avgt2.01.92.00.00.00.30.1-0.20.50.40.10.00.4-0.3-0.4EU weighted Avgt+12.32.0-0.32.62.41.61.30.41.21.01.51.41.40.10.0EU weighted Avgt+22.62.20.32.31.82.01.81.40.60.41.91.82.0-0.1-0.2EU weighted Avgt+32.62.41.70.90.62.21.92.00.2-0.12.01.81.90.1-0.1EU weighted AvgAvg t t+12.12.00.81.31.20.90.70.10.80.70.80.70.9-0.1-0.2EU weighted AvgAvg t+2 t+32.62.31.01.61.22.11.91.70.40.12.01.82.00.0-0.2EU weighted AvgAvg t t+32.42.10.91.51.21.51.30.90.60.41.41.31.40.0-0.2Option A Weighted Avgt2.42.42.9-0.6-0.60.50.40.10.40.30.30.20.4-0.1-0.2Option A Weighted Avgt+12.12.10.31.91.81.71.50.41.31.11.51.51.10.40.4Option A Weighted Avgt+22.91.70.62.21.11.91.71.10.80.61.81.91.80.00.0Option A Weighted Avgt+32.61.81.70.90.11.91.71.80.1-0.21.81.71.90.0-0.2Option A Weighted AvgAvg t t+12.22.21.60.60.61.11.00.30.90.70.90.90.80.20.1Option A Weighted AvgAvg t+2 t+32.71.81.21.60.61.91.71.50.50.21.81.81.80.0-0.1Option A Weighted AvgAvg t t+32.52.01.41.10.61.51.30.90.70.51.41.31.30.10.0Option B Weighted Avgt1.51.0-0.41.91.5-1.3-1.4-2.20.90.8-1.0-1.1-1.30.30.1Option B Weighted Avgt+12.21.6-2.04.23.60.50.1-1.31.81.40.71.01.4-0.7-0.3Option B Weighted Avgt+22.41.9-1.33.73.21.61.51.40.20.11.10.92.7-1.5-1.7Option B Weighted Avgt+32.52.31.41.00.82.01.92.7-0.7-0.71.61.52.7-1.1-1.2Option B Weighted AvgAvg t t+11.81.3-1.23.02.5-0.4-0.6-1.81.41.1-0.10.00.0-0.2-0.1Option B Weighted AvgAvg t+2 t+32.42.10.12.42.01.81.72.0-0.2-0.31.41.22.7-1.3-1.4Option B Weighted AvgAvg t t+32.11.7-0.62.72.30.70.50.10.60.40.60.61.4-0.7-0.8Option C Weighted Avgt1.71.61.50.20.20.30.1-0.20.50.30.10.00.8-0.6-0.7Option C Weighted Avgt+12.42.1-0.42.82.51.61.40.80.80.71.71.51.8-0.1-0.4Option C Weighted Avgt+22.62.30.71.91.72.22.01.80.40.22.21.92.10.1-0.2Option C Weighted Avgt+32.72.51.80.90.72.52.22.10.40.22.42.11.70.70.3Option C Weighted AvgAvg t t+12.11.90.61.51.31.00.80.30.70.50.90.71.3-0.4-0.6Option C Weighted AvgAvg t+2 t+32.62.41.21.41.22.42.12.00.40.22.32.01.90.40.1Option C Weighted AvgAvg t t+32.42.20.91.51.31.71.41.10.50.31.61.41.60.0-0.2Option C: Hybrid MethodsOption B: Mainly JudgementalOption A: Commonly Agreed Production FunctionActual GDP Growth ratesSpring 2011 VintageSpring 2012 VintageSpring 2013 Vintage* 
 
In terms of forecasting methods, both the Commission and the Member States unanimously agree that 
the  best  approach  over  the  short  term  (t,  t+1)  is  to  use  an  essentially  judgement  driven  forecasting 
approach.  This  degree  of  unanimity  is  however  absent  over  the  medium  term  (t+2,  t+3)  where  the 
Commission  and  1/3  of  the  Member  States  use  the  non-judgemental,  commonly  agreed,  PF 
methodology to produce a medium term actual GDP growth projection, with the remaining 2/3 of the 
Member States using a wide variety of judgement dominated forecasting methods.  

Table 4.1 provides an overview of the accuracy of the Commission and SCP short and medium term 
forecasts by focusing only on the weighted average for the EU as a whole (the detailed country-by-
country  results  are  given  in  Annex  8.1).  The  main  conclusions  to  be  drawn  from  Table  4.1  are  as 
follows: 

  Firstly, regarding the accuracy of judgemental forecasts over the short run (i.e. t and t+1), the 
accuracy level of the Commission's short run judgemental forecasts for the EU as a whole for 
actual  GDP  growth  is  broadly  equal  to  that  of  the  Member  States,  with  in  fact  the 
Commission  having  slightly  more  accurate  results  over  the  three  vintages  analysed.  This 
overall  conclusion  also  applies  to  the  three  country  groupings  shown  in  the  table  (these 
groupings are taken from the earlier table, with the breakdown of the 28 countries based on 
the  responses  received  from  the  Member  States  to  the  Survey  launched  by  the  EPC 
secretariat). As noted earlier, for the years t and t+1, there are no differences in the forecasting 
methodologies  used  by  the  28  countries  and  the  Commission  -  all  use  expert  driven 
judgemental approaches. 

  Secondly, regarding  the medium term forecasts, here there are significant  differences in the 
forecasting methodologies used by the three country groupings. The option A countries use a 
non-judgemental approach (similar to the 100% rules based EU approach); option B countries 
would  describe  their  forecasting  methods  as  mainly  judgemental;  whilst  option  C  countries 
use medium term forecasting methods which are "model-assisted but judgement dominated". 
The key results are as follows: 

1.  The  Commission's  medium  term  forecasts  (based  on  the  EU's  commonly  agreed 
methodology)  outperform  those  of  all of  the  EU's  Member  States  combined  in  both 
the 2011 and 2012 vintages, whilst the Member States do slightly better for the 2013 
vintage.  

2.  Relative to all three of the individual groupings of Member States (i.e. options A, B 
and C), again the Commission did better for the 2011 and 2012 vintages, and slightly 
worse for the 2013 vintage. 

3.  The breakdown of the Member States's results into options A, B and C unfortunately 
does  not  yield  very  much  in  terms  of  analytical  insights.  Firstly,  it  is  difficult  to 
explain the relatively large degree of differences between the results for the 3 options. 
Secondly, it is surprising to see the extent of the differences between the Commission 
results  and  those  for  option  A  countries  (which  also  apply  the  EU's  agreed  model 
based  approach)  -  one  would  have  expected  to  witness  a  greater  degree  of 
convergence  in  terms  of  forecast  accuracy  but  this  is  not  the  case.  Given  the 
difficulties  in  trying  to  explain  these  differences  across  the  A,  B  and  C  country 
categories,  the  rest  of  the  paper  will  confine  itself  to  comparing  the  Commission 
forecasts with  the SCP  forecasts for all of the Member States combined. This latter 
approach  is  also  justified  given  that  the  Commission  and  SCP  forecasts  are  not 
completely  independent  exercises  since  the  Member  States  SCP  forecasts  are 
expected to be compared with the most recently available Commission forecasts. 

15 

 
 
 
 
 
 
 
4.  Finally, concerning the issue of biasedness, table 4.2 shows that the bias introduced 
from the year t forecasts, both for the Commission and for all of the SCP's combined, 
is  relatively  small  and  consequently  there  is  no  real  debate  for  most  countries  as  to 
whether  to  include  year  t  actual  GDP  growth  forecasts  in  the  potential  growth 
calculations  (this  conclusion  applies  also  to  the  derived  output  gap  and  structural 
balance  calculations).  Current  year  forecasts  appear  to  contain  some  useful 
information for the calculation of output gaps  and since those forecasts are close to 
being unbiased, they are generally not problematic to use in the PF method. However, 
this  is  clearly  not  the  case  with  t+1  forecasts  which  introduce  a  significant  and 
systematic optimistic bias14 in the case of both the Commission and SCP forecasts. As 
will  be  discussed  later  in  section  4.2,  this  short  run  forecast  bias  issue  not  only has 
implications  for  medium  term  actual  GDP  growth  forecasts  but  also,  more 
importantly, it already introduces a significant bias into the short run output gap and 
structural balance calculations. 

In  the  literature  on  evaluating  the  performance  of  different  expert  driven  judgemental  forecasting 
methods, a well-established result is the loss of accuracy in the forecast beyond the first few quarters. 
Consequently,  in  addition  to  evaluating  the  bias  in  the  forecast  over  a  medium  term  horizon,  it  is 
important to first look at differences in the forecast accuracy between the first and second year of the 
forecasts (i.e. t and t+1). For these short run forecast periods, it should be stressed again that there are 
no differences in the forecasting methodologies used by the 28 Member States and the Commission – 
all  the  forecasts  can  be  characterised  as  expert  driven  judgemental  approaches  which  are  strongly 
based on using large information sets, including also survey indicators. 

The  results  shown  in  table  4.2  /  graph  4.1  confirm  the  general  conclusion  from  the  literature  on 
forecast  comparison  exercises  that  the  accuracy  of  judgmental  forecasts  deteriorates  rapidly  beyond 
the first year. For year T, one can see that the Commission and Member State forecasts for the current 
year are generally very accurate and very importantly do not show any bias across vintages - averaged 
across  the  three  vintages,  the  forecast  bias  is  essentially  zero  for  both  the  Commission  and  the 
Member  States.  However,  the  picture  for  T+1  is  very  different  with  significant,  persistent,  forecast 
errors.  Averaged  across  the  three  vintages,  there  is  a  large  optimistic  growth  rate  bias  of  over  1% 
point for the Commission's forecasts (+1.1% points) and of about 1 ¼% points for the Member States 
SCP forecasts (+1.3% points)15. 

14  Please  note  that  this  optimistic  bias  in  the  forecasts  (which  is  essentially  due  to  the  fact  that  expert  judgement  driven 
forecasts invariably never forecast downturns / recessions – this tendency applies not only to short term forecasts but also 
over  medium  term  time  horizons)  is  different  from  the  end  point  bias  issue  attached  to  the  use  of  statistical  filtering 
techniques, especially the end point bias problem with the HP filter. There are very little end point bias issues with the KF 
method.  Consequently,  with  the  method's  smoothing  properties,  the  optimistic  bias  in  the  t+1  forecasts  ends  up  as  an 
optimistic bias in the potential growth calculations. 

15 This is similar to the results from a paper by Pain et al. (2014) which analysed OECD forecasts for GDP growth rates over 
the period 2007-2012 and found that the “average over prediction for OECD countries for the current year was only 0,15 
percentage points, whereas for the one-year-ahead forecast it was 1,5 percentage points, i.e. ten times greater”. 

16 

 
 
 
 
 
                                                            
 
Table 4.2:   Accuracy of Commission's and SCP's Judgemental Short Term Forecasts (2011, 2012 and 
2013 Actual Growth Rate Forecast Vintages vs Actual Spring 2016 Outturns) and Average 
Forecast Bias in T and T+1 (EU, Weighted Average) 

Note:  Individual  countries  GDP  growth  rates  are  weighted  using  nominal  GDP  levels  for  the  vintages  and  for  the 

outturn and the forecast error is calculated as the difference between these EU growth rates. 

Graph 4.1:  Average Forecast Bias for Year T and T+1, Actual GDP growth, Forecasts (Commission vs 

SCP's) (2011, 2012, and 2013 Spring Forecast Vintages) 

Table 4.3 / graph 4.2 then go on to show the forecast accuracy and biasedness of the Commission's 
and the Member State SCP forecasts over the short term as a whole (i.e. T plus T+1) and the medium 
term (i.e. T+2 and T+3) forecasting horizon. These combined short and medium term results confirm 
that  the  significant  positive  growth  rate  bias  shown  earlier  in  the  2nd year  of  the  short  run  forecasts 
persists beyond the short run. On average across all 3 vintages there remains an optimistic bias over 
the medium term for both the Commission's forecasts (+0.4) and the Member State forecasts (+0.7). 
The  bias  persists  either  as  an  upward  bias  in  the  growth  rate,  or  as  an  upward  bias  in  the  level  of 
GDP16. These results suggest that the Commission's strictly model based (non-judgemental) medium 
term  projections  do  significantly  better  compared  with  the  combined  performance  of  the  SCP 
projections (which includes both judgemental and non-judgemental methods), with the Commission's 
average forecast bias being only about half that of the SCP's.  

