Nowcasting eurozone industrial production.
Date: 2003-12-17
Institutions: Eurostat
Subjects: economic model, euro area, industrial production, short-term forecast

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Nowcasting 
Eurozone Industrial
Production

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THEME 1
General
statistics

E U R O P E A N
C O M M I S S I O N

 
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Nowcasting Eurozone Industrial Production

Dominique Ladiray and Dermot O’Brien

Abstract

The aim of this paper is to develop a methodology for the estima-
tion of nowcasts of the Eurozone Industrial Production Index (IPI) for
a delay of less than 45 days. We propose to build well-speciﬁed robust
models for annual and monthly eurozone IPI growth rates that incor-
porate information from business surveys and partial information from
Member States. We prioritise models that are stable and well-speciﬁed
and the optimal models are determined on the basis of an assessment
of nowcasting performance for real-time data.

1

TABLE OF CONTENTS

1. Introduction................................................................................................

2. Evolution of Arrival Delays .......................................................................

3. Prospects for an Early IPI ..........................................................................

4. Developing a Methodology for Producing IPI Nowcasts ..........................

5. Models for Eurozone IPI............................................................................
5.1 Annual IPI Growth Rates ....................................................................
5.1.1 Model A: NAIVE Model ...........................................................
5.1.2 Model B: GETS with Business Surveys....................................
5.1.3 Model C: GETS with Business Surveys and 

Partial Information (1) ...............................................................

5.1.4 Model D: GETS with Business Surveys and 

Partial Information (2) ...............................................................
5.1.5 Model E: Simple Statistical Estimate ........................................
5.1.6 Previous Eurostat Study.............................................................
5.2 Monthly IPI Growth Rates ..................................................................
5.3 VAR Modelling of Eurozone IPI Monthly Growth Rates ...................

6. Nowcasting Performances..........................................................................

7. Conclusion .................................................................................................

References.......................................................................................................

3

3

5

6

7
7
7
8

9

9
10
10
10
11

12

15

16

2

1 Introduction

There has been a clear convergence towards more timely provision of the
Industrial Production Index (IPI) by Member States following legislation
introduced in 1998. This legislation requires a delay of no longer than 45
days and we ﬁnd that, in the year 2001, the larger Member States typically
respected this deadline. Any attempt to produce reliable nowcasts for the
IPI appreciably earlier than the 45 day deadline would need to incorpo-
rate supplementary information by way of business surveys and/or partial
information from Member States.

Section 2 presents an analysis of the delay times of the Member States’
IPI. We consider, in Section 3, the prospects for the production of IPI now-
casts for delays shorter that a 45 day delay. In Section 4, we discuss some
of the issues involved in developing a methodology for producing nowcasts.
Subsequently, we propose some tentative models for annual and monthly
IPI growth rates in Section 5 and provide an assessment of the nowcasting
performance of the competing models in Section 6. Section 7 concludes.

2 Evolution of Arrival Delays

We have data on reference and arrival dates over the period June 1999 to
October 2001 and the delays (delay=arrival-reference) in days have been
calculated. Figure 1 illustrates the development over time of delays for the
respective Member States’ IPI. The plot brings into sharper focus the coun-
tries for which there have been substantial improvements in delay times and
we can observe that Austria, Finland, Portugal and Spain in particular have
made strong gains. Some of the larger EU Member States, such as Germany
and the UK, have been satisfying the 45 day deadline on a consistent basis.
Despite these advances, some Member States such as Austria, Greece, and
Ireland continue to provide IPI data with a relatively big delay.

The European aggregate IPI is only calculated when Eurostat has re-
ceived at least 60% of the total weighting. Table 1 indicates that for the
most recent reference period (Oct 2001) there was a delay of 42 days be-
fore over 60% of the total weight was received. In practise the aggregate
IPI is calculated when over 90% of the total weight has been received and
this occurs approximately at the delay legislated for i.e. 45 days (with the
exception of July).

3

Figure 1: Evolution of delays.

