LEADER 05958nam 2200625Ia 450 001 9910797938203321 005 20160316083946.0 010 $a1-78560-352-3 035 $a(CKB)3710000000570352 035 $a(EBL)4500555 035 $a(MiAaPQ)EBC4500555 035 $a(Au-PeEL)EBL4500555 035 $a(CaPaEBR)ebr11203792 035 $a(CaONFJC)MIL889666 035 $a(OCoLC)948378287 035 $a(UtOrBLW)bslw09407010 035 $a(EXLCZ)993710000000570352 100 $a20160316d2016 uy 0 101 0 $aeng 135 $aurun||||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aDynamic factor models$b[electronic resource] /$fedited by Eric Hillebrand, Siem Jan Koopman 210 1$aWagon Lane, Bingley, [England] :$cEmerald Group Publishing Limited,$d2016. 210 4$dİ2016 215 $a1 online resource (685 p.) 225 1 $aAdvances in econometrics,$x0731-9053 ;$vv. 35 300 $aDescription based upon print version of record. 311 $a1-78560-353-1 320 $aIncludes bibliographical references at the end of each chapters. 327 $aFront Cover; Dynamic Factor Models; Copyright page; Contents; List of Contributors; Editorial Introduction; Dynamic Factor Models: A Brief Retrospective; Notes; References; Part I: Methodology; An Overview of the Factor-augmented Error-Correction Model; 1. Introduction; 2. Factor-augmented error-correction model; 2.1. Representation of the FECM; 2.2. The FECM Form for Forecasting; 2.3. The FECM Form for Structural Analysis; 3. Data and empirical applications; 4. Forecasting macroeconomic variables; 4.1. Forecasting Results for the Euro Area; 4.2. Forecasting Results for the United States 327 $a4.3. Robustness Check to I(1) Idiosyncratic Errors5. Transmission of Monetary Policy Shocks in the FECM; 6. Conclusions; Notes; Acknowledgements; References; Appendix A. Additional Forecasting Results; Estimation of VAR Systems from Mixed-Frequency Data: The Stock and the Flow Case; 1. Introduction; 2. Mixed-Frequency Estimators; 2.1 Extended Yule-Walker Estimators: The Stock Case; 2.2 Extended Yule-Walker Estimators: The General Case; 2.3 Maximum Likelihood Estimation and the EM Algorithm; 3. Projecting the MF Estimators on the Parameter Space 327 $a3.1 Stabilization of the Estimated System Parameters3.2 Positive (Semi)-Definiteness of the Noise Covariance Matrix; 4. Asymptotic Properties of the XYW/GMM Estimators; 5. Simulations; 6. Outlook and Conclusions; Acknowledgments; References; Appendix; Modeling Yields at the Zero Lower Bound: Are Shadow Rates the Solution?; 1. Introduction; 2. A Standard Gaussian Term Structure Model; 2.1. The General Model; 2.2. The CR Model; 2.3. Negative Short-Rate Projections in Standard Models; 3. A Shadow-Rate Model; 3.1. The Option-Based Approach to the Shadow-Rate Model; 3.2. The B-CR Model 327 $a3.3. Measuring the Effect of the ZLB3.4. Nonzero Lower Bound for the Short Rate; 4. Comparing Affine and Shadow-Rate Models; 4.1. Analysis of Parameter Estimates; 4.2. In-Sample Fit and Yield Volatility; 4.3. Forecast Performance; 4.3.1. Short-Rate Forecasts; 4.3.2. Yield Forecasts; 4.4. Decomposing 10-Year Yields; 4.5. Assessing Recent Shifts in Near-Term Monetary Policy Expectations; 5. Conclusion; Notes; Acknowledgments; References; Appendix A: How Good is the Option-Based Approximation?; Appendix B: Formula for Policy Expectations in AFNS and B-AFNS Models 327 $aAppendix C: Analytical Formulas for Averages of Policy Expectations and for Term Premiums in the CR ModelDynamic Factor Models for the Volatility Surface; 1. Introduction; 2. Volatility Surface Data; 2.1. Constructing the Volatility Surface; 2.2. Summary Statistics and Preliminary Analysis; 3. Models for the Volatility Surface; 3.1. General DFM; 3.2. Restricted Economic DFMs; 3.3. Spline-Based DFMs; 4. Main Results; 5. Robustness and Extensions; 5.1. Alternative Surface Construction; 5.2. Higher-Dimensional Models; 5.3. Alternative Factor Dynamics 327 $a5.4. Alternative Sample Period and Log-Transformation 330 $aDynamic factor models (DFM) constitute an active and growing area of research, both in econometrics, in macroeconomics, and in finance. Many applications lie at the center of policy questions raised by the recent financial crises, such as the connections between yields on government debt, credit risk, inflation, and economic growth. This volume collects a key selection of up-to-date contributions that cover a wide range of issues in the context of dynamic factor modeling, such as specification, estimation, and application of DFMs. Examples include further developments in DFM for mixed-frequency data settings, extensions to time-varying parameters and structural breaks, for multi-level factors associated with subsets of variables, in factor augmented error correction models, and in many other related aspects. A number of contributions propose new estimation procedures for DFM, such as spectral expectation-maximization algorithms and Bayesian approaches. Numerous applications are discussed, including the dating of business cycles, implied volatility surfaces, professional forecaster survey data, and many more. 410 0$aAdvances in econometrics ;$vv. 35. 606 $aBusiness & Economics$xEconomics$xMacroeconomics$2bisacsh 606 $aEconometrics$2bicssc 606 $aMacroeconomics 606 $aMacroeconomics$xEconometric models 615 7$aBusiness & Economics$xEconomics$xMacroeconomics. 615 7$aEconometrics. 615 0$aMacroeconomics. 615 0$aMacroeconomics$xEconometric models. 676 $a339 701 $aHillebrand$b Eric$01540426 701 $aKoopman$b S. J$g(Siem Jan)$0495487 801 0$bUtOrBLW 906 $aBOOK 912 $a9910797938203321 996 $aDynamic factor models$93792077 997 $aUNINA