In addition, it should be stressed that the relative performance of the EU's, model based, medium term 
projections  could  possibly  be  further  improved  by  the  EU's  member  states  agreeing  to  replace  the 
current, purely mechanical, medium term output gap closure rule with a more symmetric time series 

16 The large optimistic bias in the 2011 vintage reflects the very delayed recovery in the EU relative to previous recoveries 
whilst the opposite situation emerged in the 2013 vintage. Regarding the latter vintage, positive headwinds (oil price drop; 
Euro devaluation; and the start of quantitative easing) led to net positive GDP growth surprises of about 1 ppt. Since these 
positive headwinds could not have been foreseen at the time of the Spring 2013 forecasts and since it is not the intention of a 
no policy change forecast to predict policy changes (QE) or changes in exogenous variables (oil prices; exchange rates), the 
small forecasting errors for the 2013 vintage should not be interpreted as an absence of a positive bias. 

17 

Vintage Spring 2011Vintage Spring 2012Vintage Spring 2013Average Bias for 3 VintagesYear T Forecasts0.00.4-0.40.0Year T+1 Forecasts2.41.00.01.1Year T Forecasts0.00.5-0.30.1Year T+1 Forecasts2.61.20.11.3EU Commission Real GDP Growth Forecast Errors (Actual minus Vintage)Stability and Convergence Programme  
 
 
 
 
 
                                                            
driven  output  gap  projection.  This  suggested  change  is  currently  being  examined  in  the  EPC's 
OGWG.  

Table 4.3:  Accuracy of Commission's and SCP's Short and Medium Term, Actual GDP growth, Forecasts 

(2011, 2012 and 2013 Forecast Vintages vs Actual Spring 2016 Outturns) and Average 
Forecast Bias (EU, Weighted Average) 

Note:  Individual  countries  GDP  growth  rates  are  weighted  using  nominal  GDP  levels  for  the  vintages  and  for  the 

outturn and the forecast error is calculated as the difference between these EU growth rates. 

Graph  4.2:  Average,  Actual  GDP  growth,  Forecast  Bias  for  Short  (T,  T+1)  and  Medium  Term  (T+2,  T+3) 
Forecasts (Commission vs SCP's) (2011, 2012, and 2013 Spring Forecast Vintages) 

4.2.  

FISCAL SURVEILLANCE IMPLICATIONS 

In this section we try to estimate the impact of a forecast bias in T+1, T+2 and T+3 on the potential 
output  and output gap estimates in period  T. Because of the smoothing properties of standard trend 
extraction  methods,  including  the  EU's  production  function  approach,  a  bias  in  the  forecast  for  T+j 
has consequences for potential growth estimates in period T (and earlier) and this therefore affects the 
output gap and structural balance estimates in period T.  

Planas and Rossi (2016) have theoretically deducted the impact of adding (judgemental) forecasts at 
the end of the data sample on the estimate of potential growth. They find that the impact depends on 

18 

Vintage Spring 2011Vintage Spring 2012Vintage Spring 2013Average Bias for 3 VintagesJudgementalShortTermForecasts : Average T, T+1 1.20.7-0.20.6Non-Judgemental Medium Term Forecasts : Average T+2, T+3 1.20.1-0.20.4JudgementalShortTermForecasts : Average T, T+1 1.30.8-0.10.7Judgmental and Non-Judgemental Medium Term Forecasts : Average T+2, T+3 1.60.40.00.7Real GDP Growth Forecast Errors (Actual minus Vintage)EU Commission Stability and Convergence Programme  
 
 
 
 
 
 
 
 
 
the  bias  in  the  forecasts  that  are  added.  As  described  in  section  4.1,  the  forecasts  often  have  a 
considerable bias, and we can differentiate between four cases (see Graph 4.3): 

  Case 1: The growth forecast bias persists beyond T+1 until T+3 : 

o  This  was  the  case  in  the  Spring  2011  vintage  on  SCPs,  in  which  the  EU  weighted 
average bias was 2.6% of growth in GDP in T+1, 2.4% in T+2 and 1.3% in T+317. 

  Case 2: The growth forecast bias gets smaller after T+1 :  

o  This  was  the  case  in  the  Spring  2012  vintage  on  SCPs,  in  which  the  EU  weighted 
average bias was 1.2% of growth in GDP in T+1, 0.6% in T+2 and 0.2% in T+318. 

  Case 3: There is only a forecast bias in T+1 : 

o  This  is  the  case  in  all  Commission  Forecasts,  using  the  current  PF  methodology, 
where  only  information  up  to  T+1  is  used  in  the  potential  output  calculations.  This 
was  for  example  visible  in  the  Spring  2011  exercise  in  which  the  EU  weighted 
forecast bias was 2.4% in T+119.  

  Case 4: The forecast bias in T+1 will be completely eliminated by T+3 :  

o  This  bias  is  purely  hypothetical  based  on  the  assumption  that  the  T+3  projection 

would completely offset the T+1 bias in the following two years20.    

17 Notice that in the work of Planas and Rossi (2016) the numbers were slightly different since their analysis was based on a 
preliminary study of the biases. The actual GDP growth rate biases used were 2.7% in T+1, 2.4% in T+2 and 1.3% in T+3.  

18 In order to make the results of Case 2 comparable to Case 1 and to isolate the impact of the profile of the bias over time, 
the forecasting errors were scaled up in order to have the same forecasting error in T+1 as in Case 1. The actual GDP growth 
rate biases used were 2.7% in T+1, 1.2% in T+2 and 0.2% in T+3.  

19 In order to make the results of Case 3 comparable to Case 1 and to isolate the impact of the profile of the bias over time, 
the forecasting errors were scaled up in order to have the same forecasting error in T+1 as in Case 1. The actual GDP growth 
rate biases used were 2.7% in T+1, 0% in T+2 and 0% in T+3.  

20 In order to make the results of Case 4 comparable to Case 1 and to isolate the impact of the profile of the bias over time, 
the actual GDP growth rate biases used were 2.7% in T+1, -0.7% in T+2 and -2.0% in T+3.  

19 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                            
 
Graph 4.3:  Bias in actual GDP growth rates and the impact on potential GDP growth rates (4 cases) 
Bias in actual GDP growth rates and the impact on potential GDP growth rates (4 cases)

The model used by Planas and Rossi (2016) is realistic in terms of the general time series properties 
we  encounter  in  trend  estimation.  Nevertheless  the  results  reported  in  Table  4.4  /  Graph  4.4,  which 
were  produced  using  the  example  of  Italy,  are  only  indicative  and  may  change  slightly  if  this 
experiment  is  conducted  for  country  datasets  other  than  Italy.  The  results  are  not  however  too 
surprising since, for example, they show that the period T potential growth rate bias is largest if the 
actual growth rate bias in the forecast persists beyond T+1 (i.e. case 1). A persistent actual growth rate 
bias of the order of magnitude of case 1 (total bias over the three years of 6.4 ppt) biases the potential 
growth estimate in period T by around 0.4 ppts. The effects of the bias for the output gap / structural 
balance are even more severe since potential growth is also biased upwards in periods T-j, with the 
result that the plausibility of the whole exercise would be severely undermined since the output gap 
bias would be more than twice as high (i.e. 1.2 ppts) compared with the potential growth bias (i.e. 0.4 
ppts). The bias for potential growth and the output gap is somewhat smaller if the actual growth rate 
bias gets significantly smaller (i.e. case 2, total bias of 4.1 ppt). The bias declines further, but is still 
considerable, in the T+1 case (i.e. case 3, total bias of 2.7 ppt) and it is smallest (but remains positive) 
if the T+4 projections correct the T+1 bias (i.e. case 4, total bias of 0 ppt). 

Table 4.4:  Knock-On effect of actual GDP growth forecast Bias on period T output gap and potential 

growth estimates  

Case 1 

Case 2 

Case 3 

(Current PF 
Method) 

Output Gap Bias 

-1.22 

-0.83 

-0.5321 

Potential Growth Rate Bias 

0.43

0.29 

0.18 

Case 4 

-0.14 

0.03 

21 This estimate of the output gap bias is similar to that of an analysis from the Bundesbank in 2012 (Kempkes 2012) which 
looked  at  real-time  output  gaps  for  EU-15  countries  over  the  period  1996-2011  and  found  an  average  downward  bias  of 
about 0,5 percentage points per year.   

20 

 
  
  
                    
Graph  4.4:  Knock-On  effect  of  actual  GDP  growth  forecast  Bias  on  period  T  Output  gap  and Potential 

growth rate estimates  

This analysis shows that a continued forecast bias beyond T+1 has non-negligible consequences for 
potential  growth,  output  gap  and  structural  balance  calculations  in  period  T  and  increases  the  bias 
even  in  situations  where  the  forecast  bias  is  not  increasing  after  t+1.  How  representative  the  bias 
profile identified from the 2011 and 2012 vintages  is, is obviously unclear. However, it may not be 
unrealistic  to  assume  that  because  of  some  mean  reversion  imposed  on  medium  term  GDP  growth 
projections, the growth rate bias in medium term GDP growth projections may not increase over time. 
Since we allow the bias to be largest in T+1, we are confident that we are not biasing the experiment 
in  favour  of  the  official  T+1  methodology.  In  this  regard,  table  4.4  /  graph  4.4  makes  it  clear  that 
whilst policy  makers  would be right to reject changing  the PF  methodology to  include judgemental 
T+2 and T+3 forecasts (since such a change could result in systematic structural balance errors of the 
order of 0.6% points – i.e. roughly 50% of the 1.2% output gap bias), nevertheless they should not be 
complacent about the official T+1 methodology. Whilst only information up to T+1 is currently used 
in the potential output calculations, the optimistic growth rate bias in T+1 could still be resulting in a 
structural balance bias of roughly 0.25 percentage points.  

4.3.  

IMPACT OF A LONGER FORECAST HORIZON 

This  section  looks  at  the  impact  of  a  longer  forecast  horizon  on  Potential  Growth  and  Output  Gap 
revisions.  We  investigate  whether  there  are  any  stability  gains  from  moving  to  medium  term 
judgemental forecasts, e.g. can they reduce procyclicality and the forecast bias. Several EU member 
states are concerned that since potential growth tends to be a bit procyclical, it would be important to 
assess  whether  a  longer  judgemental  forecast  horizon  could  help  in  stabilising  potential  output  and 
therefore also the output gap and structural balance estimates22. Various alternative views exist on the 

22  Could  longer  judgemental  forecasts  stabilise  or  reverse  an  optimistic  or  pessimistic  bias?  Alternatively,  given  that  the 
evidence  for  mean  reversion  is  weak  for  most  EU  countries  (unlike  the  US  with  its  deterministic  trend),  would  longer 
judgemental forecasts just result in a more persistent bias (i.e. waves of optimism or pessimism), such as for example the 
persistent positive bias in the run up to the 2008 crisis? In addition, if a forecaster keeps his actual GDP growth rates at a 
steady rate of 2% for the medium term years, whilst this might arithmetically lead to more stable potential growth rates, it 

21 

 
 
 
 
 
                                                            
advantages and disadvantages of a longer forecast horizon on revisions. Regarding the advantages, it 
can  possibly  be  argued  that  longer  term  projections  may  provide  more  stability,  with  beneficial 
impacts on revisions (this could especially be true in the case of potential output estimates). However, 
one  can  also  argue  that  waves  of  optimism  and  pessimism  may  be  more  pronounced  in  the  case  of 
T+4  projections.  A  typical  example  for  this  latter  view  would  be  a  comparison  of  medium  term 
projections made before the 2009 recession and right after 2009. Whilst before the 2009 recession, the 
medium  term  projection  was  likely  to  be  over-optimistic  for  4  years  instead  of  2  years,  whilst  –  in 
light  of  the  big  recession  in  2009,  the  outlook  for  the  following  years  could  have  been  overly 
pessimistic.  Since  it  is  impossible  to  come  up  with  clear  a  priori  reasons  for  differentiating  the 
revision  properties  of  the  T+2  vs  T+4  approach,  an  empirical  evaluation  based  on  available  data 
vintages seems warranted.  

This section looks at this question by comparing the revision properties of output gaps and potential 
growth  rates  between  the  T+2  (non-judgemental)  and  the  T+4  (judgemental)  methodologies.  In 
particular, we compare projections made for output gaps in period T for period T, T+1, T+2 and T+3, 
with  output  gap  estimates  made  in  later  periods.  In  this  regard,  the  rows  V,  V+1,  V+2  and  V+3  in 
tables 4.5 and 4.6 must be read as follows:  

  Line V compares the projection made in year T (Vintage T) for year T with the output gap / 

potential growth estimate made for period T in T+1 (Vintage T+1).  

  Line  V+1  compares  the  projection  made  in  T  (Vintage  T)  for  T+1  with  the  output  gap  / 

potential growth estimate made for period T+1 in T+1 (Vintage T+1).  

  Line  V+2  compares  the  projection  made  in  T  (Vintage  T)  for  T+2  with  the  output  gap  / 

potential growth estimate made for period T+2 in T+2 (Vintage T+2).  

  Line  V+3  compares  the  projection  made  in  T  (Vintage  T)  for  T+3  with  the  output  gap  / 

potential growth estimate made for period T+3 in T+3 (Vintage T+3).  