Table 1: Delays for Cumulative Weightings

Reference
Oct ’00
Nov ’00
Dec ’00
Jan ’01
Feb ’01
Mar ’01
Apr ’01
May ’01
Jun ’01
Jul ’01
Aug ’01
Sep ’01
Oct ’01

30% 40% 50% 60% 70% 80%
49
37
50
43
47
39
44
37
44
40
45
40
45
39
46
39
45
38
66
38
45
39
46
39
44
37

45
47
44
43
43
44
44
42
40
44
45
44
42

41
47
39
40
40
44
42
40
39
42
40
44
41

37
47
39
40
40
40
42
39
39
42
40
39
40

45
48
46
43
44
45
45
43
41
51
45
45
43

The cumulative weights (in percentages) for a given delay are presented
in Table 2. We can observe that, for a given reference period, at most
two of the smaller states (two of Finland, Luxembourg and Denmark) have
provided IPI data with a delay of 35 days or less during 2001. The fact
that Finland has already broken the 30 day delay barrier does oﬀer some
hope that a 30 day delay for the Eurozone IPI is a realisable goal with other
Member States following the example set by Finland.

4

With the possible exception of Germany, we can observe a high volatility
for the delays and in particular a sharp jump in delays for July. Volatile
delays, needless to say, complicate the task of producing an IPI nowcast
with a known and systematic release date.

Table 2: Cumulative Weights for a given Delay, year 2001

Delay
45
44
43
42
41
40
39
38
37
36
35

Jan Feb Mar Apr May
78.0
92.7
85.7
78.0
60.1
85.7
78.0
44.5
70.6
62.9
44.5
53.0
59.1
44.5
53.0
55.9
41.3
53.0
48.5
1.7
37.4
3.3
1.7
37.4
3.3
1.7
37.4
3.3
0
1.6
1.7
0
1.6

92.6
62.8
59.6
58.4
31.2
31.2
31.2
1.6
1.6
1.6
1.6

92.7
92.7
62.9
58.0
56.8
53.0
17.2
17.2
17.2
0
0

Jun
92.7
77.5
77.5
77.5
77.5
68.5
50.0
31.2
0
0
0

Jul
61.3
61.3
58.1
51.9
31.3
31.3
31.3
31.3
1.6
1.6
1.6

Aug Sep Oct
93.9
76.0
89.5
93.9
60.9
59.7
79.2
45.3
59.7
60.9
45.3
59.7
59.7
45.3
59.7
49.7
45.3
55.9
34.1
44.1
34.1
34.1
3.3
3.2
34.1
1.6
3.2
1.7
1.6
3.2
1.7
1.6
3.2

3 Prospects for an Early IPI

Legislation dating from 1998 established a clear target for submission of
IPI to Eurostat and has been successful in encouraging Member States to
shorten delays. We can observe that the major economies in the eurozone
provide IPI data with a delay that respects the 45 day deadline (or almost,
as in the case of Italy).

The recent Finnish performance in providing IPI data can serve as a
useful point of reference to other Member States. However, unless new
legislation is introduced, Member States may not be motivated to the same
extent to make further reductions in delay times. Moreover, it may become
progressively more diﬃcult to reduce delays as in the beginning the more
obvious steps in reducing delay times are taken.

The task of developing a methodology for producing early indicators for
the IPI is a challenging one. However, information from business surveys and
ﬂash estimates from larger Member States can provide valuable explanatory
power. We propose to incorporate this information into a model of IPI and
demonstrate that reliable early indicators for the IPI can be obtained with
a well-speciﬁed model.

5

4 Developing a Methodology for Producing IPI

Nowcasts

There are a number of criteria that need to be satisﬁed when developing a
model for producing early indicators (nowcasts) of the IPI. These criteria
fall under three headings

• Statistical: The model needs to be well-speciﬁed with model robust-
ness being a particularly desirable property. We note also that real-
time data (i.e. non-revised) may be subject to three forms of revisions:
Member States provide revisions of previously submitted data; Mem-
ber States provide data that was previously estimated by Eurostat;
and seasonal adjustment revisions. The delay time for the IPI now-
cast should be suﬃciently reduced from 45 days to make the nowcasts
of clear beneﬁt to users. In this case, we would need to incorporate
into our model information from other sources e.g. business surveys
and ﬂash estimates from Member States.

