05532oam 22012134 450 991078823380332120230721045634.01-4623-1341-81-4527-4908-61-4518-7024-81-282-84117-39786612841170(CKB)3170000000055071(EBL)1607942(SSID)ssj0001477988(PQKBManifestationID)11892589(PQKBTitleCode)TC0001477988(PQKBWorkID)11453221(PQKB)10440308(OCoLC)761928073(MiAaPQ)EBC1607942(IMF)WPIEE2008166(EXLCZ)99317000000005507120020129d2008 uf 0engur|n|---|||||txtccrThe Information Content of Money in Forecasting Euro Area Inflation /Emil Stavrev, Helge BergerWashington, D.C. :International Monetary Fund,2008.1 online resource (31 p.)IMF Working PapersIMF working paper ;WP/08/166Description based upon print version of record.1-4519-1477-6 Includes bibliographical references.Contents; I. Introduction; II. Related Literature; III. Models of Inflation; A. DSGE Models; B. Partial Equilibrium Models; C. Empirical Models; IV. Empirical Methods and Data; A. Estimation Techniques; B. Prior Distribution of Parameters for the Bayesian Estimates; C. Forecasting and the Information Content of Money; D. Data; V. Results; A. The Marginal Contribution of Money; Figures; 1. Forecast Performance of DSGE Models; 2. Forecast Performance of Empirical Models; 3. Forecast Performance of P* and Phillips Curve Models; B. Comparison of Money-Based Models; C. Comparison Across All ModelsTables1. Out-of-Sample Forecasting Performance of Models; VI. Conclusions; References; Appendices; I. Empirical Specifications; II. Bayesian PriorsThis paper contributes to the debate on the role of money in monetary policy by analyzing the information content of money in forecasting euro-area inflation. We compare the predictive performance within and among various classes of structural and empirical models in a consistent framework using Bayesian and other estimation techniques. We find that money contains relevant information for inflation in some model classes. Money-based New Keynesian DSGE models and VARs incorporating money perform better than their cashless counterparts. But there are also indications that the contribution of money has its limits. The marginal contribution of money to forecasting accuracy is often small, money adds little to dynamic factor models, and it worsens forecasting accuracy of partial equilibrium models. Finally, non-monetary models dominate monetary models in an all-out horserace.IMF Working Papers; Working Paper ;No. 2008/166Monetary policyEconometric modelsMoneyEconometric modelsInflation (Finance)ForecastingEconometric modelsEconometricsimfInflationimfMoney and Monetary PolicyimfForecastingimfForecasting and Other Model ApplicationsimfPrice LevelimfDeflationimfComputable and Other Applied General Equilibrium ModelsimfClassification MethodsimfCluster AnalysisimfPrincipal ComponentsimfFactor ModelsimfDemand for MoneyimfEconomic ForecastingimfMacroeconomicsimfEconometrics & economic statisticsimfMonetary economicsimfEconomic forecastingimfDynamic stochastic general equilibrium modelsimfFactor modelsimfDemand for moneyimfPricesimfEconometric modelsimfMoneyimfNew ZealandimfMonetary policyEconometric models.MoneyEconometric models.Inflation (Finance)ForecastingEconometric models.EconometricsInflationMoney and Monetary PolicyForecastingForecasting and Other Model ApplicationsPrice LevelDeflationComputable and Other Applied General Equilibrium ModelsClassification MethodsCluster AnalysisPrincipal ComponentsFactor ModelsDemand for MoneyEconomic ForecastingMacroeconomicsEconometrics & economic statisticsMonetary economicsEconomic forecastingDynamic stochastic general equilibrium modelsFactor modelsDemand for moneyPricesEconometric modelsMoney332.46Stavrev Emil1465934Berger Helge1462107DcWaIMFBOOK9910788233803321The Information Content of Money in Forecasting Euro Area Inflation3704167UNINA02621nam 2200625 450 991082415490332120200520144314.01-283-87855-01-55469-375-6(CKB)2560000000072044(EBL)682819(OCoLC)659175731(SSID)ssj0000482022(PQKBManifestationID)11291756(PQKBTitleCode)TC0000482022(PQKBWorkID)10484066(PQKB)10634173(CEL)435564(CaBNvSL)slc00225966(Au-PeEL)EBL682819(CaPaEBR)ebr10773282(CaONFJC)MIL419105(VaAlCD)20.500.12592/z6nzff(MiAaPQ)EBC682819(EXLCZ)99256000000007204420111102d2010 uy 0engur|n|---|||||txtccrOrca currents resource guide /[written by Susan Grey, Janice Reynolds and Kate Lane Hill]Victoria, British Columbia :Orca Book Publishers,2010.1 online resource (307 p.)Orca currentsDescription based upon print version of record.1-55143-956-5 The value of using Orca Currents in the classroom -- Orca Currents and reading levels -- How to use this curriculum guide -- Classroom teaching ideas - reading workshop - individual silent reading -- Literature circles -- Assigning students to book discussion groups -- Student ownership of book discussion groups -- Modeling responses to literature -- Written preparation for literature circle discussion -- Dialectical journals -- Assessment- informal -- Assessment- formal -- Culminating activities -- Author biographies -- Book summaries -- Books grouped by subject -- Appendix A : Lexile levels -- Appendix B : Frey reading levels -- Index of teachers' guides.This resource guide enables a teacher to implement the Orca Currents series as part of a comprehensive independent reading and literacy unit.Orca currents.ReadingRemedial teachingReading (Middle school)ReadingRemedial teaching.Reading (Middle school)372.4Grey Susan1628446Hill Kate Lane1628449Reynolds Janice1628447MiAaPQMiAaPQMiAaPQBOOK9910824154903321Orca currents resource guide3965592UNINA