Note in particular, for Vintage T and Vintage T+1, we compare not only the forecast made in period T 
for T+1 with the nowcast made in T+1 for T+1 but we also compare the estimate for T, made in T+1 
with the nowcast made in T for T.  

Due to limited data availability, this comparison is only done for the vintages 2011, 2012, 2013, 2014 
and 2015. A comparison for three forecasting years can only be made for the first two vintages. For 
the  following  years  we  have  to  progressively  shorten  the  forecasting  horizon  such  that  for  vintage 
2015 we compare the forecast made for the output gap / potential growth in 2015 for 2015 with the 
projection made in Spring 2016 for the year 2015.  

The  following  tables  show  revisions  of  forecasts  for  output  gaps  and  potential  growth  rates  for  a 
weighted average of all the EU countries (see Annex 8.2 for the country specific results). 

may not necessarily lead to lower output gap revisions. The key question therefore is whether longer judgemental forecasts 
can add any new information? 

22 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                                                                                                                                                         
Table 4.5:  Output Gap Revisions - EU (weighted Average)   T+4 (SCP) vs. T+2 (COM) 

Vintage 2011 

Vintage 2012 

Vintage 2013 

Vintage 2014 

Vintage 2015 

SCP 

COM 

SCP 

COM 

SCP 

COM* 

SCP 

COM 

SCP 

COM 

V 

V+1 

V+2 

V+3 

-0.6 

-1.1 

-0.3 

-0.5 

1.1 

1.6 

1.8 

0.2 

0.5 

0.5 

0.9 

1.2 

1.2 

0.5 

0.6 

0.6 

-1.3 

-0.2 

-0.2 

-0.8 

0.5 

1.1 

0.0 

0.1 

0.0 

0.1 

-0.3 

-0.4 

*  Note:  In  March  2014  the  EPC  approved  a  significant  change  to  the  common  methodology  used  for  calculating 

non-cyclical  unemployment  (i.e.  the  so-called  NAWRU).  This  change  explains  the  apparent  discrepancy  between 

the  small  change  shown  for  potential  growth  in  Table  4.6,  with  the  large  change  to  the  output  gap  shown  in  this 

table. Whilst the potential growth rate effects of this NAWRU change were quite small in the year 2013, the fact that 

the change led to significant backward revisions to the NAWRU, this resulted in much greater output gap shifts. As 

an example, the change to the Spanish NAWRU led to a change in Spain's 2013 output gap of over 4 percentage 

points between the Spring 2013 real time estimate (-4.6%) and the latest Spring 2017 estimate for 2013 (-8.7%). 

Table 4.6:  Potential Growth Revisions - EU (weighted Average)  T+4 (SCP) vs. T+2 (COM) 

Vintage 2011 

Vintage 2012 

Vintage 2013 

Vintage 2014 

Vintage 2015 

SCP 

COM 

SCP 

COM 

SCP 

COM 

SCP 

COM 

SCP 

COM 

V 

V+1 

V+2 

V+3 

Absolute 

0.1 

0.1 

0.2 

0.3 

0.2 

0.0 

-0.1 

0.1 

0.3 

0.1 

0.2 

0.3 

0.2 

0.2 

0.2 

0.2 

0.3 

0.2 

0.1 

0.2 

0.3 

0.4 

0.3 

0.1 

0.1 

0.1 

0.3 

0.1 

0.0 

0.0 

0.0 

0.0 

0.4 

0.1 

0.2 

0.0 

0.0 

0.0 

average 
As  can  be  seen  from  Tables  4.5  and  4.6,  extending  the  judgemental  forecasting  horizon  does  not 
improve the revision properties of the output gap or the potential growth rate calculations : 

  Concerning  output  gaps,  it  appears  that  for  the  medium  term  (V+2  and  V+3)  the  revisions  are 
larger when using the SCP than the Commission. The closing of the output gap was too optimistic 
in both methods, but still more optimistic in the SCP. Looking at the shorter term forecast, both 
methods perform equally well (V). 

  Concerning  potential  output  projections,  the  results  show  that,  on  average,  potential  growth 
forecasts  based  on  both  methods  have  been  biased  upwards  in  general,  but  this  is  more 
pronounced  for  the  SCP  approach  for  all  vintages,  except  for  2015,  where  in  both  approaches 
potential growth for 2015 has not been revised based on data available in Spring 2016. 

In overall terms, given the limited number of vintages used in this analysis, we have to be careful in 
drawing definitive conclusions regarding  the effects of a longer judgemental  forecasting  horizon on 
stabilising the potential growth, output gap and structural balance estimates. Whilst one cannot clearly 
state that a shorter T+2 time horizon is better than a longer T+4 time horizon, one can be comfortable 
with the view that there are no stability advantages from moving to T+4, and that such a move could 
result  in  even  slight  disadvantages.  If  one  just  focuses  on  revisions  to  the  output  gap  and  potential 
growth  estimates,  on  the  basis  of  the  evidence  presented,  a  move  to  T+4  would  not  lead  to  an 
improvement in the stability of either indicator.  

23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.  OVERALL EVALUATION OF THE EU’S, NON-

JUDGEMENTAL, MEDIUM TERM FORECASTING 
METHODOLOGY  

Drawing on the earlier insights from the literature survey and the empirical evidence presented, this 
final section tries to evaluate all of the evidence in order to draw some tentative conclusions regarding 
the EU's commonly agreed, model based, medium term forecasting approach. From a methodological 
perspective,  the  fundamental  question  to  be  answered  in  this  section  is  the  following,  is  it 
economically prudent for the common PF methodology to include 4 years of judgemental forecasts in 
calculating a, no policy change, potential output baseline scenario, or is it more sensible to stick with 
the  current  approach  of  only  allowing  2  years  of  judgemental  forecasts?  In  answering  this  question 
and,  in  particular,  in  evaluating  the  merits  of  the  EU's  non-judgemental  medium  term  forecasting 
methods, it is important at the outset to stress the specificities of the EU's surveillance framework. As 
highlighted in the IMF's IEO report in 2014, these “specificities” need to be clearly understood if a 
balanced evaluation is to take place. In commenting on the EU's approach, the IEO report was very 
conscious  of  the  necessity  of  the  EU  having  much  more  centralised  procedures,  compared  with  the 
level  of  discretion  currently  accorded  to  the  IMF's  experts.  The  report  in  fact  offers  a  convincing 
justification for the current, rules based, EU surveillance framework : 'In the case of the EC, member 
countries  indeed  require  homogeneity  of  forecasting  methods  because  of  the  preeminent  role  of 
cyclically adjusted fiscal balances in the institutional quantitative assessments for these countries. A 
central unit within the EC coordinates the efforts of the teams producing medium-term forecasts for 
individual  economies,  with  a  view  to  assuring  not  only  accounting,  technical,  and  statistical 
consistency, but also analytical and economic consistency.' 

As  well  as  being  aware  of  the  above  IMF  comments,  it  is  clear  that  there  are  a  number  of  other 
important considerations which need to be examined closely in order to reach a balanced judgement 
with  respect  to  the  overall  performance  of  the  EU’s  rules-based  forecasting  methodology.  In  the 
OGWG  discussions  on  the  respective  merits  of  medium  term  judgemental  versus  non-judgemental 
forecasting  methods,  there  was  a  consensus  in  the  working  group  around  four  standard  criteria  for 
evaluating  the  EU’s  rules  based  medium  term  forecasting  methodology:  firstly,  consistency  with 
established “best practice” in the economic literature; secondly, an empirical assessment of the degree 
of bias, if any, in the forecasting approach; thirdly, given the legally binding nature of the EU’s fiscal 
decisions, the importance of any forecasting methodology ensuring transparency and equal treatment 
for all EU countries; and finally the need to avoid excessive complexity.  

Criterion 1: Is the EU’s medium term forecasting method consistent with “Best Practice” in the 
literature and in other international institutions? : A prerequisite for acceptance of a surveillance 
procedure is consistency with common practice, which is usually based on a broad consensus in the 
economic literature. As discussed earlier, the current EU approach to medium term forecasts is fully 
in line with the literature consensus. In addition, all of the international institutions which do medium 
term  projections  use  a  similar  model  based  approach  to  that  of  the  EU  for  their  medium  term 
forecasting  exercises.  The  only  differences  between  the  IMF,  OECD  and  EU  approaches  are  firstly 
the extent to which a single modelling approach is imposed (where the IMF is finding it difficult to 
impose one common approach due to much higher levels of country heterogeneity and data issues for 
developing  countries);  and  secondly,  the  degree  to  which  they  impose  a  single  set  of  output  gap 
closure rules (where both the OECD and the IMF do give a degree of discretion to their experts with 
respect  to  the  closure  path,  whereas  the  EU's  method  strictly  follows  the  EPC  approved  output  gap 
closure rules).  

24 

 
 
 
 
Criterion 2: Does the EU’s model based methodology have a general tendency to produce biased 
(too optimistic or too pessimistic) or unbiased medium term forecasts and would medium term 
judgemental forecasts lead to an improvement or a deterioration with respect to biasedness  ?: 
An  important  criterion  in  evaluating  forecasting  methods  is  the  degree  of  bias  and  persistent  errors 
inflicted  by  the  different  methodological  approaches.  Judgement  based  methods  are  subject  to  a 
possible  (positive)  bias.  There  is  evidence  for  this  from  the  IMF's  2014  evaluation  process  ("WEO 
medium-term forecasts have a tendency to overpredict GDP growth….As in short-term forecasts, the 
existence  of  bias  in  medium-term  forecasts  of  GDP  growth  seems  to  be  largely  a  reflection  of  the 
inability  to  predict  crises  and  large  recessions").  This  IMF  viewpoint  is  also  supported  by  the 
empirical research summarised in this paper, which especially finds evidence of a significant upward 
bias in the second year of the short term forecasts, which persists over the medium term, for both the 
Commission's  own  forecasts  and  from  those  of  the  Member  States  in  their  annual  SCP  forecasts. 
Strictly model based forecasts are also subject to a bias under specific circumstances. For example, if 
GDP growth and its determinants show a downward trend historically, mean reversion usually implies 
some upward bias. If the medium term projection is based on a biased short term forecast, then this 
bias will persist over time.  

The  extent  to  which  both  approaches  inflict  a  bias  is  an  empirical  matter,  with  the  current  paper 
showing that the EU's methodology has an optimistic actual GDP growth forecast bias of roughly ½ a 
%  over  both  the  short  run  and  medium  term  time  horizons,  compared  with  an  equivalent  short  and 
medium  term  bias  of  around  ¾  of  a  %  point  for  the  SCP  forecasts.  The  implications  of  these 
significant  actual  GDP  growth  forecast  biases  on  the  potential  growth  and  output  gap  /  structural 
balance  calculations  depend  crucially  on  understanding  that  the  backward  smoothing  mechanism  in 
the EU's trend extraction methodology ensures that a bias in the forecast for T+j has consequences for 
potential  growth  estimates  in  period  T  (and  earlier)  and  this  therefore  affects  the  output  gap  and 
structural balance estimates in period T. As a result, the longer the period of the forecasts which are 
permitted to be used in the official PF method, the greater the risk of exacerbating the optimistic bias 
which  currently  exists  in  the  PF  method.  As  stressed  earlier,  since  currently  the  EU's  method  only 
allows  the  Commission's  short  run  forecasts  to  be  taken  into  account  in  the  potential  growth  and 
output gap / structural balance calculations, the final optimistic structural balance bias is of the order 
of a ¼ of a % point, whereas changing the method to allow medium term forecasts to also to be taken 
into  account in  the  calculations  could  result  in  an optimistic  structural  balance  bias  of  around  0.6% 
points.  Clearly  therefore  on  the  basis  of  forecast  bias  considerations,  the  evidence  suggests  that  the 
EU should not consider lengthening the time horizon of the Commission forecasts used in the output 
gap / structural balance calculations.  

