1. 1

2. 2 publication of quarterly national accounts within 70 days after the end of reference period State of the art in European statistics: flash estimates in 45 days National account data High frequency data Key targets: Improvements in releases of short term indicators BRIDGE MODELS Need timelier information about National accounts

3. 3 BRIDGE MODELS High frequency data National accounts variables One bridge equation for each NA variable (Semi-structural ARDL equations + indicators) The whole set of regressors (lagged endogenous and exogenous variables) should be known over the projection period (“Nowcast”) No need of Bridge Models for weather forecasts!!

4. 4 Comparing Forecasting performance Area wide aggregate Bridge equation Benchmark models (aggregate and disaggregate).. Disaggregate Bridge Models Larger information set Aggregate or Disaggregate? National DataHigh frequency real data Limited information set Area wide data

5. 5 Benchmark and Bridge models Forecasts Private consumption Collective consumption Gross fixed capital formation Imports of goods and services GDP= CON + COC + INV + EXP - IMP + VSP GDP Changes in stocks ________________________________________________________________ (GDP+Imports) SUPPLY SIDE DEMAND SIDE Exports of goods and services Bus. Surveys (exp. orders), constr. components Retail sales, cons. conf, URUnivariate model Trade variables, real exch. Rates, IP, surveys IP, business surveysGDP, surveys

6. 6 Aggregate supply-side equation How to forecast euro area GDP Euro Area GDP 1) Forecast GDP For France, Germany and Italy 2) Run a regression of Euro area GDP growth rate on countries GDP growth rates 3) Apply coefficients estimated in 2) to GDP forecasts in 1) to get a euro area GDP forecast

7. 7 Horse race Area wide aggregate Bridge equation Benchmark models (aggregate and disaggregate).. Disaggregate Bridge Models

8. 8 Forecasts comparison RMSE OF THE EURO AREA GDP FORECASTS (1999.1-2001.2) Area-wide modelsAggregation of national-models ARIMA0.32ARIMA0.33 AR(5) model0.35AR(5) models0.32 Structural equation0.37VAR0.34 Aggregate supply-side equation0.34BM supply-side equations0.12 BM demand-side equations0.25 BM average of supply and demand0.14

9. 9 Forecasts comparison RMSE OF SINGLE-COUNTRY GDP FORECASTS (1999.1-2001.2) Germany France Italy ARIMA0.600.300.31 AR(5)0.600.280.35 VAR 0.60 0.340.36 BM supply-side0.320.150.16 BM demand-side0.360.450.67 BM average0.200.280.31

10. 10 CONCLUSIONS Bridge models always better than benchmark models Forecasts with national data perform better than the aggregate bridge model