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European Commission Directorate General Economic and Financial Affairs
An experimental sentiment indicator
for the euro area – tracking and
nowcasting q-o-q GDP growth
Andreas Reuter
Business and consumer surveys and
short-term forecast (ECFIN A4.2)
2
Outline
2. Strengths / Weaknesses of the Economic Sentiment Indicator (ESI)
4. Improving the ESI's tracking performance of q-o-q GDP growth
step a: re-constructing the ESI based on
"best-performing" survey questions
step b: an ESI with amplified changes
3. Refresher on ESI Construction Method
1. Introduction: the Economic Sentiment Indicator (ESI)
5. The nowcasting performance of the experimental ESI
step a: Model selection (based on regression analyses)
step b: out-of-sample properties (sim. real-time scenario)
3
1. Introduction: the Economic Sentiment Indicator (ESI)
Added value: timeliness (complementing delayed quantitative
statistics)
high frequency
Purpose of the ESI:
summarising developments in all 5 sectors covered by DG ECFIN's
Business and Consumer Surveys (BCS):
services
tracking GDP growth at Member State, EU and euro-area level
industry
construction
retail trade
consumers
ingredient for bridge models now-/forecasting GDP growth
-6
-4
-2
0
2
4
6
60
70
80
90
100
110
120
96-q
2
97-q
2
98-q
2
99-q
2
00-q
2
01-q
2
02-q
2
03-q
2
04-q
2
05-q
2
06-q
2
07-q
2
08-q
2
09-q
2
10-q
2
11-q
2
12-q
2
13-q
2
y-o-y (%)
quarterly averages
ESI and GDP growth; euro-area (1996q2-2013q2)
ESI GDP growth (rhs)
Note: monthly BCS data are converted into quarterly by
averaging the balances over 3 months. GDP figures refer
to y-o-y changes (%). Source: European Commission.
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
60
70
80
90
100
110
120
96-q
2
97-q
2
98-q
2
99-q
2
00-q
2
01-q
2
02-q
2
03-q
2
04-q
2
05-q
2
06-q
2
07-q
2
08-q
2
09-q
2
10-q
2
11-q
2
12-q
2
13-q
2
q-o-q (%)
quarterly averages
ESI and GDP growth; euro-area (1996q2-2013q2)
ESI GDP growth (rhs)
Note: monthly BCS data are converted into quarterly by
averaging the balances over 3 months. GDP figures refer
to q-o-q changes (%). Source: European Commission.
4
2. Strengths / Weaknesses of the Economic Sentiment
Indicator (ESI)
0.93
0.88
Correlations (1996q2-2013q2):
coincident
leading 1
0.72
0.49
Correlations (1996q2-2013q2):
coincident
leading 1
excellent sub-optimal
see-sawing
movement
absent in ESI
pace of
recovery under-
estimated by
ESI
addition of 100
multiplication by 10
5
ingredients: balance series of 15 survey questions
effect:
values >100 indicate above-
average economic sentiment
2/3 of observations will be in the
interval [90 ; 110] if norm. distr.
3. Refresher on ESI Construction Method
The questions are:
seasonally adjusted
standardised
allocating weights per sector:
Industry: 40% ; Services: 30% ; Consumers: 20% ; Construction: 5% ; Retail Trade: 5%
calculation of arithmetic mean of weighted balances
standardisation of the ESI and:
effect:
comparability of balance series in
terms of mean and volatility
no series dominates development
of ESI due to a higher amplitude
individual indu question has weight of 13,3% (=40% weight / 3 questions)
% of positive answers minus % of negative answers
6
4. Improving the ESI's tracking performance of q-o-q GDP
growth
step a: re-constructing the ESI based on "best-performing" survey questions
correlation of all individual EU BCS survey questions (quarterly averages) with:
i) the respective reference series (e.g. Gross Value added in
Manufacturing for the industry questions, etc.) ii) q-o-q GDP growth (EA)
construction of 3 new Confidence Indicators (CIs) for every sector observed: arithmetic mean of the respective (s.a.) balance series
i) based on 2/3 best performing questions of respective sector
iii) based on all forward-looking questions of respective sector
for each sector: selection of best sectoral CI (reg. correlation with ref. series &
q-o-q GDP growth (EA))
questions contained in the best CIs make up the new ESI
7
Intermediate Results:
4. Improving the ESI's tracking performance of q-o-q GDP growth – step a: reconstructing the ESI based on "best-performing" survey questions
Industry Services Retail Trade Consumers
order books
- currently
business
- last 3 months
business activity
(sales)
- last 3 months
household's fin.
