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Methods and Estimation: Euro Area GDP Flash at T+30 Days

This paper discusses the methods and estimation techniques for Euro Area GDP flash at T+30 days, including challenges, modeling strategies, forecasting, and compilation issues. It covers examples and concludes with proposed scenarios and next steps.

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Methods and Estimation: Euro Area GDP Flash at T+30 Days

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  1. Methods and estimation techniques of euro area GDP flash at T+30 days: preliminary reflectionsFilippoMoauroISTAT - Direzione centrale di Contabilità Nazionale, Via A. Depretis 74/B, 00184 Roma, ItalyEmail: moauro@istat.itEUROSTAT task force ‘GDP Flash at T+30 days’Working group Methods and estimation techniqueContribution 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 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

  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)

  6. Italian industrial value added, production and confidence indexes

  7. The Italianindustialvalueadded, IPI and the confidenceindicator

  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 valueadded yt • Industrial valueadded 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.

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