LEADER 07109oam 22013094 450 001 9910811772703321 005 20240402044750.0 010 $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(IMF)WPIEE2009241 035 $a(EXLCZ)993170000000055371 100 $a20020129d2009 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 /$fMarco Lombardi, Raphael Espinoza, Fabio Fornari 205 $a1st ed. 210 1$aWashington, D.C. :$cInternational Monetary Fund,$d2009. 215 $a1 online resource (56 p.) 225 1 $aIMF Working Papers 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 3 $aThe U.S. business cycle typically leads the European cycle by a few quarters and this can be used to forecast euro area GDP. We investigate whether financial variables carry additional information. We use vector autoregressions (VARs) which include the U.S. and the euro area GDPs as a minimal set of variables as well as growth in the Rest of the World (an aggregation of seven small countries) and selected combinations of financial variables. Impulse responses (in-sample) show that shocks to financial variables influence real activity. However, according to out-of-sample forecast exercises using the Root Mean Square Error (RMSE) metric, this macro-financial linkage would be weak: financial indicators do not improve short and medium term forecasts of real activity in the euro area, even when their timely availability, relative to GDP, is exploited. This result is partly due to the 'average' nature of the RMSE metric: when forecasting ability is assessed as if in real time (conditionally on the information available at the time of the forecast), we find that models using financial variables would have been preferred, ex ante, in several episodes, in particular between 1999 and 2002. This result suggests that one should not discard, on the basis of RMSE statistics, the use of predictive models that include financial variables if there is a theoretical prior that a financial shock is affecting growth. 410 0$aIMF Working Papers; Working Paper ;$vNo. 2009/241 606 $aBusiness cycles$zEurope 606 $aBusiness cycles$zUnited States 606 $aEconomic indicators$zEurope 606 $aEconomic indicators$zUnited States 606 $aBanks and Banking$2imf 606 $aEconometrics$2imf 606 $aFinance: General$2imf 606 $aStatistics$2imf 606 $aIndustries: Financial Services$2imf 606 $aTime-Series Models$2imf 606 $aDynamic Quantile Regressions$2imf 606 $aDynamic Treatment Effect Models$2imf 606 $aDiffusion Processes$2imf 606 $aGeneral Financial Markets: General (includes Measurement and Data)$2imf 606 $aInterest Rates: Determination, Term Structure, and Effects$2imf 606 $aData Collection and Data Estimation Methodology$2imf 606 $aComputer Programs: Other$2imf 606 $aBanks$2imf 606 $aDepository Institutions$2imf 606 $aMicro Finance Institutions$2imf 606 $aMortgages$2imf 606 $aFinance$2imf 606 $aEconometrics & economic statistics$2imf 606 $aVector autoregression$2imf 606 $aStock markets$2imf 606 $aYield curve$2imf 606 $aFinancial statistics$2imf 606 $aLoans$2imf 606 $aStock exchanges$2imf 606 $aInterest rates$2imf 607 $aUnited States$2imf 615 0$aBusiness cycles 615 0$aBusiness cycles 615 0$aEconomic indicators 615 0$aEconomic indicators 615 7$aBanks and Banking 615 7$aEconometrics 615 7$aFinance: General 615 7$aStatistics 615 7$aIndustries: Financial Services 615 7$aTime-Series Models 615 7$aDynamic Quantile Regressions 615 7$aDynamic Treatment Effect Models 615 7$aDiffusion Processes 615 7$aGeneral Financial Markets: General (includes Measurement and Data) 615 7$aInterest Rates: Determination, Term Structure, and Effects 615 7$aData Collection and Data Estimation Methodology 615 7$aComputer Programs: Other 615 7$aBanks 615 7$aDepository Institutions 615 7$aMicro Finance Institutions 615 7$aMortgages 615 7$aFinance 615 7$aEconometrics & economic statistics 615 7$aVector autoregression 615 7$aStock markets 615 7$aYield curve 615 7$aFinancial statistics 615 7$aLoans 615 7$aStock exchanges 615 7$aInterest rates 676 $a338.5443094 700 $aLombardi$b Marco$035223 701 $aEspinoza$b Raphael$01143129 701 $aFornari$b Fabio$01627344 712 02$aInternational Monetary Fund.$bMiddle East and Central Asia Dept. 801 0$bDcWaIMF 906 $aBOOK 912 $a9910811772703321 996 $aThe Role of Financial Variables in Predicting Economic Activity in the Euro Area$93963891 997 $aUNINA