• Economic: The model needs to have a sensible economic interpreta-

tion.

• Production: The model must be simple and easy to implement. An
important property of the model is that it should be stable over time
so as to avoid a necessity for frequent revisions of the model.

A further issue concerns whether to nowcast the Eurozone IPI (direct
approach) or nowcast Member States’ IPI and then aggregate (indirect ap-
proach). We keep these various criteria in mind as we now investigate a
large set of potential models for nowcasting eurozone industrial production.

6

5 Models for Eurozone IPI

The principal aim of this study is to identify a model for eurozone IPI that
will produce reliable nowcasts. There are a number of diagnostic and other
criteria for the model speciﬁcation that need to be satisﬁed in order to have
a good degree of conﬁdence in the suitability of a candidate model. We ex-
amine a number of potential model speciﬁcations. Initially we focus on mod-
elling annual growth rates for eurozone IPI i.e. (IP It − IP It−12)/IP It−12.
An earlier study by Eurostat built models for annual IPI growth rates and
part of the motivation of this current study is to examine the relative per-
formance of other competing methodologies. In the next section we turn
our attention to monthly growth rates calculated using a log diﬀerence ap-
proximation (log IP It − log IP It−1).

We use data from January 1988 to December 2001 for eurozone IPI and
business surveys (at eurozone and national levels). The dependent variable
is the annual or monthly growth rate for eurozone IPI and is based on
the working day non-seasonally adjusted series. The potential explanatory
variables are as follows:

lags of the dependent variable IPI;

current and lagged production trend observed in recent months (IPT);

current and lagged assessment of order book levels (IOB); and

current and lagged production expectations for the months ahead (IPE)1.

Individual Member States’ IPI and business survey results could be ad-
mitted in this exercise and have been incorporated in a previous Eurostat
study on modelling eurozone IPI.

5.1 Annual IPI Growth Rates

5.1.1 Model A: NAIVE Model

We will use this simple model as our benchmark model. The changes in the
series are assumed to follow a random walk giving the model representation

∆yt = ∆yt−1 + εt

where εt are i.i.d. with mean zero. The model has been shown to be

robust to structural breaks - see Hendry and Clements (1999).

1There are three other business survey variables - selling price expectations for the
months ahead (ISPE); assessment of export order book levels (IEOB) and assessment
of stocks of ﬁnished products (ISFP). These have not provided any useful explanatory
power in previous modelling exercises and thus have not been included in our initial set
of explanatory variables.

7

5.1.2 Model B: GETS with Business Surveys

Nowcasts can be produced at a particular delay after the reference period
and the respective models should take into account the complete set of
information available at that delay. We can imagine as a starting point a
delay of 15 days, which corresponds to the release delay for the US Industrial
Production Index. For such a delay, we do not have partial information from
any of the Member States. However, business survey data are available and
can oﬀer informative assessments of the conditions in the economy, and thus
may also provide useful explanatory power in a model of IPI.

The General-to-Speciﬁc Modelling Approach advocates beginning with
a large general dynamic model. Our initial model includes three lags for
each explanatory variable and we can reduce the model to the following2

IP It = deterministic terms +0.05 ∗ IP It−1 + 0.16 ∗ IP It−2 + 0.14 ∗ IP Tt

−0.08 ∗ IOBt−3 + 0.22 ∗ IP Et−1 + errort

The model is estimated using OLS and the usual assumptions are made
for the errors. We use the Progress function in PcGive where each reduc-
tion is tested using F-tests conditional on the previous model (one variable
removed at each step)3. We consider model diagnostics carefully only when
we can no longer reduce the model. The deterministic terms comprise a
constant and also two impulse dummies at 1995-2 and 2000-1 to account for
structural breaks.