Criterion 3: How does the EU’s medium term approach compare in terms of transparency and 
equal treatment? : In the context of the EU's fiscal surveillance framework where, unlike the OECD 
and IMF forecasts, there are crucial, legally binding, decisions taken on the basis of the ECFIN expert 
forecasts,  transparency  and  equal  treatment  are  an  especially  important  set  of  evaluation  criteria. 
Purely  judgemental  methods  have  a  hard  time  in  meeting  these  two,  mutually  supportive,  criteria, 
since  the  criteria  /  intuition  underlying  expert  judgement  cannot  be  completely  revealed  by  the 
expert23.  A  purely  model  based  approach  comes  close  to  fulfilling  the  requirements  for  equal 

23 In the context of medium term projections, there is also the risk that experts would either assume that the economy reverts 
back  to  the  historic  trend  (mean  reversion)  or  to  a  trend  estimate  based  on  another  source.  Pritchett  and  Summers  (2014) 
stress that “abnormally rapid growth is rarely persistent” since “regression to the mean is the empirically most salient feature 
of economic growth” and consequently an assumption of reversion to historical growth trends following a period of under- 
or over-performance appears reasonable (although it should be stressed that unlike the US, the evidence for mean reversion 
is much less clearcut for many EU countries). Given the empirical evidence, it is not surprising to find that mean reversion is 
a  feature  not  only  of  macro  models  but  is  also  employed  by  judgement-based  forecasters,  since  there  is  practically  no 
information about the cyclical development of economies beyond a few quarters. Whilst forecasters need to consider some 
form  of  mean  reversion  or  an  output  gap  closure  rule  to  produce  their  forecasts,  the  key  consideration  is  whether  these 
choices  should  be  decided  by  the  expert  or  by  a  rules  based  framework  ?  Experts  must  assume  a  gradual  return  of  the 
economy towards equilibrium which inevitably means having to consider either some form of mean reversion towards the 
economy's long term potential growth rate (this is a difficult judgement call for experts since projecting either an excessively 

25 

 
 
 
                                                            
treatment and transparency. Although, to some extent, even these methods rely on residual judgement: 
for example, when it comes to setting variance bounds for the cycle and trend shocks in the context of 
statistical filters. Nevertheless the criteria for setting priors can be revealed and justified in principle. 
Also efforts have been  made in recent years to increase the level of transparency within the current 
EU methodology by publishing (on the CIRCABC website) the variance bounds and priors used after 
each of the Commission's Winter, Spring and Autumn forecasting exercises. The HP filter constitutes 
the  limit  case  of  a  methodology  which  treats  each  country  equally  on  methodological  grounds. 
However,  here  the  question  arises  as  to  whether  the  underlying  assumptions  concerning  time  series 
properties may not apply to the same extent across countries.  

Criterion  4:  How  does  the  EU’s  approach  compare  in  terms  of  complexity?:  Whilst  the  PF 
approach seems to do relatively well (compared with judgemental approaches) if assessed on the basis 
of  the  three  criteria  postulated  above,  it  is  sometimes  argued  that  the  approach  has  become  too 
complex. This complexity, however, is a reflection of the fact that since the Commission's experts are 
not  allowed  to  use  their  judgement  to  overrule  the  model  projections,  a  degree  of  complexity  is 
unavoidable  in  order  to  produce  credible  forecasts.  This  complexity  is  also  justified  given  the 
empirical results presented in the last section  which showed that the forecasting performance of the 
EU's, purely model-based, approach compares reasonably well with the more judgemental approaches 
used by the Member States in their SCP forecasts. The current degree of complexity is also vindicated 
by a 2015 analysis of the EU's method, which concluded that, in relative terms, the stability, real-time 
reliability  and  financial  crisis  performance  of  the  EU's  PF  method  was  superior  to  those  of  the 
previously used HP filter method, as well as the equivalent OECD and IMF approaches24. Finally, in 
evaluating methods relative to the complexity criterion, one needs to be aware of a trade-off between 
non-  model  based  judgemental  approaches,  which  score  badly  in  terms  of  transparency,  and  model 
based  approaches  which  can  be  more  transparent  but  will  be  much  more  complex  if  they  require 
"fully-fledged  macroeconomic  and  fiscal  projections".  The  latter  variant  would  add  substantially  to 
the current level of complexity in the estimation process (e.g. in terms of the number of variables to 
be  considered,  compared  with  the  current  relatively  parsimonious  specification  of  the  medium  term 
model underpinning the common method). In this context, it is also very important to stress that the 
EU's common methodology is very different from using a judgement dominated approach based on a 
fully fledged macreconomic model since although the final forecast from a fully-fledged model may 
be  model  consistent,  the  forecast  path  is  determined  by  the  judgement  of  the  forecaster,  not  by  the 
model (i.e. the residuals of the model are simply adjusted by the forecaster to produce the path which 
has already been established in the forecaster's own mind). 

rapid or excessively slow return towards trend growth in the last year of the forecast inevitably creates problems in terms of 
revisions for the outer year in subsequent forecasts) or be guided by an output gap closure rule.  

24 Quarterly Report on the Euro Area (QREA), December 2015. 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                                                                                                                                                         
 
 
6.  CONCLUDING REMARKS 

On the basis of four, OGWG endorsed, evaluation criteria, and bearing in mind the specificities of the 
EU's  policy  surveillance  procedures,  this  paper  has  tried  to  answer  two  different,  but  interlinked, 
questions  –  firstly,  how  do  the  EU's,  medium  term,  actual  GDP  growth  rate  forecasts  compare,  in 
terms  of  accuracy  and  biasedness,  with  those  of  the  EU's  member  states  in  their  equivalent  SCP 
forecasts and secondly should medium term actual GDP growth forecasts be allowed to be included in 
the potential growth, output gap and structural balance calculations for period T and T+1.  

  Regarding the first question, the evidence presented in this paper shows that the EU's forecasts are 
equally as good, and arguably better, than the results from the SCP forecasts combined. Whilst the 
Commission's and the SCP forecasts both clearly suffer from a very significant optimistic bias in 
period T+1 which persists over the medium term, the Commission bias is smaller compared with 
that of the SCP's over both the short and medium term. 

  Regarding  the  second  question,  due  to  the  backward  smoothing  properties  of  the  EU's  PF 
methodology for calculating potential growth rates,  and given the persistence of the actual GDP 
growth forecast bias over the short and medium term, the clear conclusion of this paper is that the 
EU's methodology should not be changed to include more forecast information in the output gap / 
structural  balance  calculations.  Since  the  effectiveness  of  the  EU's  fiscal  surveillance  processes 
depend strongly on having an unbiased, no policy change, short and medium term potential output 
baseline upon which prudent fiscal policy decisions can be taken, including information in the PF 
method which leads to unjustified optimism regarding the current or future supply side health of 
the EU's economy must be avoided at all costs. Since the first year of the Commission’s short run 
forecasts  already  suffers  from  an  optimistic  bias,  one  should  avoid  potentially  exacerbating  the 
bias by adding additional forecast years. A prudent approach is warranted in order to limit the risk 
of procyclicality problems with the output gap / structural balance calculations, especially in the 
upswing phase of cycles where most of the large fiscal policy errors tend to occur. 

27 

 
 
 
 
 
 
 
7.  REFERENCES 

Bundesbank  (2014),  "On  the  reliability  of  international  organisations'  estimates  of  the  output  gap", 
Deutsche Bundesbank Monthly Report (April). 

Chauvet, M. and S. Potter (2013), "Forecasting Output", Handbook of Economic Forecasting, Vol 2, 
ed. A. Timmermann and G. Elliott, Elsevier / North Holland, 1-56. 

Diebold,  F.X.  and  L.  Kilian  (1997),  "Measuring  Predictability:  Theory  and  Macroeconomic 
Applications", Technical Working Paper 213, NBER, Cambridge. 

Economic  Policy  Committee  (2001),  "Report  on  potential  output  and 
(EPC/670/01/EN). 

the  output  gap" 

Edge,  R.M.  and  R.  S.  Gurkaynak  (2011),  "How  Useful  are  Estimated  DSGE  Model  Forecasts", 
Finance and Economics Discussion Series, Federal Reserve Board, Washington, D.C. 

Elliott, G. and A. Timmermann (2016) "Forecasting in Economics and Finance", Review in: Annual 
Review of Economics, Vol. 8, October 2016, pp. 81-110. 

Fildes,  R.  and  H.O.  Stekler  (2002)  "The  State  of  Macroeconomic  Forecasting",  Journal  of 
Macroeconomics 24, pp. 435-468. 

Galbraith,  J.W.  (2003),  "Content  Horizons  for  Univariate  Time-Series  Forecasts",  International 
Journal of Forecasting 19 : 43-55. 

Gayer,  C.  (2006),  "Forecast  Evaluation  of  European  Commission  Survey  Indicators",  Journal  of 
Business Cycle Measurement and Analysis, Vol. 2005/2. 

Genre,  V.,  Geoff,  K.,  Meyler,  A.  and  A.  Timmermann  (2013)  "Combining  expert  forecasts:  Can 
anything beat the simple average?", International Journal of Forecasting, Vol. 29, pp. 108-121. 

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  and  output  gaps".  Directorate-General  for  Economic  and  Financial  Affairs. 
Brussels: European Commission. 

Hofer,  H.,  T.  Schmidt  and  K.  Weyerstrass  (2010),  "Practice  and  Prospects  of  Medium-Term 
Economic Forecasting", Ruhr Economic Papers, No. 177. 

IEO  (2014),  "IMF  Forecasts  :  Process,  Quality,  and  Country  Perspectives",  Independent  Evaluation 
Office (IEO) of the International Monetary Fund. 

IEO  (2014),  "An  Assessment  of  IMF  Medium  Term  Forecasts  of  GDP  Growth",  Independent 
Evaluation Office (IEO) of the International Monetary Fund. 

IMF World Economic Outlook (2015), Chapter 3 "Where are we headed? Perspectives on Potential 
Output". 

Jos  Jansen,  W.,  Jin  X.,  de  Winter,  J.M.  (2016)  "Forecasting  and  nowcasting  real  GDP:  Comparing 
statistical models and subjective forecasts", International Journal of Forecasting 32, pp. 411-436. 

28 

 
 
Kempkes, G (2012), "Cyclical adjustment in fiscal rules : some evidence on real-time bias for EU-15 
countries", Bundesbank Discussion Paper No15/2012. 

Mc Morrow, K., W. Roeger, V. Vandermeulen and K. Havik (2015) "An assessment of the real time 
reliability of the output gap estimates produced by the EU's Production Function methodology", Joint 
EPC-ECFIN-JRC Workshop. 

Pain, N., et al. (2014), "OECD forecasts during and after the financial crisis: a post mortem", OECD 
Economics Department Working Papers No.1107. 

Planas, C. and A. Rossi (2016), "Judgemental forecasts and revision errors in signal extraction", paper 
presented to Output Gap Working Group in June 2016. 

Pritchett,  L  and  L.  Summers  (2014),  "Asiaphoria  Meets  Regression  to  the  Mean",  NBER  Working 
Paper No. 20573. 

Stark,  T  (2010),  "Realistic  Evaluation  of  Real-Time  Forecasts  in  the  Survey  of  Professional 
Forecasters", Federal Reserve Bank of Philadelphia. 

Turner,  D.  (2016)  “The  use  of  models  in  producing  OECD  macroeconomic  forecasts”,  OECD 
Economics Department Working Papers No.1336. 

29 

 
 
 
 
 
 
8.  ANNEXES   

8.1.   ACTUAL GROWTH RATE REVISIONS: COMMISSION VERSUS MEMBER STATES (SCP 

UPDATES) 

The  tables  in  this  annex  show  the  growth  in  output  (Yg)  forecast  by  the  Member  States  in  the 
framework of the SCP and by ECFIN in their Spring forecast round. This output growth is for each 
vintage compared to the Spring 2016 forecast which contains the realised output growth25. For each 
vintage  the  last  two  columns  report  the  difference  between  the  forecast  and  the  realised  value,  and 
thus a smaller (absolute) value shows smaller revisions over time and thus a more correct estimate of 
the output growth. We look at vintage Spring 2011, for the years t:2011 until t+3:2014, vintage Spring 
2012 for the years t:2012 until t+3:2015, and vintage Spring 2013, for the years t:2013 until t+3:2016. 
Since we are using the Spring forecasts, the t value is also a forecast, and can be considered  as the 
first  year  forecast.  Therefore  t+3  is  in  fact  the  fourth  year  forecast  (t+4).  The  next  lines  give  an 
average for t and t+1, for t+2 and t+3 and then for the whole four year forecast period (t-t+3). This is 
repeated for all countries. 

Although countries show different results some conclusions can be drawn: 

  The differences (errors) in actual output growth are much larger as of t+1. For t they are, for most 

countries and most vintages, relatively small.  

  The  differences  seem  to  be  smaller  for  most  countries  for  the  2013  vintage  than  for  the  two 
previous  vintages.  This  might  be  related  to  the  fact  that  the  first  year  of  the  oldest  vintage  is 
longer ago than that of the last vintage, and as time passes by there is a larger chance for revisions 
(e.g.  because  of  data  revisions  and  added  data  points  at  the  end  of  the  sample).  In  addition,  as 
discussed  in  footnote  16,  the  small  forecasting  errors  for  the  2013  vintage  should  not  be 
interpreted as an absence of a positive bias. 

  Comparing the Member State and ECFIN forecasts, we can see that the differences  are often in 
the same direction and of the same size. It seems that both the Member States and ECFIN often 
make the same mistake, although the errors made by ECFIN are often slightly smaller.   

25  Note  :  For the  Spring  2013  vintage,  the  Commission's Spring  2017  forecasts were  used  for  the actual  GDP outturn  for 
2016. 

