The Role of Financial Variables in Predicting Economic Activity in the Euro Area / / Marco Lombardi, Raphael Espinoza, Fabio Fornari

• Record Nr.: UNINA9910788224903321
• Autore: Lombardi Marco
• Pubbl/distr/stampa: Washington, D.C. : , : International Monetary Fund, , 2009
• ISBN: 1-4623-2750-8; 1-282-84441-5; 9786612844416; 1-4518-7388-3; 1-4527-8840-5
• Descrizione fisica: 1 online resource (56 p.)
• Collana: IMF Working Papers
• Altri autori (Persone): EspinozaRaphael; FornariFabio
• Soggetti: Business cycles - Europe; Business cycles - United States; Economic indicators - Europe; Economic indicators - United States; Banks and Banking; Econometrics; Finance: General; Statistics; Industries: Financial Services; Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; General Financial Markets: General (includes Measurement and Data); Interest Rates: Determination, Term Structure, and Effects; Data Collection and Data Estimation Methodology; Computer Programs: Other; Banks; Depository Institutions; Micro Finance Institutions; Mortgages; Finance; Econometrics & economic statistics; Vector autoregression; Stock markets; Yield curve; Financial statistics; Loans; Stock exchanges; Interest rates; United States
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "November 2009."
• Nota di contenuto: Cover Page; Title Page; Copyright Page; Contents; I. Introduction; II. The VAR models; A. Data; 1. Rates of Growth of Real GDP in the Three Economic Areas (quarter-on-quarter); B. Specifications; III. Characterizing the Models; A. IRFs and Pre-1985 and Post-1985 Evidence; 2. Impulse Response Functions from a Trivariate VAR; 3. Impulse Response Function from a 9-Variable VAR; 4. Impulse Response Function to GDP Shocks Across Sub-Samples; 5. Impulse Response Functions Across Sub-Samples; B. Linkages and the Role of Financial Shocks; 6. Forecast Error Variance Decomposition for the Euro Area GDP; 1. Variance Decomposition of the GDP in the Three Areas2. R2 of a Regression of Δlog GDP on its Counterfactual; 7. Historical Decomposition; IV. Out-of-Sample Evidence; A. 'Unconditional' Forecast Evaluation; 3. Unconditional Out-of-Sample RMSE; B. Conditional Forecast Evaluation; 4. Out-of-Sample RMSE; 5. Out-of-Sample RMSE; C. Additional Explanatory Factors; 6. Conditional Choice Between Models at Selected Horizons; V. Conditional Evaluation; A. Rolling RMSEs; 8. RMSE from Competing Classes of Models; 9. RMSE from Competing Classes of Models (ctd.); B. Conditional Predictive Ability Test; 10. GW Test for Conditional Predictive - Random Walk Model11. GW Test for Conditional Predictive Ability - 2 GDP VAR; 12. GW Test for Conditional Predictive Ability - 3 GDP VAR; VI. Conclusions; References; Footnotes
• Sommario/riassunto: 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.