We now consider some of the properties of the proposed model. The
R2-adjusted is 89% indicating a very satisfactory model ﬁt. The parameters
have reasonable sign and magnitude, bar the coeﬃcient for IOBt−3. The
unusual coeﬃcient for IOBt−3 could be partly explained by the presence of
multicollinearity - IOB is strongly correlated with both IPT (77%) and IPE
(80%). Thus, care needs to be taken when interpreting the parameters. We
note that IPIt−1 is not signiﬁcant but we include it in the model nonetheless.
All other variables are signiﬁcant at the 5% level.

A battery of speciﬁcation tests reveals no evidence to suggest that there
is autocorrelation in the residuals, no ARCH eﬀects, that the standardised
residuals have approximately a normal distribution and that there is no het-
eroskedasticity. We ﬁnd weak evidence of functional form mis-speciﬁcation.
The test for model functional form mis-speciﬁcation is Ramseys RESET test
and is signiﬁcant at the 5% level (p-value=0.024). The two dummy variables
included in the model improve model diagnostics and are signiﬁcant. There
are some minor structural breaks remaining as revealed by the 1-up Chow
test. The Break-point and Forecast Chow test results are satisfactory and
thus we conclude that the model is parameter constant.

2Note that all variables refer to the eurozone 12 unless otherwise indicated.
3PcGive uses as default the Schwarz criterion in general-to-speciﬁc modelling for se-

lecting between congruent simpliﬁcations.

8

5.1.3 Model C: GETS with Business Surveys and Partial Infor-

mation (1)

As noted in Section 2, the IPI from Member States arrive with varying de-
lays. We can construct an aggregate for the IPI at 40 days for a subset
of countries. We choose a delay of 40 days as the IPI data for Germany
generally arrives with such a delay. Other eurozone countries that may
reasonably be included in this subset are Spain, Netherlands, Finland and
Luxemburg. The composite percentage weight of this group of ﬁve coun-
tries is approximately 51.8%. The aggregate IPI for this group would be
expected to provide valuable explanatory power in a model of eurozone IPI.
We incorporate such a variable into Model B and ﬁnd that this aggregate is
indeed very signiﬁcant. The ﬁnal model derived is as follows4

IP It = deterministic terms +0.11∗IP Tt−0.04∗IOBt−3+0.07∗IP Et−1+

0.51 ∗ IP I partial

t

+ errort

where IP I partial

t

is the aggregated IPI for ﬁve countries at time t. We
note that the lagged terms for IPI are not present in the model. The Progress
function conﬁrms that these lagged terms can be dropped from the model
while the inclusion of the aggregate IPI term also serves to remove any
autocorrelation in the residuals. The model is well-speciﬁed and has an R2-
adjusted value of 91.9%. The signs and magnitudes of the coeﬃcients are
sensible except for the negative coeﬃcient for IOBt−3.

5.1.4 Model D: GETS with Business Surveys and Partial Infor-

mation (2)

We modify Model C by dropping the IOBt−3 term from the model in order
to investigate the nowcasting performance of a more coherent but with some
autocorrelation present in the residuals. Other alternatives for IOBt−3 term
have been examined in order to account for the presence of residual autocor-
relation but on every occasion the sign of the added term is ’wrong’. These
alternatives included the industrial conﬁdence indicator (ICI), assessment
of export order books (IEOB), stocks of ﬁnished products (ISFP) and their
respective lags. The model formulation is presented below. The R2-adjusted
value is 94.86% and the coeﬃcients in the model have reasonable sign and
magnitude.

IP It = deterministic terms +0.10 ∗ IP Tt + 0.005 ∗ IP Et−1 + 0.59 ∗

IP I partial
t

+ errort

There are a number of potential factors that could lead to a model
mis-speciﬁcation such as residual autocorrelation. A likely cause is that

4Note that all variables refer to the eurozone 12 unless otherwise indicated.

9

a relevant (autocorrelated) explanatory variable has been omitted. Resid-
ual autocorrelation could also be attributed to incorrect functional form or a
mis-speciﬁcation of the equation dynamics. Only when we are satisﬁed that
none of these explanations are valid can we fall back on the generalised least
squares approaches (including Cochrane-Orcutt Iterative Least Squares) to
the problem of non-spherical errors. Attempts to incorporate additional ex-
planatory variables into the model have led to implausible coeﬃcients signs.
This is somewhat puzzling and can only be only partially explained by pres-
ence of strong multicollinearity across the explanatory variables.