30 

 
 
 
 
 
 
                                                            
31 

MSECFIN2016_IMS minus 2016_IECFIN minus 2016_IMSECFIN2016_IMS minus 2016_IECFIN minus 2016_IMSECFIN2016_IMS minus 2016_IECFIN minus 2016_IATt2.52.42.8-0.3-0.40.40.80.8-0.40.01.00.60.30.70.3ATt+12.02.00.81.31.21.41.70.31.11.31.81.80.41.41.4ATt+22.11.70.31.71.42.01.60.41.61.32.01.80.91.11.0ATt+32.21.80.41.81.52.21.40.91.30.51.81.41.50.3-0.1ATAvg t t+12.32.21.80.50.40.91.20.50.40.71.41.20.31.00.8ATAvg t+2 t+32.11.80.31.81.42.11.50.61.50.91.91.61.20.70.4ATAvg t t+32.22.01.11.10.91.51.40.60.90.81.61.40.80.90.6BEt2.02.41.80.20.60.10.00.2-0.1-0.20.20.00.00.20.0BEt+12.32.20.22.12.11.31.20.01.31.21.51.21.30.2-0.2BEt+22.11.80.02.11.81.71.51.30.40.11.61.51.40.20.1BEt+32.31.91.31.00.51.81.51.40.40.21.71.61.20.50.3BEAvg t t+12.22.31.01.21.30.70.60.10.60.50.90.60.70.2-0.1BEAvg t+2 t+32.21.90.71.51.21.81.51.40.40.11.71.51.30.30.2BEAvg t t+32.22.10.81.31.21.21.10.70.50.31.31.11.00.30.1BGt3.62.81.62.01.21.40.50.21.20.31.00.91.3-0.3-0.4BGt+14.13.70.23.93.42.51.91.31.20.71.81.71.50.30.1BGt+24.43.11.33.11.83.52.61.52.01.02.92.33.0-0.1-0.6BGt+34.23.31.52.71.74.02.93.01.0-0.13.42.53.40.0-0.9BGAvg t t+13.93.20.92.92.32.01.20.81.20.51.41.31.40.0-0.1BGAvg t+2 t+34.33.21.42.91.83.82.72.31.50.53.22.43.2-0.1-0.8BGAvg t t+34.13.21.22.92.02.92.01.51.30.52.31.82.30.0-0.5CYt1.51.50.41.11.1-0.5-0.8-2.41.91.6-8.7-8.7-5.9-2.8-2.7CYt+12.52.4-2.44.94.80.50.3-5.96.46.3-3.9-3.9-2.5-1.4-1.4CYt+22.71.6-5.98.77.51.01.7-2.53.54.21.2-0.51.6-0.4-2.1CYt+33.01.5-2.55.54.01.51.81.6-0.10.31.9-0.22.8-0.9-3.0CYAvg t t+12.01.9-1.03.03.00.0-0.2-4.24.24.0-6.3-6.3-4.2-2.1-2.1CYAvg t+2 t+32.91.5-4.27.15.81.31.7-0.51.72.21.6-0.32.2-0.7-2.5CYAvg t t+32.41.7-2.65.04.40.60.8-2.32.93.1-2.4-3.3-1.0-1.4-2.3CZt1.92.02.0-0.10.00.20.0-0.91.10.90.0-0.4-0.50.50.1CZt+12.32.9-0.93.23.81.31.5-0.51.82.11.21.62.0-0.8-0.3CZt+23.32.3-0.53.82.82.22.82.00.20.82.12.14.2-2.1-2.1CZt+34.02.42.02.00.42.83.04.2-1.4-1.22.62.12.40.2-0.3CZAvg t t+12.12.40.51.61.90.80.8-0.71.51.50.60.60.7-0.1-0.1CZAvg t+2 t+33.72.30.72.91.62.52.93.1-0.6-0.22.42.13.3-1.0-1.2CZAvg t t+32.92.40.62.21.71.61.81.20.40.61.51.42.0-0.5-0.7DEt2.32.63.7-1.4-1.10.70.70.40.30.30.40.40.30.10.1DEt+11.81.90.41.41.51.61.70.31.31.41.61.81.60.00.2DEt+21.61.51.60.0-0.11.41.61.7-0.3-0.1DEt+31.61.31.7-0.1-0.41.41.01.9-0.5-0.9DEAvg t t+12.12.32.00.00.21.21.20.40.80.81.01.10.90.10.2DEAvg t+2 t+31.61.41.60.0-0.31.41.31.8-0.4-0.5DEAvg t t+32.12.32.00.00.21.41.31.00.40.31.21.21.4-0.2-0.2DKt1.81.71.20.60.61.21.1-0.11.31.20.70.7-0.20.91.0DKt+11.81.5-0.11.91.61.51.4-0.21.71.61.61.71.30.30.4DKt+22.11.6-0.22.31.81.81.21.30.50.01.72.81.20.51.6DKt+31.81.71.30.50.52.21.41.21.10.22.43.11.31.11.8DKAvg t t+11.81.60.51.31.11.41.3-0.21.51.41.21.20.50.60.7DKAvg t+2 t+32.01.70.51.41.12.01.31.20.80.12.12.91.20.81.7DKAvg t t+31.91.60.51.41.11.71.30.51.10.81.62.10.90.71.2EEt4.04.97.6-3.6-2.71.71.65.2-3.5-3.63.03.01.61.41.5EEt+14.04.05.2-1.2-1.23.03.81.61.42.23.64.02.90.71.1EEt+23.62.01.62.00.43.42.82.90.5-0.13.53.01.12.41.9EEt+33.62.22.90.7-0.83.52.81.12.41.73.52.61.61.91.0EEAvg t t+14.04.46.4-2.4-2.02.42.73.4-1.0-0.73.33.52.21.11.3EEAvg t+2 t+33.62.12.21.4-0.23.52.82.01.50.83.52.81.32.21.5EEAvg t t+33.83.24.3-0.5-1.12.92.72.70.20.13.43.21.81.61.4Vintage 2013Yg, like with likeVintage 2011Vintage 2012 
 
32 

MSECFIN2016_IMS minus 2016_IECFIN minus 2016_IMSECFIN2016_IMS minus 2016_IECFIN minus 2016_IMSECFIN2016_IMS minus 2016_IECFIN minus 2016_IELt-3.0-3.5-9.16.15.6ELt+11.11.1-7.38.48.4ELt+22.11.6-3.25.34.8ELt+32.11.90.71.41.3ELAvg t t+1-1.0-1.2-8.27.37.0ELAvg t+2 t+32.11.8-1.33.43.0ELAvg t t+30.60.3-4.75.35.0ESt1.30.8-1.02.31.8-1.7-1.8-2.60.90.9-1.3-1.5-1.70.40.2ESt+12.31.5-2.64.94.10.2-0.3-1.71.91.30.50.91.4-0.9-0.4ESt+22.42.0-1.74.13.61.41.51.40.00.10.90.33.2-2.3-2.9ESt+32.62.41.41.21.01.82.03.2-1.4-1.21.30.93.2-1.9-2.3ESAvg t t+11.81.1-1.83.62.9-0.8-1.0-2.11.41.1-0.4-0.3-0.2-0.2-0.1ESAvg t+2 t+32.52.2-0.22.72.31.61.72.3-0.7-0.61.10.63.2-2.1-2.6ESAvg t t+32.21.7-1.03.12.60.40.30.10.40.30.40.21.5-1.2-1.4FIt3.63.72.61.01.10.80.8-1.42.22.20.40.3-0.81.21.0FIt+12.72.6-1.44.14.01.51.6-0.82.32.41.61.0-0.72.31.7FIt+22.42.6-0.83.23.32.11.4-0.72.82.12.11.40.51.60.9FIt+32.12.4-0.72.83.11.91.40.51.40.91.71.51.40.30.1FIAvg t t+13.23.10.62.62.61.21.2-1.12.22.31.00.6-0.71.71.4FIAvg t+2 t+32.32.5-0.73.03.22.01.4-0.12.11.51.91.41.00.90.5FIAvg t t+32.72.8-0.12.82.91.61.3-0.62.21.91.51.00.11.30.9FRt2.01.82.1-0.1-0.30.70.50.20.50.30.1-0.10.7-0.6-0.8FRt+11.81.30.71.10.71.21.10.21.00.9FRt+22.02.00.21.81.82.02.31.20.81.1FRt+32.02.01.20.80.82.02.41.20.81.2FRAvg t t+12.01.82.1-0.1-0.31.20.90.40.80.50.70.50.40.20.1FRAvg t+2 t+32.02.00.71.31.32.02.31.20.81.2FRAvg t t+32.01.82.1-0.1-0.31.61.40.51.10.91.31.40.80.50.6HRtHRt+1HRt+2HRt+3HRAvg t t+1HRAvg t+2 t+3HRAvg t t+3HUt3.12.71.81.31.00.1-0.3-1.71.81.40.70.21.9-1.2-1.7HUt+13.02.6-1.74.74.31.61.01.9-0.3-0.91.91.43.7-1.8-2.3HUt+23.20.41.91.3-1.52.50.73.7-1.2-3.02.31.82.9-0.6-1.2HUt+33.30.53.7-0.4-3.12.50.72.9-0.4-2.22.52.02.00.50.0HUAvg t t+13.12.70.03.02.70.90.40.10.70.31.30.82.8-1.5-2.0HUAvg t+2 t+33.30.52.80.5-2.32.50.73.3-0.8-2.62.41.92.40.0-0.6HUAvg t t+33.21.61.41.70.21.70.51.70.0-1.21.91.32.6-0.8-1.3IEt0.80.62.6-1.8-1.90.70.50.20.50.41.31.11.4-0.1-0.4IEt+12.51.90.22.31.82.21.91.40.80.52.42.25.2-2.8-3.0IEt+23.00.71.41.6-0.73.00.45.2-2.2-4.82.81.57.8-5.0-6.3IEt+33.01.65.2-2.2-3.63.01.07.8-4.8-6.82.72.05.2-2.5-3.2IEAvg t t+11.71.31.40.3-0.11.51.20.80.70.41.91.63.3-1.5-1.7IEAvg t+2 t+33.01.23.3-0.3-2.23.00.76.5-3.5-5.82.81.86.5-3.8-4.7IEAvg t t+32.31.22.30.0-1.12.21.03.6-1.4-2.72.31.74.9-2.6-3.2ITt1.11.00.60.50.4-1.2-1.4-2.81.61.4-1.3-1.3-1.70.40.4ITt+11.31.3-2.84.14.10.50.4-1.72.22.21.30.7-0.31.61.1ITt+21.51.3-1.73.33.01.01.0-0.31.31.41.51.30.80.70.5ITt+31.61.3-0.32.01.71.21.20.80.40.51.31.40.90.40.6ITAvg t t+11.21.1-1.12.32.3-0.4-0.5-2.31.91.80.0-0.3-1.01.00.7ITAvg t+2 t+31.61.3-1.02.62.41.11.10.20.90.91.41.30.80.60.5ITAvg t t+31.41.2-1.12.52.30.40.3-1.01.41.40.70.5-0.10.80.6Yg, like with likeVintage 2011Vintage 2012Vintage 2013 
 