position
- next 12 months
stock of (finished)
products
- currently
demand for firm's
services
- last 3 months
volume of stock
- currently
econ. situa-tion in
MS
- next 12 months
production
- next 3 months
demand for firm's
services
- next 3 months
business activity
(sales)
- next 3 months
unemploy-ment in
MS
- next 12 months
likelihood of
saving money
- next 12 months
Construction
order books
- currently
firm's employment
- next 3 months
production
- last 3 months
expected level of
major purchases
- next 12 months
expected orders
with suppliers
- next 3 months
building activity
- last 3 months
current ESI improvement ESI (new questions)
coincident 0.72 7% 0.77
correlations with GDP q-o-q:
leading 1 0.49 10% 0.54
8
step b: an ESI with amplified changes
Intuition of the approach:
4. Improving the ESI's tracking performance of q-o-q GDP growth
Comparable changes in the ESI should be taken more "seriously", when reflected
by many survey questions.
Example:
-2
-1.5
-1
-0.5
0
0.5
change in 1999Q1 (compared to previous quarter)
change in 2000Q4 (compared to previous quarter)
Change in modified ESI: -1.8
Change in modified ESI: -2
Instead:
change in ESI
should be
-2*x (with x > 1)
standard deviation of balance series
>>>> change in ESI should be multiplied, if a critical amount of questions changes
in the same direction we propose:
8 (out of 11) questions we propose:
multiplication by 3
9
Calculation method for the experimental ESI
Ingredients: balance series from 11 EU BCS questions see slide 7:
i) make sum of all balance series
v) re-calculate series A:
questions expressed as:
quarterly averages
standardised
weighted (remember: industry questions get
40%, services questions 30%, etc.)
ii) calculate absolute q-o-q change in series A (series B)
(series A)
iii) make dummy-variable taking value 1 if, in a given quarter, >=8 balance series
increase/decrease (series C)
iv) re-calculate q-o-q changes: if series C = 1: series B * 3 if series C = 0: series B
new series
shows q-o-q
changes, which
are, if applicable,
amplified by 3
(series D)
for quarter q: series A (q-1) + series D (q)
"classical" sum of balance
series (previous quarter)
amplified q-o-q change of current
quarter (unamplified if not enough
questions move up/down)
vi) standardise & re-scale the new series >>>>experimental ESI
4. Improving the ESI's tracking performance of q-o-q GDP growth – step b: an ESI with amplified changes
60
70
80
90
100
110
120
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
96
-q2
97
-q2
98
-q2
99
-q2
00
-q2
01
-q2
02
-q2
03
-q2
04
-q2
05
-q2
06
-q2
07
-q2
08
-q2
09
-q2
10
-q2
11
-q2
12
-q2
13
-q2
q-o-q (%) quarterly averages ESI, PMI, experimental ESI and GDP growth;
euro-area (1996q2-2013q2)
GDP growth (rhs)
Composite PMI
experimental ESI
10
4. Improving the ESI's tracking performance of q-o-q GDP growth – step b: an ESI with amplified changes
Results:
60
70
80
90
100
110
120
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
96
-q2
97
-q2
98
-q2
99
-q2
00
-q2
01
-q2
02
-q2
03
-q2
04
-q2
05
-q2
06
-q2
07
-q2
08
-q2
09
-q2
10
-q2
11
-q2
12
-q2
13
-q2
q-o-q (%) quarterly averages ESI, PMI, experimental ESI and GDP growth;
euro-area (1996q2-2013q2)
GDP growth (rhs)
ESI
Composite PMI
experimental ESI
60
70
80
90
100
110
120
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
96
-q2
97
-q2
98
-q2
99
-q2
00
-q2
01
-q2
02
-q2
03
-q2
04
-q2
05
-q2
06
-q2
07
-q2
08
-q2
09
-q2
10
-q2
11
-q2
12
-q2
13
-q2
q-o-q (%) quarterly averages ESI, PMI, experimental ESI and GDP growth;
euro-area (1996q2-2013q2)
GDP growth (rhs)
ESI
experimental ESI
"micro"-volatility of
GDP better captured
2008-downturn announced
with steeper slope
2009-upswing reflected
with steeper slope
"micro"-volatility of GDP better
captured
Better leading
properties
60
70
80
90
100
110
120
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
96
-q2
97
-q2
98
-q2
99
-q2
00
-q2
01
-q2
02
-q2
03
-q2
04
-q2
05
-q2
06
-q2
07
-q2
08
-q2
09
-q2
10
-q2
11
-q2
12
-q2
13
-q2
q-o-q (%) quarterly averages ESI, PMI, experimental ESI and GDP growth;
euro-area (1996q2-2013q2)
GDP growth (rhs)
ESI
Composite PMI
experimental ESI
time-period current ESI PMI ESINEW (ampl.)
