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The Role of Financial Variables in Predicting Economic Activity in the Euro Area.

The U.S. business cycle typically leads the European cycle by a few quarters and this can be used to forecast euro area GDP. We investigate whether financial variables carry additional information. We use vector autoregressions (VARs) which include the U.S. and the euro area GDPs as a minimal set of...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Lombardi, Marco
Otros Autores: Espinoza, Raphael A., Fornari, Fabio
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Washington : International Monetary Fund, 2009.
Colección:IMF Working Papers.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Lombardi, Marco. 
245 1 4 |a The Role of Financial Variables in Predicting Economic Activity in the Euro Area. 
260 |a Washington :  |b International Monetary Fund,  |c 2009. 
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490 1 |a IMF Working Papers ;  |v v. Working Paper No. 09/241 
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520 3 |a The U.S. business cycle typically leads the European cycle by a few quarters and this can be used to forecast euro area GDP. We investigate whether financial variables carry additional information. We use vector autoregressions (VARs) which include the U.S. and the euro area GDPs as a minimal set of variables as well as growth in the Rest of the World (an aggregation of seven small countries) and selected combinations of financial variables. Impulse responses (in-sample) show that shocks to financial variables influence real activity. However, according to out-of-sample forecast exercises using the Root Mean Square Error (RMSE) metric, this macro-financial linkage would be weak: financial indicators do not improve short and medium term forecasts of real activity in the euro area, even when their timely availability, relative to GDP, is exploited. This result is partly due to the 'average' nature of the RMSE metric: when forecasting ability is assessed as if in real time (conditionally on the information available at the time of the forecast), we find that models using financial variables would have been preferred, ex ante, in several episodes, in particular between 1999 and 2002. This result suggests that one should not discard, on the basis of RMSE statistics, the use of predictive models that include financial variables if there is a theoretical prior that a financial shock is affecting growth. 
505 0 |a I. Introduction; II. The VAR models; A. Data; B. Specifications; III. Characterizing the Models; A. IRFs and Pre-1985 and Post-1985 Evidence; B. Linkages and the Role of Financial Shocks; IV. Out-of-Sample Evidence; A. 'Unconditional' Forecast Evaluation; B. Conditional Forecast Evaluation; C. Additional Explanatory Factors; V. Conditional Evaluation; A. Rolling RMSEs; B. Conditional Predictive Ability Test; VI. Conclusions; References; Figures ; 1. Rates of Growth of Real GDP in the Three Economic Areas (quarter-on-quarter); 2. Impulse Response Functions from a Trivariate VAR. 
505 8 |a 3. Impulse Response Function from a 9-Variable VAR4. Impulse Response Function to GDP Shocks Across Sub-Samples; 5. Impulse Response Functions Across Sub-Samples; 6. Forecast Error Variance Decomposition for the Euro Area GDP; 7. Historical Decomposition; 8. RMSE from Competing Classes of Models; 9. RMSE from Competing Classes of Models (ctd.); 10. GW Test for Conditional Predictive -- Random Walk Model; 11. GW Test for Conditional Predictive Ability -- 2 GDP VAR; 12. GW Test for Conditional Predictive Ability -- 3 GDP VAR; Tables ; 1. Variance Decomposition of the GDP in the Three Areas. 
505 8 |a 2. R2 of a Regression of Elog GDP on its Counterfactual3. Unconditional Out-of-Sample RMSE; 4. Out-of-Sample RMSE; 5. Out-of-Sample RMSE; 6. Conditional Choice Between Models at Selected Horizons. 
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650 0 |a Economic indicators  |z Europe. 
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650 6 |a Cycles économiques  |z Europe. 
650 6 |a Cycles économiques  |z États-Unis. 
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700 1 |a Espinoza, Raphael A. 
700 1 |a Fornari, Fabio. 
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