Dynamic factor models / / edited by Eric Hillebrand, Siem Jan Koopman

• Pubblicazione: Wagon Lane, Bingley, [England] : , : Emerald Group Publishing Limited, , 2016; ©2016
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (685 p.)
• Disciplina: 339
• Soggetto topico: Business & Economics - Economics - Macroeconomics; Econometrics; Macroeconomics; Macroeconomics - Econometric models
• Altri autori: HillebrandEric KoopmanS. J (Siem Jan)
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references at the end of each chapters.
• Nota di contenuto: Front 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 ; 4.3. 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 ; 3.1 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 ; 3.3. 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 ; Appendix 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 ; 5.4. Alternative Sample Period and Log-Transformation
• Sommario/riassunto: Dynamic 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.
• Titolo autorizzato: Dynamic factor models
• ISBN: 1-78560-352-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910826114903321
• Lo trovi qui: Univ. Federico II
• Serie: Advances in econometrics ; ; v. 35.