1. Methods and estimation techniques of euro area GDP flash at T+30 days: preliminary reflections Filippo Moauro ISTAT - Direzione centrale di Contabilità Nazionale, Via A. Depretis 74/B, Roma, Italy EUROSTAT task force ‘GDP Flash at T+30 days’ Working group Methods and estimation technique Contribution to the second meeting, Lisbon, 9 December 2013

2. Layout of the presentation
Introduction
Main challenges
A first case study
Modelling strategy
Forecasting
Compilation issues and chain linking
Short conclusions

3. Introduction
Quarterly GDP probably the most relevant economic short term statistics
Towards a days timetable
EA and EU data compiled according to the ‘direct method’
MS data are required
Focus  SA growth rates of volume measures

4. Main challenges (1) yt is available until quarter T-1
two dimensions of the problem:
1) estimation method used in QNA
production
(a) direct; (b) indirect;
2) availability of information on related data xt
(1) full; (2) partial (3) absent
six situations might be identified:

5. Main challenges (2) yt is not available at T+30 days Related series xt
fully available
partially available
not available
Direct method
Vary rare
Adoption of an alternative method
based on xt
xt is monthly and only 1-2 months are observed
Forecasting xt
Exstrapolate yt
1)Pure forecasting
2) exploit econometric relationships with other variables
Indirect method
Very rare
Use of the same method as current QNA
Production
Temporal disaggregation yt

6. Italian industrial value added, production and confidence indexes
date
industrial value added
industrial production
confidence indicator
levels
growth rates
2012q1
60,496
-1.1
96.2
-2.3
91.1
-1.9
2012q2
60,139
-0.6
94.6
-1.7
88.6
-2.7
2012q3
60,552
0.7
95.0
0.4
87.5
-1.2
2012q4
58,938
91.8
-3.3
88.1
0.6
2013q1
58,594
91.5
-0.3
88.7
2013q2
58,538
-0.1
91.3
-0.2
89.5
0.9
2013q3 -
94.2
5.1
july
0.3
92.3
1.5
august
91.2
-0.4
93.5
1.3
september
96.8
3.5

7. The Italian industial value added, IPI and the confidence indicator

8. Modelling strategies (1)
First classification:
1) pure forecasting methods
2) use of explanatory variables
Flash estimate at T+30 is a composite exercise
Bridge models:
(1) prediction of unobserved months of xt
(2) aggregate at the quarterly frequency
(3) its use in a regression with yt as dependent variable
Large use of dynamic regressions  ADL models

9. Modelling strategies (2)
Pure forecasting methods:
1) ARIMA models
2) STS models
Multivariate extensions
1) VAR models
2) SUTSE models
3) dynamic factor analysis
Models handling mixed frequency data
Other methods (state-dependent models)

10. Forecasting The exercise implies: (1) choice of the model class
(2) choice of model specification within chosen class
Goal  accurate forecasts
(a) low ex-ante forecast errors
(b) low ex-post forecast errors
Good practice
(i) rolling forecasting exercise
(ii) comparison based on synthetic error statistics (MAE, RMSE, others)

11. Estimating Italian service value added: an example

12. ADL(1,1) model yt = c + φyt-1 + β0xt + β1xt-1 + εt, εt~N(0, σ2)
Service value added  yt
Industrial value added  xt

13. Compilation issues and chain linking
First estimation of GDP-components
Aggregation of GDP-components adopting chain linking rules:
(1) GDP components are de-chained and put
in terms of previous year prices;
(2) previous year data are summed up to
obtain GDP;
(3) GDP at previous year prices is chain linked.

14. Short conclusions First proposal of possible scenarios
Two case studies based on Italian data
(1) graphical analysis and role of transformations;
(2) hypothesis on estimating service data using available related data
Next steps:
- complete the scenarios;
- put in evidence the role of detailed data;
- guidelines with definition of A-B-C methods.