33 

MSECFIN2016_IMS minus 2016_IECFIN minus 2016_IMSECFIN2016_IMS minus 2016_IECFIN minus 2016_IMSECFIN2016_IMS minus 2016_IECFIN minus 2016_ILTt5.85.06.0-0.2-1.12.52.43.8-1.3-1.43.03.13.5-0.6-0.4LTt+14.74.73.80.90.93.73.53.50.20.03.43.63.00.40.5LTt+23.71.03.50.2-2.53.72.53.00.7-0.64.31.91.62.70.3LTt+33.41.93.00.4-1.13.42.41.61.80.84.01.92.31.7-0.3LTAvg t t+15.34.84.90.3-0.13.13.03.7-0.6-0.73.23.33.3-0.10.0LTAvg t+2 t+33.61.53.30.3-1.83.62.42.31.20.14.11.91.92.20.0LTAvg t t+34.43.14.10.3-1.03.32.73.00.3-0.33.72.62.61.00.0LUt3.23.42.60.60.91.01.1-0.81.81.91.00.84.3-3.3-3.5LUt+13.53.8-0.84.34.72.12.14.3-2.2-2.22.21.64.1-1.9-2.5LUt+23.73.54.3-0.6-0.83.31.84.1-0.8-2.31.71.54.8-3.1-3.4LUt+34.03.64.1-0.1-0.44.12.04.8-0.7-2.93.41.74.2-0.8-2.5LUAvg t t+13.43.60.92.52.81.61.61.7-0.2-0.21.61.24.2-2.6-3.0LUAvg t+2 t+33.93.64.2-0.4-0.63.71.94.5-0.8-2.62.61.64.5-2.0-2.9LUAvg t t+33.63.62.51.11.12.61.73.1-0.5-1.42.11.44.4-2.3-3.0LVt3.33.36.2-2.9-2.92.02.24.0-2.0-1.84.03.83.01.00.8LVt+14.04.04.00.00.03.73.63.00.70.64.04.12.41.61.8LVt+24.00.93.01.0-2.14.02.42.41.60.04.02.42.71.3-0.3LVt+34.01.72.41.6-0.64.02.62.71.3-0.14.02.62.02.00.6LVAvg t t+13.73.75.1-1.5-1.42.92.93.5-0.7-0.64.04.02.71.31.3LVAvg t+2 t+34.01.32.71.3-1.44.02.52.61.40.04.02.52.31.70.1LVAvg t t+33.82.53.9-0.1-1.43.42.73.00.4-0.34.03.22.51.50.7MTt2.42.01.90.50.11.51.22.8-1.3-1.61.41.44.1-2.7-2.7MTt+12.32.22.8-0.5-0.72.01.94.1-2.1-2.21.61.83.7-2.1-1.9MTt+22.61.64.1-1.5-2.42.00.83.7-1.7-2.91.91.86.3-4.4-4.5MTt+32.81.83.7-0.9-1.92.10.96.3-4.2-5.41.92.25.0-3.1-2.9MTAvg t t+12.42.12.40.0-0.31.81.63.5-1.7-1.91.51.63.9-2.4-2.3MTAvg t+2 t+32.71.73.9-1.2-2.22.10.85.0-3.0-4.21.92.05.7-3.8-3.7MTAvg t t+32.51.93.1-0.6-1.21.91.24.2-2.3-3.01.71.84.8-3.1-3.0NLt-0.7-0.9-1.10.40.2-0.4-0.8-0.50.1-0.3NLt+11.30.7-0.51.81.21.10.91.00.1-0.1NLt+21.62.51.00.61.51.61.72.0-0.4-0.3NLt+31.62.72.0-0.40.71.61.92.2-0.6-0.3NLAvg t t+10.3-0.1-0.81.10.70.40.10.30.1-0.2NLAvg t+2 t+31.62.61.50.11.11.61.82.1-0.5-0.3NLAvg t t+31.01.30.40.60.91.00.91.2-0.2-0.2PLt4.04.05.0-1.0-1.02.52.71.60.91.21.51.11.30.2-0.2PLt+14.03.71.62.42.12.92.61.31.61.32.52.23.3-0.8-1.1PLt+23.74.31.32.43.03.23.63.3-0.10.33.83.73.60.20.0PLt+33.94.03.30.60.73.83.53.60.2-0.14.33.82.71.61.1PLAvg t t+14.03.93.30.70.62.72.71.41.31.22.01.72.3-0.3-0.6PLAvg t+2 t+33.84.12.31.51.93.53.63.50.00.14.13.73.20.90.6PLAvg t t+33.94.02.81.11.23.13.12.40.70.73.02.72.70.30.0PTt-3.0-3.3-4.01.00.8-2.3-2.3-1.1-1.2-1.2PTt+10.60.3-1.11.71.40.60.60.9-0.3-0.3PTt+22.01.60.91.10.71.51.61.50.00.1PTt+32.42.11.50.90.61.82.21.40.40.8PTAvg t t+1-1.2-1.5-2.61.41.1-0.9-0.9-0.1-0.7-0.7PTAvg t+2 t+32.21.81.21.00.61.71.91.40.20.4PTAvg t t+30.50.2-0.71.20.90.40.50.7-0.3-0.2ROt1.51.51.10.40.41.71.40.61.10.71.61.63.5-1.9-1.9ROt+14.03.70.63.43.03.12.93.5-0.4-0.72.22.23.0-0.8-0.7ROt+24.52.93.51.0-0.63.63.73.00.60.72.43.63.8-1.4-0.2ROt+34.72.83.01.7-0.13.93.83.80.10.03.03.84.8-1.8-1.0ROAvg t t+12.82.60.81.91.72.42.12.10.30.01.91.93.2-1.3-1.3ROAvg t+2 t+34.62.93.21.4-0.43.83.73.40.40.42.73.74.3-1.6-0.6ROAvg t t+33.72.72.01.60.73.12.92.70.40.22.32.83.8-1.5-1.0Yg, like with likeVintage 2011Vintage 2012Vintage 2013 
 
34 

MSECFIN2016_IMS minus 2016_IECFIN minus 2016_IMSECFIN2016_IMS minus 2016_IECFIN minus 2016_IMSECFIN2016_IMS minus 2016_IECFIN minus 2016_ISEt4.64.22.71.91.50.40.3-0.30.70.61.21.51.20.00.3SEt+13.82.5-0.34.12.83.32.11.22.10.82.22.52.3-0.10.2SEt+23.61.91.22.40.73.71.72.31.4-0.63.62.34.1-0.5-1.8SEt+32.81.72.30.5-0.53.61.74.1-0.5-2.43.92.33.30.6-1.1SEAvg t t+14.23.31.23.02.21.91.20.51.40.71.72.01.8-0.10.2SEAvg t+2 t+33.21.81.81.40.13.71.73.20.5-1.53.82.33.70.0-1.4SEAvg t t+33.72.61.52.21.12.81.41.80.9-0.42.72.12.70.0-0.6SIt1.81.90.61.21.3-0.9-1.4-2.71.81.3-1.9-2.0-1.1-0.8-0.9SIt+12.22.5-2.74.95.21.20.7-1.12.31.70.2-0.13.0-2.8-3.1SIt+22.33.0-1.13.44.12.22.23.0-0.8-0.91.21.42.9-1.7-1.5SIt+32.83.33.0-0.20.22.22.62.9-0.7-0.31.61.72.5-0.9-0.8SIAvg t t+12.02.2-1.03.03.20.2-0.4-1.92.01.5-0.9-1.01.0-1.8-2.0SIAvg t+2 t+32.63.11.01.62.12.22.43.0-0.8-0.61.41.62.7-1.3-1.1SIAvg t t+32.32.70.02.32.71.21.00.50.60.50.30.31.8-1.6-1.6SKt3.43.52.80.60.71.11.81.5-0.40.31.21.01.4-0.2-0.5SKt+14.84.41.53.32.92.72.91.41.31.42.92.82.50.40.3SKt+24.83.01.43.41.63.62.92.51.10.43.34.33.6-0.30.7SKt+34.83.02.52.30.53.72.83.60.1-0.83.64.43.30.31.1SKAvg t t+14.14.02.21.91.81.92.31.50.40.92.11.92.00.1-0.1SKAvg t+2 t+34.83.02.02.81.03.72.83.10.6-0.23.54.43.40.00.9SKAvg t t+34.53.52.12.41.42.82.62.30.50.32.83.12.70.00.4UKt1.81.72.0-0.2-0.30.80.51.2-0.4-0.70.60.62.2-1.6-1.6UKt+12.72.11.21.50.92.01.72.2-0.2-0.41.81.72.9-1.1-1.1UKt+22.92.82.20.70.72.72.32.9-0.2-0.52.31.82.30.0-0.5UKt+32.93.22.90.00.33.02.72.30.70.32.72.11.80.90.3UKAvg t t+12.31.91.60.70.31.41.11.7-0.3-0.61.21.12.5-1.3-1.4UKAvg t+2 t+32.93.02.50.40.52.92.52.60.3-0.12.51.92.10.4-0.1UKAvg t t+32.62.42.00.50.42.11.82.10.0-0.31.91.52.3-0.4-0.8Yg, like with likeVintage 2011Vintage 2012Vintage 2013 
 
 
 
 
 
 
 
 
 
 
8.2.   POTENTIAL GROWTH AND OUTPUT GAP REVISIONS: COMMISSION VERSUS 

MEMBER STATES (SCP UPDATES) 

The  tables  in  this  annex  show  the  revisions  in  growth  in  potential  output  and  output  gaps  between 
what was forecast by the Member States in the framework of the SCP and by ECFIN in their Spring 
forecast round and the realised values in Spring 2016 (2015 is the latest year with realised data). The 
revisions are given for t to t+3, whenever available, and an average of the absolute error for the four 
years. This is repeated for all countries. 

Although countries show different results some conclusions can be drawn: 

  The differences in potential output growth are  very small and very comparable between ECFIN 

and the Member States, with those of ECFIN being slightly smaller.  

  The  revisions  in  output  gaps  are  larger  than  those  in  potential  output  growth  and  the  errors  in 
those  of  ECFIN  are  mostly  similar  or  smaller  than  the  Member  States  forecasts  (the  exceptions 
being Cyprus, Denmark, Hungary and Latvia). 

35 

 
 
 
 
 
Table 8.1:  Potential GDP growth revisions for vintages 2011 until 2015 

36 

MSECFINMSECFINMSECFINMSECFINMSECFINATV0.40.2-0.20.20.00.00.40.4-0.3-0.1ATV+10.40.30.20.3-0.10.00.40.6ATV+20.50.30.00.00.30.5ATV+30.30.30.60.5ATAvg ABS V V+30.40.20.20.30.10.20.40.50.30.1BEV0.30.50.40.1-0.10.20.0-0.10.0-0.2BEV+10.40.40.50.20.10.10.00.1BEV+20.90.70.40.30.10.1BEV+30.80.80.40.2BEAvg ABS V V+30.60.60.40.20.10.10.00.10.00.2BGV1.81.40.6-0.10.3-0.20.10.2-0.80.0BGV+11.21.50.50.0-0.3-0.50.50.4BGV+21.40.90.6-0.30.30.0BGV+31.30.31.00.2BGAvg ABS V V+31.41.00.70.10.30.20.30.30.80.0CYV1.11.11.81.81.31.3CYV+11.20.92.63.5CYV+2CYV+3CYAvg ABS V V+30.60.51.11.31.31.3CZV1.00.40.41.00.80.10.00.0-0.5-0.5CZV+11.10.50.31.00.4-0.30.0-0.1CZV+21.61.40.80.90.0-0.2CZV+32.31.00.50.8CZAvg ABS V V+31.50.80.50.90.40.20.00.10.50.5DEV0.20.2-0.2-0.10.10.0-0.1-0.2DEV+10.20.3-0.2-0.20.1-0.1DEV+2-0.2-0.5-0.3-0.4DEV+3-0.1-0.7DEAvg ABS V V+30.20.40.20.20.10.00.10.2DKV0.50.50.3-0.40.50.20.80.30.40.0DKV+10.40.30.0-0.3-0.10.10.20.4DKV+20.40.00.2-0.20.10.5DKV+30.30.10.60.1DKAvg ABS V V+30.40.20.30.20.20.30.50.30.40.0EEV-0.3-1.0-1.6-0.40.71.00.50.1-0.60.1EEV+1-0.3-1.0-1.1-0.51.11.40.50.2EEV+2-1.2-1.5-0.2-0.21.30.8EEV+3-0.3-0.40.5-0.3EEAvg ABS V V+30.51.00.90.31.01.10.50.20.60.1ELVELV+1ELV+2ELV+3ELAvg ABS V V+3ESV0.60.4-0.4-0.2-0.2-1.0-0.1-0.10.20.1ESV+11.61.1-0.10.2-0.3-1.2-0.4-0.2ESV+21.92.2-0.20.5-0.7-0.5ESV+31.81.5-0.60.7ESAvg ABS V V+31.51.30.30.40.40.90.30.10.20.1Vintage 2015Vintage 2012Potential GDP Growth, RevisionsVintage 2011Vintage 2013Vintage 2014 
37 

MSECFINMSECFINMSECFINMSECFINMSECFINFIV0.60.80.30.20.50.50.30.10.20.1FIV+10.60.90.40.40.70.60.40.2FIV+20.81.20.90.91.00.7FIV+31.11.50.90.8FIAvg ABS V V+30.81.10.60.60.70.60.40.20.20.1FRV0.40.20.0-0.10.50.00.00.2FRV+10.30.30.0-0.10.40.1FRV+20.20.00.40.1FRV+30.60.1FRAvg ABS V V+30.40.10.10.10.50.10.00.2HRVHRV+1HRV+2HRV+3HRAvg ABS V V+3HUV1.00.30.50.10.0-0.1-0.4-0.5-0.1-0.3HUV+11.50.20.50.0-0.1-0.4-0.6-0.8HUV+21.50.20.4-0.9-0.6-1.1HUV+31.2-0.3-0.1-1.8HUAvg ABS V V+31.30.20.40.70.20.60.50.70.10.3IEV-0.50.20.2-0.3-0.1-0.5-0.5-0.8-1.7-1.3IEV+1-0.20.40.5-0.3-0.3-0.2-0.5-0.8IEV+20.20.4-0.5-0.4-0.7-1.0IEV+3-0.40.0-1.1-1.3IEAvg ABS V V+30.30.20.60.60.40.50.50.81.71.3ITV0.20.40.20.30.40.00.30.40.10.2ITV+10.70.90.00.30.30.10.40.3ITV+20.61.00.30.40.40.3ITV+30.90.90.50.6ITAvg ABS V V+30.60.80.30.40.40.20.40.30.10.2LTV0.2-0.2-0.1-0.4-0.2-0.2-0.20.60.60.5LTV+10.20.0-0.10.0-0.2-0.6-0.10.7LTV+20.0-1.3-0.3-1.1-0.3-0.6LTV+3-0.3-1.2-0.4-1.0LTAvg ABS V V+30.20.70.20.70.20.50.20.60.60.5LUV0.71.20.20.1-0.4-0.5-0.3-0.7-0.6-0.8LUV+10.81.30.10.5-0.5-0.3-0.3-0.7LUV+21.02.2-0.4-0.1-0.9-1.1LUV+30.41.8-0.9-0.7LUAvg ABS V V+30.71.60.40.40.60.60.30.70.60.8LVV-0.6-1.2-0.20.30.60.10.70.70.30.0LVV+1-0.9-1.6-0.3-0.10.50.71.00.7LVV+2-1.2-1.40.3-0.41.31.0LVV+3-0.7-0.91.1-0.2LVAvg ABS V V+30.91.30.50.20.80.60.90.70.30.0MTV0.80.70.2-0.3-0.2-0.3-0.9-1.2-1.1-0.8MTV+10.80.80.2-0.2-0.6-0.3-1.4-1.8MTV+21.10.3-0.3-0.9-2.0-1.8MTV+30.70.1-1.4-2.5MTAvg ABS V V+30.80.50.51.00.90.81.21.51.10.8Potential GDP Growth, RevisionsVintage 2011Vintage 2012Vintage 2013Vintage 2014Vintage 2015 
  