5.1.5 Model E: Simple Statistical Estimate

We also calculate a simple estimate of the overall eurozone IPI based on a
multiple of the aggregated IPI for the ﬁve selected countries. The multiple
is chosen according to the weighting by GDP for this subset (approximately
52%). The obvious adavantage of such an estimate is its simplicity and if it
is the case that the eurozone countries have indeed similar business cycles,
then we would expect this simple estimate to perform well.

5.1.6 Previous Eurostat Study

Donzel[3] identiﬁes a model for IPI using an automatic statistical model
selection strategy that allows the investigator choose from a small set of
models rather than from a large set of potential explanatory variables (see
Ladiray). Thus, this approach has the advantage of allowing the study cater
for a large set of explanatory variables. The model can be deﬁned as5

+0.03 ∗ IP EIT A

t

+ 0.02 ∗ IP T F RA

t

+ 0.05 ∗ IP Tt + 0.59 ∗ IP I partial

+ εt

t

IP It = deterministic terms

We can observe that the large countries not present in the aggregate
variable (i.e. France and Italy) are represented by one of their respective
business surveys variables 6. Tests reveal that the model is not well-speciﬁed
as there is autocorrelation in the residuals.
In addition, model stability
remains a concern in this speciﬁcation. The R2-adjusted value is 90.0%.

5.2 Monthly IPI Growth Rates

We present three single-equation models for monthly eurozone IPI growth
rates where each is an extension of the previous model. The models have

5Note that all variables refer to the eurozone 12 unless otherwise indicated.
6We should note also that the model we use here is a slight adaptation of a model

identiﬁed by Donzel for the EU 15.

10

been arrived at using the general-to-speciﬁc model selection approach. An
intercept and seasonal terms are included in each case. The ﬁrst model
(Model F) serves as a benchmark model and is the autoregressive model
commonly used in nowcasting/ forecasting exercises. We assume that the
changes in the log levels of the series, the monthly growth rates, follow an
autoregressive process. The model with 20 lags can be described as follows

∆ipit = deterministic terms +

14(cid:80)

i=0

∆ipit−i + εt

The second model (Model G) incorporates information from business
survey variables. The model is well-speciﬁed and stable. There are three
lags on each of IPE (expectations for the months ahead) and IPT (trends
in production in the recent months). The model is deﬁned as follows

∆ipit = deterministic terms +

14(cid:80)

i=0

∆ipit−i +

3(cid:80)

j=0

aiIP Tt−j +

3(cid:80)

k=0

IP Et−k +

εt

The third single-equation model incorporates partial information from
Member States in a model with business survey variables. We reduce the
model such that there are three lags on the partial aggregate of eurozone
IPI. The model is well-speciﬁed and stable according to recursive Chow tests.
We deﬁne the model to be

∆ipit = deterministic terms +

14(cid:80)

i=0

∆ipit−i +

3(cid:80)

j=0

aiIP Tt−j +

3(cid:80)

k=0

IP Et−k +

3(cid:80)

l=0

∆ipipartial

t−l + εt

5.3 VAR Modelling of Eurozone IPI Monthly Growth Rates

We attempt to assess the relative performance of modelling a system of vari-
ables rather than the single equation models we have treated thus far. The
dimension of the system is naturally limited to a small number of variables
and we focus on the eurozone industrial conﬁdence indicator (ICI EZ) along
with the main variable of interest the eurozone industrial production in-
dex (IPI EZ). Although a VAR model reparametrised as an error-correction
model tends to produce quite elaborate models with an interpretation that
is less obvious, this approach is investigated in order to provide a useful
benchmark for the nowcasting performances of the various single equation
alternatives. We identify a cointegrating relationship between IPI EZ and
ICI EZ and standardise on IPI EZ. The cointegrating vector is (1,-0.415)
and the adjustment coeﬃcients in both equations are signiﬁcantly diﬀerent
from zero. We include constant, seasonals and 13 lags for the diﬀerence

11

terms. The model is well-speciﬁed and stable according to the usual battery
of diagnostic tests. The model in error-correction form is estimated in order
to calculate nowcasts for IPI EZ.

