LEADER 04154nam 2200673Ia 450 001 9910463687903321 005 20200504041405.0 010 $a1-4623-2750-8 010 $a1-282-84441-5 010 $a9786612844416 010 $a1-4518-7388-3 010 $a1-4527-8840-5 035 $a(CKB)3170000000055371 035 $a(EBL)1605949 035 $a(SSID)ssj0000939930 035 $a(PQKBManifestationID)11596385 035 $a(PQKBTitleCode)TC0000939930 035 $a(PQKBWorkID)10939159 035 $a(PQKB)11177470 035 $a(OCoLC)680613569 035 $a(MiAaPQ)EBC1605949 035 $a(EXLCZ)993170000000055371 100 $a20100408d2009 uf 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe role of financial variables in predicting economic activity in the Euro area$b[electronic resource] /$fprepared by Raphael Espinoza, Fabio Fornari and Marco Lombardi 210 $a[Washington, D.C.] $cInternational Monetary Fund, Middle East and Central Asia Dept.$d2009 215 $a1 online resource (56 p.) 225 1 $aIMF working paper ;$v09/241 300 $a"November 2009." 311 $a1-4519-1805-4 327 $aCover 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 327 $a1. Variance Decomposition of the GDP in the Three Areas2. R2 of a Regression of ?log 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 327 $a10. 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 330 $aThe U.S. business cycle typically leads the European cycle by a few quarters and this can be used to forecast euro area GDP. We investigate whether financial variables carry additional information. We use vector autoregressions (VARs) which include the U.S. and the euro area GDPs as a minimal set of variables as well as growth in the Rest of the World (an aggregation of seven small countries) and selected combinations of financial variables. Impulse responses (in-sample) show that shocks to financial variables influence real activity. However, according to out-of-sample forecast exercises usin 410 0$aIMF working paper ;$vWP/09/241. 606 $aBusiness cycles$zEurope 606 $aBusiness cycles$zUnited States 606 $aEconomic indicators$zEurope 606 $aEconomic indicators$zUnited States 608 $aElectronic books. 615 0$aBusiness cycles 615 0$aBusiness cycles 615 0$aEconomic indicators 615 0$aEconomic indicators 700 $aEspinoza$b Raphael A$0873063 701 $aFornari$b Fabio$0928678 701 $aLombardi$b Marco J.$f1976-$035223 712 02$aInternational Monetary Fund.$bMiddle East and Central Asia Dept. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910463687903321 996 $aThe role of financial variables in predicting economic activity in the Euro area$92087089 997 $aUNINA