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, 00184 Roma, Italy Email: moauro@istat.it EUROSTAT task force ‘GDP Flash at T+30 days’ Working group Methods and estimation technique Contribution to the second meeting, Lisbon, 9 December 2013

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

Introduction Quarterly GDP probably the most relevant economic short term statistics Towards a 30-60-90 days timetable EA and EU data compiled according to the ‘direct method’ MS data are required Focus  SA growth rates of volume measures

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:

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

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

The Italian industial value added, IPI and the confidence indicator

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

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)

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)

Estimating Italian service value added: an example

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

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.

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.