Nowcasting Eurozone Industrial Production

2003 EDITION

THEME 1 General EUROPEAN statistics COMMISSION 1 Europe Direct is a service to help you find answers to your questions about the European Union New freephone number: 00 800 6 7 8 9 10 11

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Luxembourg: Office for Official Publications of the European Communities, 2003

ISBN 92-894-3416-3 ISSN 1725-4825

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 incorporate 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...... 3

2. Evolution of Arrival Delays ...... 3

3. Prospects for an Early IPI ...... 5

4. Developing a Methodology for Producing IPI Nowcasts ...... 6

5. Models for Eurozone IPI...... 7 5.1 Annual IPI Growth Rates ...... 7 5.1.1 Model A: NAIVE Model ...... 7 5.1.2 Model B: GETS with Business Surveys...... 8 5.1.3 Model C: GETS with Business Surveys and Partial Information (1) ...... 9 5.1.4 Model D: GETS with Business Surveys and Partial Information (2) ...... 9 5.1.5 Model E: Simple Statistical Estimate ...... 10 5.1.6 Previous Eurostat Study...... 10 5.2 Monthly IPI Growth Rates ...... 10 5.3 VAR Modelling of Eurozone IPI Monthly Growth Rates...... 11

6. Nowcasting Performances...... 12

7. Conclusion ...... 15

References...... 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 incorporate 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 nowcasts 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 countries 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 received 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 before 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 30% 40% 50% 60% 70% 80% Oct ’00 37 37 41 45 45 49 Nov ’00 43 47 47 47 48 50 Dec ’00 39 39 39 44 46 47 Jan ’01 37 40 40 43 43 44 Feb ’01 40 40 40 43 44 44 Mar ’01 40 40 44 44 45 45 Apr ’01 39 42 42 44 45 45 May ’01 39 39 40 42 43 46 Jun ’01 38 39 39 40 41 45 Jul ’01 38 42 42 44 51 66 Aug ’01 39 40 40 45 45 45 Sep ’01 39 39 44 44 45 46 Oct ’01 37 40 41 42 43 44

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 Jan Feb Mar Apr May Jun Jul Aug Sep Oct 45 85.7 92.7 92.7 92.6 78.0 92.7 61.3 89.5 76.0 93.9 44 85.7 92.7 60.1 62.8 78.0 77.5 61.3 59.7 60.9 93.9 43 70.6 62.9 44.5 59.6 78.0 77.5 58.1 59.7 45.3 79.2 42 53.0 58.0 44.5 58.4 62.9 77.5 51.9 59.7 45.3 60.9 41 53.0 56.8 44.5 31.2 59.1 77.5 31.3 59.7 45.3 59.7 40 53.0 53.0 41.3 31.2 55.9 68.5 31.3 55.9 45.3 49.7 39 37.4 17.2 1.7 31.2 48.5 50.0 31.3 34.1 44.1 34.1 38 37.4 17.2 1.7 1.6 3.3 31.2 31.3 3.2 3.3 34.1 37 37.4 17.2 1.7 1.6 3.3 0 1.6 3.2 1.6 34.1 36 1.6 0 0 1.6 3.3 0 1.6 3.2 1.6 1.7 35 1.6 0 0 1.6 1.7 0 1.6 3.2 1.6 1.7

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 nowcast 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 interpretation.

• 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 approach). 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 examine a number of potential model specifications. Initially we focus on modelling annual growth rates for eurozone IPI i.e. (IPIt − IPIt−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 performance of other competing methodologies. In the next section we turn our attention to monthly growth rates calculated using a log difference ap- proximation (log IPIt − log IPIt−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

IPIt = deterministic terms +0.05 ∗ IPIt−1 + 0.16 ∗ IPIt−2 + 0.14 ∗ IPTt −0.08 ∗ IOBt−3 + 0.22 ∗ IPEt−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 delays. 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 countries 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

IPIt = deterministic terms +0.11∗IPTt−0.04∗IOBt−3+0.07∗IPEt−1+ partial 0.51 ∗ IPIt + errort

partial where IPIt 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 autocorrelation 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.

IPIt = deterministic terms +0.10 ∗ IPTt + 0.005 ∗ IPEt−1 + 0.59 ∗ partial IPIt + 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 explanatory variables into the model have led to implausible coefficients signs. This is somewhat puzzling and can only be only partially explained by presence 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

IPIt = deterministic terms ITA FRA partial +0.03 ∗ IPEt + 0.02 ∗ IPTt + 0.05 ∗ IPTt + 0.59 ∗ IPIt + εt

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

P14 ∆ipit = deterministic terms + ∆ipit−i + εt i=0

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

P14 P3 P3 ∆ipit = deterministic terms + ∆ipit−i + aiIPTt−j + IPEt−k + i=0 j=0 k=0 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 P14 P3 P3 ∆ipit = deterministic terms + ∆ipit−i + aiIPTt−j + IPEt−k + i=0 j=0 k=0 3 P partial ∆ipit−l + εt l=0

5.3 VAR Modelling of Eurozone IPI Monthly Growth Rates We attempt to assess the relative performance of modelling a system of variables 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 index (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 trace 95% max. λ 95% r=0 29.63** 14.1 30.81** 15.4 r=1 1.17 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 possibility 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 P b i|yi−yi| MAE = n and the root mean squared error (RMSE) by q P b 2 i(yi−yi) RMSE = n

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

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.

P c yi−yi c i yi MAP E = n .

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 Model A B C D E MAE 2.04 1.02 0.55 0.51 0.94 RMSE 2.35 1.33 0.68 0.74 1.16 MAPE 1.71 0.84 0.46 0.42 0.78 Sign Error 3 1 0 1 1 *nowcast errors based on IPI levels estimates and real-time data;

Monthly IPI Growth Rate Models

Model F serves as the benchmark model for monthly growth rate models. This approach gives a sign error of zero but the model performs poorly under the other criteria. Results for Model G demonstrate that the introduction 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 results. While the sign error is non-zero the single error arises where the actual 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 F G H I MAE 2.08 1.29 0.60 1.37 RMSE 2.57 1.71 0.75 1.74 MAPE 1.76 1.07 0.52 1.15 Sign Error 0 1 1 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 aggregate 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 References

[1] Blake, A., Kapetanios, G. and Weale, M. (2000), Industrial Production Nowcasting. Report Submitted to Eurostat by NIESR.

[2] Bodo, G., Golinelli, R. and Parigi, G. (2000), Forcasting Industrial Pro- duction in the Euro Area, Temi di discussione, No. 370, Banca D’Italia.

[3] Donzel, F. (2001), Study on the Industrial Production Index. Eurostat.

[4] Hamilton, J. (1994), Time Series Analysis, Princeton University Press.

[5] Hendry, D. and Doornik, J. (1999), Empirical Econometric Modelling Using PcGive, Timberlake Consultants Ltd.

[6] Ladiray, D. (1997), Using Business Survey Data to Forecast Employ- ment. Invited Paper, 51st Session of the International Statistical Insti- tute, Istanbul.

[7] Rendu, C. (2000), Constructing a Leading Indicator for Euroland Indus- trial Output: Why and How. Morgan Stanley Dean Witter.