Table 3: Tests for cointegrating relationship

rank
r=0
r=1

trace
29.63**
1.17

95% max. λ 95%
15.4
30.81**
14.1
3.8
1.17
3.8

A future study could examine other possible VAR modelling approaches.
One possibility would be to consider a VAR(3) with IPI EZ, ICI EZ and
IPI US and this avenue is investigated in a recent paper by Bodo, Golinelli
and Parigi (2000). A cointegrating relationship is identiﬁed for the three
endogenous variables and through tests of weak and strong exogeneity the
VAR(3) is reduced to a single-equation conditional model for IPI EZ. This
model performs comparatively well in nowcasting exercises. The second pos-
sibility would be to examine a VAR(3) with the three endogenous variables
IPI EZ, IPT and IPE. A preliminary study of this VAR(3) model reveals two
cointegrating relationships increasing the complexity of the eventual model.

6 Nowcasting Performances

We have real-time (i.e. pre-revision) eurozone IPI series for each month from
September 2000 to December 20017. This allows us to compute nowcast or
one-step ahead forecast errors for each of the candidate models and the
nowcasting results for annual IPI growth rate models are presented in the
Appendix (Tables A.1-A.3). We use a number of measures to assess the
overall nowcasting performance of each of the models. The mean absolute
error (MAE) is given by

M AE =

yi|

i|yi−
n

and the root mean squared error (RMSE) by

b

P

RM SE =

(cid:113)

yi)2

i(yi−
n

P
where n is the number of nowcasts, yi is the actual value and (cid:98)yi is the
nowcast value8. A closely related measure is the mean absolute percentage
error (MAPE) and is given by

b

7We consider revisions in the WDA eurozone 12 IPI series. The IPI series was revised
on 46 occasions between 14 May ’01 and 28 March ’02. The biggest change in eurozone
IPI is 2.65 (Jul ’95).

8These measures are typically based on the residuals for a single regression whereas
in our case we base our measures on the results for n=16 regressions i.e. we are using
real-time data and thus the dataset and regressions change from month to month.

12

M AP E =

i

yi

yi−
yi

n

c

(cid:12)
(cid:12)
(cid:12)

.

(cid:12)
(cid:12)
(cid:12)

c

P
The sign error is deﬁned as the number of times the sign of the nowcast
and the actual value do not correspond and is an important criteria in an
assessment of model nowcasting performance. The user of a model would
at least expect that a nowcast with the correct sign is produced.
It can
serve as an indicator of how well the model copes with turning points in the
series. The MAE, RMSE, MAPE and Sign Error for each model are based
on 16 nowcasts and are displayed in Tables 4 and 5 for annual and monthly
growth rate models respectively.

Annual IPI Growth Rate Models

Models C and D outperform the other model speciﬁcations for each of the
evaluation criteria. We can make a number of observations here. Firstly, the
addition of a variable based on an aggregate at day 40 increases substantively
the predictive power of the model (Models C and D superior to Model B).
We can also note that the modelling approaches that make use of partial
information at day 40 produce superior nowcasts to the non-modelling pure
statistical estimate approach9. There is no clear winner among the ’partial
information’ models C and D. While Model C produces a lower RMSE and
Sign error with respect to those of Model D, the latter model produces a
lower MAE and MAPE. Although we associate great importance to the sign
error criteria, we opt for the Model D as the optimal model given that the
model is characterised by better coherency (we can observe that for Model
D, the single sign error appears for a growth rate very close to zero and thus
is less disquieting than may appear at ﬁrst).