 
38 

MSECFINMSECFINMSECFINMSECFINMSECFINNLV0.20.60.0-0.1-0.10.00.0-0.2NLV+10.50.90.4-0.1-0.4-0.1NLV+20.80.70.10.2NLV+30.50.7NLAvg ABS V V+30.50.70.20.10.20.00.00.2PLV0.30.4-0.6-0.20.1-0.30.00.00.20.2PLV+10.50.50.00.4-0.1-0.60.10.0PLV+20.60.90.1-0.1-0.1-0.6PLV+30.70.20.1-0.3PLAvg ABS V V+30.50.50.20.30.10.50.10.00.20.2PTV1.10.1-0.5-0.50.10.30.2-0.3PTV+11.71.0-0.40.0-0.10.1PTV+21.00.7-0.20.8PTV+30.91.0PTAvg ABS V V+31.20.70.40.40.10.20.20.3ROV-0.9-0.10.50.50.10.30.40.1-0.4-0.3ROV+11.3-0.30.30.30.30.5-0.1-0.2ROV+22.1-0.20.30.50.10.6ROV+32.3-0.20.30.2ROAvg ABS V V+31.70.20.40.40.20.50.30.20.40.3SEV-2.1-2.00.4-0.40.3-0.30.80.5-0.9-0.4SEV+1-2.5-1.70.2-0.2-0.2-0.20.20.3SEV+2-2.5-1.80.3-0.70.50.0SEV+3-2.3-2.21.0-0.6SEAvg ABS V V+32.41.90.50.50.30.20.50.40.90.4SIV0.71.41.00.90.2-0.4-0.9-0.50.30.1SIV+11.61.21.21.3-0.1-0.4-1.2-0.4SIV+22.62.71.00.7-1.4-0.6SIV+32.22.0-0.50.3SIAvg ABS V V+31.81.80.90.80.60.41.10.50.30.1SKV0.90.10.00.20.70.5-0.40.1-0.2-0.2SKV+11.80.30.0-0.21.00.8-0.60.0SKV+21.30.30.80.10.40.7SKV+31.90.70.3-0.1SKAvg ABS V V+31.50.30.30.20.70.70.50.00.20.2UKV2.41.20.40.40.00.00.5-0.20.50.0UKV+11.60.40.50.50.0-0.20.1-0.1UKV+20.80.70.40.10.1-0.4UKV+30.20.60.30.0UKAvg ABS V V+31.20.70.40.20.00.20.30.10.50.0Vintage 2015Potential GDP Growth, RevisionsVintage 2011Vintage 2012Vintage 2013Vintage 2014 
 
  
 
Table 8.2:  Output gap revisions for vintages 2011 until 2015 

39 

MSECFINMSECFINMSECFINMSECFINMSECFINATV-2.5-1.1-0.2-0.30.20.20.60.5-0.7-0.3ATV+1-1.3-0.2-0.10.50.50.31.50.9ATV+2-0.90.40.50.82.31.3ATV+3-0.50.52.21.3ATAvg ABS V V+31.30.50.70.71.00.61.10.70.70.3BEV-1.5-0.1-0.7-0.40.5-0.2-0.40.3-0.3-0.4BEV+10.30.6-0.10.60.5-0.50.20.7BEV+20.61.30.40.20.40.1BEV+31.30.80.20.7BEAvg ABS V V+30.90.70.30.50.50.20.30.50.30.4BGV-1.2-1.2-1.0-1.9-0.1-0.4-1.3-1.4-0.10.0BGV+10.20.4-0.1-0.9-0.30.0-0.6-0.9BGV+21.0-0.30.5-0.3-0.4-0.1BGV+31.50.50.70.0BGAvg ABS V V+31.00.60.60.80.30.21.01.10.10.0CYV0.0-0.90.0-1.64.13.6CYV+11.41.40.94.1CYV+2CYV+3CYAvg ABS V V+30.40.60.21.44.13.6CZV-1.5-1.00.10.1-0.30.0-0.6-0.50.0-0.9CZV+1-0.51.31.01.1-1.2-0.1-1.3-0.5CZV+21.12.80.40.9-2.3-0.8CZV+30.72.1-0.40.1CZAvg ABS V V+31.01.80.50.61.30.31.00.50.00.9DEV-0.9-0.80.20.1-0.10.2-0.4-0.4DEV+10.10.20.20.30.30.4DEV+20.30.30.30.7DEV+30.40.7DEAvg ABS V V+30.40.50.20.30.20.30.40.4DKV0.30.10.72.2-0.20.2-0.3-0.5-0.5-0.1DKV+10.40.21.33.20.00.3-0.1-0.8DKV+21.83.21.43.3-0.10.2DKV+31.93.61.12.4DKAvg ABS V V+31.11.81.12.80.10.20.20.60.50.1EEV-3.0-1.6-1.0-3.20.2-0.1-0.7-0.6-1.30.7EEV+1-0.61.6-0.4-2.01.10.70.5-0.2EEV+2-0.3-4.41.1-1.11.0-0.1EEV+31.4-1.01.4-1.0EEAvg ABS V V+31.32.11.01.80.80.30.60.41.30.7ELVELV+1ELV+2ELV+3ELAvg ABS V V+3ESV1.1-1.02.10.21.03.4-0.1-0.3-0.3-0.1ESV+13.51.13.51.00.54.4-0.8-0.9ESV+27.12.44.94.3-0.82.3ESV+37.85.64.42.6ESAvg ABS V V+34.92.53.72.00.83.40.40.60.30.1Output Gap RevisionsVintage 2011Vintage 2015Vintage 2012Vintage 2013Vintage 2014 
 
40 

MSECFINMSECFINMSECFINMSECFINMSECFINFIV0.2-1.4-1.7-0.10.60.50.40.4-0.3-0.4FIV+11.5-0.6-1.00.80.90.70.80.8FIV+20.90.40.21.61.91.5FIV+32.01.71.22.3FIAvg ABS V V+31.21.01.01.21.10.90.60.60.30.4FRV-1.7-0.5-0.5-0.60.1-0.5-0.2-0.4FRV+1-0.30.7-0.3-0.50.4-0.2FRV+20.01.00.40.1FRV+30.51.3FRAvg ABS V V+30.60.90.40.40.30.30.20.4HRVHRV+1HRV+2HRV+3HRAvg ABS V V+3HUV-0.80.00.41.30.9-0.5-0.8-1.3-0.50.1HUV+10.52.70.72.00.7-1.0-0.8-1.2HUV+21.83.91.50.7-0.3-2.3HUV+32.62.10.3-0.9HUAvg ABS V V+31.42.20.71.20.61.30.81.30.50.1IEV-0.4-0.6-1.30.10.81.10.2-1.1-1.3-0.7IEV+11.60.4-0.91.21.41.8-0.7-1.0IEV+21.7-0.31.21.41.1-0.4IEV+33.80.71.4-0.9IEAvg ABS V V+31.90.51.20.91.11.10.51.01.30.7ITV0.2-0.80.60.2-0.30.30.90.6-0.3-0.6ITV+11.91.02.21.7-0.10.41.11.0ITV+24.52.81.92.01.21.3ITV+34.23.02.82.7ITAvg ABS V V+32.71.91.91.70.50.71.00.80.30.6LTV-0.70.4-0.7-2.00.00.9-2.7-1.1-0.40.3LTV+11.22.60.1-1.60.11.7-0.9-0.8LTV+21.1-0.10.3-0.4-0.70.4LTV+31.30.5-0.2-0.9LTAvg ABS V V+31.10.90.31.20.31.01.80.90.40.3LUV0.2-0.4-0.6-0.40.60.80.11.00.7-0.3LUV+11.91.00.40.30.10.2-0.21.1LUV+23.01.11.40.5-0.90.4LUV+34.01.22.60.8LUAvg ABS V V+32.30.91.30.50.50.50.21.10.70.3LVV-4.4-1.00.5-1.90.30.60.10.00.6-0.3LVV+1-1.32.20.5-2.1-0.20.30.81.2LVV+20.3-1.20.5-2.40.4-0.5LVV+31.2-1.71.2-1.9LVAvg ABS V V+31.81.50.72.10.30.50.50.60.60.3MTV0.00.30.20.1-0.3-0.1-0.3-0.2-1.1-1.1MTV+10.00.50.50.8-0.5-0.3-0.30.2MTV+20.30.60.20.1-0.3-0.5MTV+3-0.3-0.10.0-0.3MTAvg ABS V V+30.10.30.20.30.40.30.30.21.11.1Vintage 2011Vintage 2012Vintage 2013Vintage 2014Vintage 2015Output Gap Revisions 
 
41 

MSECFINMSECFINMSECFINMSECFINMSECFINNLV-0.5-1.20.10.00.10.5-0.8-0.7NLV+10.6-0.60.0-0.1-0.20.3NLV+20.70.0-0.10.2NLV+30.30.7NLAvg ABS V V+30.50.70.10.10.10.40.80.7PLV-0.8-1.01.4-0.2-1.9-1.2-0.4-0.7-0.3-0.1PLV+10.0-0.62.60.9-2.7-1.6-0.2-0.5PLV+22.91.40.50.2-2.6-1.3PLV+30.80.70.30.1PLAvg ABS V V+31.10.91.20.42.41.30.30.60.30.1PTV-0.3-1.10.81.11.11.1-1.4-0.9PTV+11.00.50.60.51.10.8PTV+21.61.31.90.8PTV+32.71.8PTAvg ABS V V+31.41.21.10.81.11.01.40.9ROV1.6-1.6-1.3-1.6-1.0-1.00.20.10.20.2ROV+12.50.9-0.2-0.7-1.5-1.8-0.20.1ROV+22.00.8-0.4-1.0-2.1-1.5ROV+30.90.30.3-0.2ROAvg ABS V V+31.80.90.60.91.51.40.20.10.20.2SEV-0.9-1.1-0.50.3-0.60.4-0.50.0-0.5-0.4SEV+12.00.81.41.0-0.90.4-0.10.1SEV+23.41.51.51.1-0.90.1SEV+32.51.41.00.8SEAvg ABS V V+32.21.21.10.80.80.30.30.10.50.4SIV0.4-1.8-0.6-1.7-1.3-0.60.00.30.0-0.8SIV+11.80.71.2-0.4-1.6-1.0-0.4-0.3SIV+22.81.20.5-0.3-1.3-1.0SIV+31.41.20.8-0.1SIAvg ABS V V+31.61.20.80.61.40.90.20.30.00.8SKV-1.7-0.7-1.7-0.22.00.5-0.3-0.6-1.8-1.5SKV+10.31.50.01.81.60.40.4-0.6SKV+20.93.02.42.91.40.3SKV+32.93.62.62.1SKAvg ABS V V+31.52.21.71.81.70.40.40.61.81.5UKV-1.2-1.50.3-1.3-1.70.0-0.50.0-0.1-0.2UKV+1-0.9-0.41.2-0.6-2.3-0.8-0.60.0UKV+20.9-0.1-0.8-1.1-2.9-1.2UKV+3-0.7-0.3-0.9-1.0UKAvg ABS V V+30.90.60.81.02.30.70.60.00.10.2Output Gap RevisionsVintage 2011Vintage 2012Vintage 2013Vintage 2014Vintage 2015 
 
 
 
 
 
8.3.   A SHORT OVERVIEW OF THE EU'S PRODUCTION FUNCTION METHODOLOGY 

8.3.1.   Main Features of Methodology 

Instead of making statistical assumptions on the time series properties of trends and their correlation 
with the cycle, the production function approach makes assumptions based on economic theory. This 
latter approach focuses on the supply potential of an economy and has the advantage of giving a more 
direct link to economic theory but the disadvantage is that it requires assumptions on the functional 
form  of  the  production  technology,  returns  to  scale,  trend  technical  progress  (TFP)  and  the 
representative  utilisation  of  production  factors.  As  shown  in  the  diagram  below,  with  a  production 
function,  potential  GDP  can  be  represented  by  a  combination  of  factor  inputs,  multiplied  with  the 
technological  level  or  total  factor  productivity  (TFP).  The  parameters  of  the  production  function 
essentially determine the output elasticities of the individual inputs, with the trend components of the 
individual production factors, except capital, being estimated. Since the capital stock is not detrended, 
estimating potential output amounts therefore to removing the cyclical component from both labour 
and TFP.   