increase
compared to
current ESI
increase
compared to
PMI
98Q3 – 08Q1 0.61 (0.38) 0.77 (0.55) 0.76 (0.56) 0% (2%)
08Q2 – 13Q2 0.69 (0.31) 0.86 (0.56) 0.89 (0.71) 28% (131%) 3% (28%)
98Q3 – 13Q2 0.75 (0.50) 0.86 (0.65) 0.89 (0.75) 19% (48%) 3% (14%)
Correlation with GDP growth q-o-q (in brackets: leading 1 correlations)
26% (47%)
11
5. The nowcasting performance of the experimental ESI
step a: Model selection (based on regression analyses):
exp. ESI is based on quarterly averages of BCS questions
>>>>any nowcast using exp. ESI can only be produced at the end of a
given quarter (when quarterly average of BCS questions is known)
several hard-data are available at the end of a given quarter and can thus be
included in the now-casting model:
Δ index of industrial production
still 45 days
before first
GDP release!
Δ index of production in construction
Δ index of turnover in retail trade
(except of motor
vehicles/motorcycles) Δ value of euro-area exports
(extra- and intra-euro-area) unemployment rate Δ new passenger car registrations
VStoxx (measure of volatility on European stock markets)
Calculation of quarterly changes (Δ):
i) in-sample:
for a quarter q:
(monthly average over quarter q)
(monthly average over quarter q-1)
ii) out-of-sample:
for a quarter q:
(month 1 value of quarter q)
(month1 value of quarter q-1)
12
constant constant constant constant constant
Benchm'k
Model(s)
constant constant
GDP(-1) exp. ESI exp. ESI exp. ESI exp. ESI exp. ESI exp. ESI
GDP(-2) ΔIPI ΔIPI ΔIPI ΔIPI
GDP(-3) ΔIPC ΔIPC ΔIPC
VSt'x(-1) VSt'x(-1) VSt'x(-1)
ΔReta
Δexports
Unempl.
New Model(s)
Δcar reg.
R2: 0.49 R2: 0.93
R2: 0.48 R2: 0.80 R2: 0.91 R2: 0.84 R2: 0.90 R2: 0.90 R2: 0.88
5. The nowcasting performance of the experimental ESI – step a: Model selection (based on regression analyses)
13
time-period GDP(-1) exp. ESI
exp. ESI
ΔIPI
ΔIPC
exp. ESI
ΔIPI
VSt'x(-1)
exp. ESI
ΔIPI
MAE: 0.39 0.27 0.21 0.20 0.21
step b: out-of-sample properties (simulated real-time scenario):
04q1 - 08q1 0.18 0.16 0.15 0.16 0.15
08q2 - 09q4 1.10 0.49 0.42 0.41 0.42
10q1 - 13q2 0.28 0.28 0.16 0.15 0.17
RMSE: 0.67 0.36 0.29 0.27 0.26
04q1 – 08q1 0.22 0.19 0.20 0.20 0.18
08q2 – 09q4 1.45 0.61 0.51 0.43 0.44
10q1 – 13q2 0.34 0.34 0.22 0.23 0.23
hit ratio 1: 32/38 33/38 35/38 36/38 34/38
04q1 – 08q1 17/17 17/17 17/17 17/17 17/17
08q2 – 09q4 5/7 6/7 7/7 6/7 6/7
10q1 – 13q2 10/14 10/14 11/14 13/14 11/14
Hit ratio 2: 14/37 26/37 28/37 30/37 29/37
04q2 – 08q1 5/16 11/16 9/16 11/16 11/16
08q2 – 09q4 5/7 7/7 7/7 7/7 6/7
10q1 – 13q2 4/14 8/14 12/14 12/14 12/14
Observations: AR-model's nowcasts on
average 0.40 pp off actual
GDP (>>bad)
AR-model: MAE/RMSE
highest in crisis-period,
lowest in pre-crisis period
exp. ESI model: clearly
outperforming AR-model;
added value of exp. ESI lies
in crisis period (logical:
amplification technique
captures "momentum")
addition of hard-data helps in
terms of:
MAE/RMSE;
hit ratios
improvements mainly in
calm times (post-crisis)
0.39 0.27 0.20
0.67 0.36 0.26
33/38 36/38
30/37
0.49 1.10
0.15
1.45 0.61
10/14
8/14
13/14
12/14
0.22
26/37
0.34
+3
+4
+3
+4
0.28
-46%
-55%
-26%
-46%
-31%
-58%
-35%
-28%
best model is "exp. ESI, IPI,
Vstoxx(-1)":
lowest MAE
highest hit-ratio 1
highest hit-ratio 2
5. The nowcasting performance of the experimental ESI
14
Comparative advantage of new model most obvious in:
last 2 years (correct sign of GDP just once missed, although GDP
hovered around 0)
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
08-q
1
08-q
2
08-q
3
08-q
4
09-q
1
09-q
2
09-q
3
09-q
4
10-q
1
10-q
2
10-q
3
10-q
4
11-q
1
11-q
2
11-q
3
11-q
4
12-q
1
12-q
2
12-q
3
12-q
4
13-q
1
13-q
2
q-o-q (%)
Nowcasts of GDP growth produced by bridge models; euro area (2008q1-2013q2)