Models for Annual Growth Rates

A: NAIVE approach
B: GETS approach using only business survey variables
C: GETS approach incorporating partial data at day 40 (with IOBt−3)
D: GETS approach incorporating partial data at day 40 (without IOBt−3)
E: Simple Statistical Estimate based on partial data at day 40

Table 4 Nowcasting Performances of Models A - D and Simple Estimate *

9The GETS model selection strategy produces a model with a comparatively good

predictive power (Models C and D superior to Previous Eurostat Model)

13

B
1.02
1.33
0.84
1
*nowcast errors based on IPI levels estimates and real-time data;

Model
MAE
RMSE
MAPE
Sign Error

E
0.94
1.16
0.78
1

A
2.04
2.35
1.71
3

C
0.55
0.68
0.46
0

D
0.51
0.74
0.42
1

Monthly IPI Growth Rate Models

Model F serves as the benchmark model for monthly growth rate mod-
els. This approach gives a sign error of zero but the model performs poorly
under the other criteria. Results for Model G demonstrate that the intro-
duction of business survey variables produces a very noticeable improvement
in nowcasting performance. This is further improved with the introduction
of partial information to the available set of explanatory variables (Model
H). The VAR approach incorporating the industrial conﬁdence indicator
performs similarly to the single-equation equivalent with business survey
variables only. Thus, not surprisingly Model H produces the optimal re-
sults. While the sign error is non-zero the single error arises where the ac-
tual growth approximates to zero thus making the likelihood of wrong sign
prediction quite feasible. We can also note that the performance of Model H
compares favourably against that of the annual growth rate optimal model
(Model D).

Models for Monthly Growth Rates

F: Autoregressive Model
G: GETS approach using only business survey variables
H: GETS approach incorporating partial data at day 40
I: VAR(2): vector autoregressive model with IPI EZ and ICI EZ

Table 5 Nowcasting Performances of Models for Monthly Growth Rates *

Model
MAE
RMSE
MAPE
Sign Error

F
2.08
2.57
1.76
0

G
1.29
1.71
1.07
1

H
0.60
0.75
0.52
1

I
1.37
1.74
1.15
0

*nowcast errors based on IPI levels estimates and real-time data;

14

The nowcasts from Models D (for monthly growth rates) and H (for
annual growth rates) can be produced approximately with a delay of 40 days
or when the German (and Spanish data) arrive. There is a clear dependence
on the arrival of the German data but we have at least noted in Section 3 that
the volatility in the delay of the German data is relatively low. Undoubtedly,
the provision of early estimates from Germany would greatly assist this
process.

7 Conclusion

An analysis of delay times for Member States’ IPI reveals that generally the
larger Member States satisfy the 45 day deadline for the release of their IPI.
However, the volatility in delays can vary across Member States and even
the lowest delays do not compare favourably against the current delay of
15 days for the US industrial production index. Attempts to produce an
estimate appreciably earlier than the 45 day deadline would need to rely on
auxiliary information such as business survey variables and perhaps a subset
of Member States’ IPI.

The study sets out to determine whether this partial information can
provide important explanatory power in a model of Eurozone IPI. A subset
of Member States, that provide data before 40 days, is identiﬁed. An ag-
gregate IPI based on this subset can be incorporated in a model of IPI and
can contribute signiﬁcantly to the nowcasting performance of the model.
The general-to-speciﬁc approach has been followed in order to formulate
a well-speciﬁed and stable model and it is this approach that has oﬀered
most promise in a comparative study of model selection procedures. Ten-
tative well-speciﬁed and stable models that incorporate partial information
from Member States in a model of eurozone IPI demonstrate that accurate
nowcasts for the eurozone IPI can indeed be obtained.

We note that unoﬃcial (internal) IPI estimates at Member State level
could also be incorporated in a model of eurozone IPI. Indeed, in order to
bridge substantively the gap between the US IPI and eurozone IPI release
dates, the availability of early estimates of individual Member States’ IPI
for the purpose of reliably nowcasting eurozone IPI would be an important
requisite. The discussion and proposed strategy presented here can provide
useful lessons for other nowcasting exercises, for instance, the production of
nowcasts at Member State level. Clearly, information from business surveys
for individual Member States would take on greater importance in such a
study.

15

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[5] Hendry, D. and Doornik, J. (1999), Empirical Econometric Modelling

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16