42 

CapitalStockWorking Age PopulationLabour ForceLabour PotentialTrend TFPMEASURING POTENTIALOUTPUT USING A PRODUCTION FUNCTION APPROACHCOBB-DOUGLAS PRODUCTION FUNCTIONEXTRACTING THESTRUCTURAL COMPONENTTotal Factor Productivity (TFP)Labour Supply(Employment * Hours Worked)Bivariate Kalman Filter Model(Exploits link between the TFP cycle & the degree of capacity utilisation)Potential EmploymentPotential OutputTrend Participation RateNAWRUTrend HoursPotential Labour Supply 
 
 
Cobb-Douglas Production Function: In more formal terms, with a production function, GDP (Y) is 
represented by a combination of factor inputs - labour (L) and the capital stock (K), corrected for the 
degree of excess capacity (𝑈𝐿, 𝑈𝐾) and adjusted for the level of efficiency (𝐸𝐿 ,𝐸𝐾). In many empirical 
applications,  including  the  Quest  model,  a  Cobb  Douglas  specification  is  chosen  for  the  functional 
form. This greatly simplifies estimation and exposition.  Thus potential GDP is given by: 

(1) 

where total factor productivity (TFP), as conventionally defined, is set equal to : 

(2)      

which summarises both the degree of utilisation of factor inputs as well as their technological level.  
Factor inputs are measured in physical units.  An ideal physical measure for labour is hours worked 
which  we  use  as  our  labour  input.  For  capital  we  use  a  comprehensive  measure  which  includes 
spending on structures and equipment by both the private and government sectors.  

Various assumptions enter this specification of the production function, the most important ones are 
the  assumption  of  constant  returns  to  scale  and  a  factor  price  elasticity  which  is  equal  to  one.    The 
main  advantage  of  these  assumptions  is  simplicity.  However  these  assumptions  seem  broadly 
consistent with empirical evidence at the macro level. The unit elasticity assumption is consistent with 
the relative constancy of nominal factor shares.  Also, there is little empirical evidence of substantial 
increasing / decreasing returns to scale (see, e.g. Burnside et al. (1995) for econometric evidence).   

The output elasticities of labour and capital are represented by 
 respectively. Under the 
assumption  of  constant  returns  to  scale  and  perfect  competition,  these  elasticities  can  be  estimated 
from  the  wage  share.  The  same  Cobb-Douglas  specification  is  assumed  for  all  countries,  with  the 
mean wage share for the EU15 over the period 1960-2003 being used as guidance for the estimate of 
the output elasticity of labour, which would give a value of .63 for 
 for all Member States and, by 
definition, .37 for the output elasticity of capital26.  While the output elasticity for labour may deviate 
somewhat  from  the  imposed  mean  coefficient  in  the  case  of  individual  Member  States,  such 
differences should not seriously bias the potential output results.  

 and 

To  summarise  therefore,  in  moving  from  actual  to  potential  output  it  is  necessary  to  define  clearly 
what  one  means  by  potential  factor  use  and  by  the  trend  (i.e.  normal)  level  of  efficiency  of  factor 
inputs.   

 
Capital:  With  respect  to  capital,  this  task  of  defining  potential  factor  use  is  straightforward 
since  the  maximum  potential  output  contribution  of  capital  is  given  by  the  full  utilisation  of  the 
existing capital stock in an economy. Since the capital stock is an indicator of overall capacity there is 
no justification to smooth this series in the production function approach. In addition, the unsmoothed 
series  is  relatively  stable  for  the  EU  and  the  US  since  although  investment  is  very  volatile,  the 
contribution of capital to growth is generally quite constant (a notable exception to this "rule" was the 
2009 financial crisis) since net investment in any given year is only a tiny fraction of the capital stock 
figures.    In  terms  of  the  measurement  of  the  capital  stock,  the  perpetual  inventory  method  is  used 
which makes an initial assumption regarding the size of the capital / output ratio. 

 
Labour: The definition of the maximum potential output contribution of labour input is more 
involved  since  it  is  more  difficult  to  assess  the  "normal"  degree  of  utilisation  of  this  factor  of 
production.  Labour input is defined in terms of hours. Determining the trend of labour input involves 
several steps. In defining the trend input we start from the maximum possible level, namely the actual 

26 Since these values are close to the conventional mean values of 0.65 & 0.35, the latter are imposed for all countries. 

43 

TFPKLKEUELUYKKLL*)()(11))((11KLKLUUEETFP)1( 
 
 
 
 
 
 
 
 
 
                                                            
 
population of working age. We obtain the trend labour force by mechanically detrending (using an HP 
filter)  the  participation  rate.  In  a  next  step  we  calculate  trend  un/employment  to  be  consistent  with 
stable, non-accelerating, (wage) inflation (NAWRU). Finally, we obtain trend hours worked (potential 
labour supply) by multiplying trend employment with the trend of average hours worked. One of the 
big advantages of this approach is that it generates a potential employment series which is relatively 
stable whilst at the same time also providing for year-to-year changes to the series to be closely linked 
to  long  run  demographic  and  labour  market  developments  in  areas  such  as  the  actual  working  age 
population, trend participation rates and structural unemployment.  

 
Trend  Efficiency  :  Within  the  production  function  framework,  potential  output  refers  to  the 
level of output which can be produced with a "normal" level of efficiency of factor inputs, with this 
trend efficiency level being  measured  using  a  bivariate Kalman  filter  model which exploits the link 
between the TFP cycle and the degree of capacity utilisation in the economy. 

Normalising the full utilisation of factor inputs as one, potential output can be represented as follows: 

(3) 

. 

8.3.2.  Medium-Term (3 year) Extension  

While the production function derived potential output estimates provide a good picture of the present 
output  capacity  of  economies,  they  should  not  however  be  seen  as  forecasts  of  medium-term 
sustainable  rates  of  growth  but  more  as  an  indication  of  likely  developments  if  past  trends  were  to 
persist in the future.  If, for example, a country's potential growth rate is 2% in 2014, it can only be 
sustained at that rate in future years if none of the underlying driving forces change.  Any longer term 
assessment  would  need  therefore  to  be  based  on  a  careful  evaluation  of  the  likelihood  that  present 
rates of growth for labour potential, productive capacity and TFP will persist over the time horizon to 
be  analysed.  It  is  important  to  stress  that  this  technical  extension  is  in  no  way  a  forecast  for  these 
years  -  it  is  simply  an  attempt  to  illustrate  what  would  happen  if  the  trends  of recent  years  were  to 
persist  into  the  medium  term.  In  more  specific  terms,  on  the  basis  of  a  number  of  explicit 
assumptions,  including  transparent  ARIMA  procedures,  the  potential  growth  rates  for  the  medium 
term are calculated using the following key inputs: 

1. 

 Trend Total Factor Productivity (TFP): The TFP trend is estimated from the Solow residual by 
using a bivariate Kalman filter method that exploits the link between the TFP cycle and capacity 
utilisation. The Solow residual employed in the estimation process is calculated until the end of 
the short term forecast horizon using forecasts for GDP, labour input and the capital stock, which 
permits  the  extension  of  the  TFP  series  by  two  additional  observations.  Since  there  are  no 
forecasts of the degree of capacity utilisation in the economy, this  means that  the Kalman filter 
model is estimated with two missing values. During the estimation process, these missing values 
for capacity utlisation are, however, not problematic since the operation of the Kalman filter is not 
dependent  on  the  availability  of  a  forecast  extension.  The  filter  can  in  fact  compute  linear 
projections through a recursive procedure which yields the expected value of the TFP cycle on the 
basis of only the available observations. The Kalman filter in turn produces trend TFP forecasts 
by  simply  running  the  Kalman  filter  out  of  sample,  over  the  required  medium-term  forecast 
horizon. 

2. 

 NAWRU’s: The trend specification chosen for the NAWRU implies that the best prediction for 
the change in the NAWRU in future periods is the current estimate of the intercept. This basically 
implies that the slope of the NAWRU in the last year of the short-term forecasts should be used 
for  the  medium-term  projection.  Such  a  specification  seems  problematic  for  longer-term 
projections  since  it  will  eventually  violate  economic  constraints  (such  as  non-negativity  of  the 
NAWRU, for example, or balancing forces in the economy). An alternative specification which is 

44 

1)()(TKTLPPKEELY 
 
 
 
 
 
 
 
 
more  consistent  with  the  common  notion  of  the  NAWRU  as  a  stable  long  run  level  of  the 
unemployment rate would be a random walk without drift. This specification would imply a flat 
extrapolation  of  the  last  NAWRU  value.  Although  this  specification  does  not  work  well  in 
estimation  for  European  data  where  persistent  trend  changes  of  the  unemployment  rate  can  be 
observed, it may be a more plausible specification for the projections. The projections in practice 
constitute a compromise between these two concepts, with the medium-term NAWRU estimated 
according to the following rule: 

a. 

b. 

medium term extension 

 for t = first year of the 

 for t = all others years of the medium term extension 

c.  In forecasting the NAWRU, 50% of the most recent decline or increase is allowed 

for in the first year of the extension. After that the NAWRU is kept stable.  

3.  Population  of  Working  Age:  In  terms  of  a  projection  for  the  population  of  working  age  for  the 
medium-term (i.e. the three years following ECFIN's short-term forecast horizon), since Eurostat 
periodically produce long range population projections for all of the EU’s Member States, it was 
decided  that  the  most  recent  vintage  of  the  Eurostat  projections  should  be  used.  At  present, 
ECFIN uses the Eurostat EUROPOP 2016 set of population projections. 

4.  Participation rate changes: On the basis of the forecasts by ECFIN’s experts for the labour force 
and the population of working age for the individual countries, the implied total participation rate 
up to the end of the short-term forecasting period is produced and this latter series is extended on 
the basis of simple autoregressive projections. A further 3 years are added at the end of the series 
to limit the end point bias problem. The HP trend is then calculated on the whole series.  

5.  Average Hours Worked: Labour input in the method is decomposed into the number of employees 
and  the  average  hours  worked  per  employee.  The  hours  worked  series  is  extended  using  an 
ARIMA  process.  As  for  other  components,  the  series  is  extended  by  6  years,  to  avoid  the  end-
point bias, and then smoothed. Only the first 3 years are then used for the medium-term extension.  

6.  Investment  to  (potential) GDP  ratio:  Since  the  purpose  of  the  exercise  is  to  get  an  estimate  for 
potential  output  in  the  medium-term,  the  investment  to  potential  GDP  series  is  used  as  an 
exogenous  variable,  while  investment  itself  is  made  endogenous.  Generally,  an  AR  process, 
allowing for a constant and a time trend, is specified and estimated using the full range of data, 
including  ECFIN's  short-term  forecasts.  For  a  constant  investment  to  GDP  ratio,  investment 
responds to potential output with an elasticity equal to one.  

8.3.3.   Technical Specification of the Model Used  

The model used can be summarised as follows: 

Exogenous Variables 

  POPW - (Population of Working Age)  
  PARTS - (Smoothed Participation Rate) 
  NAWRU - (Structural Unemployment)  
 
  SRK - (Kalman Filtered Solow Residual) 
  HOURST – (Trend, average hours worked) 

IYPOT - (Investment to Potential GDP Ratio) 

45 

)(*5.211ttttNAWRUNAWRUNAWRUNAWRU1ttNAWRUNAWRU 
 
 
 
 
 
 
 
 
 
 
 
 
 
Endogenous Variables 

  LP - (Potential Labour Input) 
 
I - (Investment) 
  K - (Capital Stock) 
  YPOT -(Potential Output) 

1. Potential Labour input 

LP



(

POPW

*

PARTS

1(*



NAWRU

*))

HOURST

2. Investment and Capital 

I



IYPOT

*

YPOT

IK



1(

Kdep
)


)1(

27 

3. Potential Output 

YPOT 



LP 

. 
65 

K 

. 
35 

SRK 

4. Output Gap 

YGAP



Y
(

/

YPOT



)1

27 The depreciation rate is assumed to remain constant over the projection period.  

46 

 
 
 
 
 
 
                    
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