observed GDP growth model using GDP(-1) model using exp ESI, IPI, Vstoxx(-1)
Note: The nowcasts are produced by a pseudo out-of-sample exercise using soft- and hard data downloaded on 30/08/13. The in-sample period for the nowcasts is 1999q1 to (incl.) the quarter preceding the quarter to be nowcast. Source: European Commission.
crisis-period
5. The nowcasting performance of the experimental ESI – step b: out-of-sample properties (simulated real-time scenario)
15
Conclusion:
BCS data can be used to construct indicator tracking q-o-q GDP growth
satisfactorily
Step 1: choose the BCS questions best correlated with q-o-q GDP growth
Experimental sentiment indicator produces better GDP nowcasts than
simple AR-model
Step 2: (artificially) amplify q-o-q changes of the sentiment indicator, in
case a vast majority of questions displays q-o-q changes in the same
direction
In combination with hard-data, the experimental sentiment indicator allows
producing GDP nowcasts of excellent quality
16
4. Improving the ESI's tracking performance of q-o-q GDP growth – step b: an ESI with amplified growth
Shortcomings of the approach
Is it possible to apply the same construction method to monthly
data?
Calculations are done with quarterly averages of BCS questions
>>>> indicator could only be published once a quarter
technically yes !
Will quarterly averages of the resulting monthly ESI-series
remain well-correlated with q-o-q GDP growth?
yes: even slightly higher
correlations with GDP q-o-q
correlation of the two
quarterly ESI series is at
0.97
However…
17
4. Improving the ESI's tracking performance of q-o-q GDP growth – step b: an ESI with amplified growth – Shortcomings of the approach
Problem of constructing ESI with amplified changes for
monthly data:
too high volatility
55
65
75
85
95
105
115
125
ESINEW - amplified changes (monthly levels) current ESI (monthly levels)
Sep 2009: in upswing-period, ESINEW with
amplified changes drops by 10 points
(=1 standard deviation)
18
4. Improving the ESI's tracking performance of q-o-q GDP growth – step b: an ESI with amplified growth – Shortcomings of the approach
If amplification is applied in t-1, but not in t, ESI will usually suggest a
drop in sentiment in t (also in case the underlying data continues the
upward/downward trend of t-1)
Source of volatility (note: amplifying changes does not only increase the
amplitude of the series, but also its volatility):
9596979899
100101102103104105
Jan Feb Mar Apr
unamplified ESI
ESI (amplified change): assuming8 questions go up in Feb+2 +2 +2
+2*3 = +6
This additional volatility improves the fit of our quarterly series, but
renders the monthly series TOO volatile.
Main reason for this difference: criterion for amplification is more
restrictive in case of quarterly set-up:
for quarterly: >= 8 questions must have gone up/down over 3
months-period (amplification in 63% of quarters)
for monthly: >= 8 questions must have gone up just one month
(amplification in 73% of the months)
19
4. Improving the ESI's tracking performance of q-o-q GDP growth – step b: an ESI with amplified growth – Shortcomings of the approach
Solution: When multiplying change of t (compared to t-1), the
resulting amplified change should be added to ESI for
month t, but also ESI of t+1 and t+2 (1/3 of the change
respectively should be added). Approach smoothens the monthly curve substantially…
55
65
75
85
95
105
115
125
ESINEW - amplified changes (monthly levels)
ESINEW - amplified changes distributed over 3 months (monthly levels)
When constructing quarterly averages, correlations
with q-o-q GDP growth remain high.
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