Dynamic factor models /

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Bibliographic Details
Edition:First edition.
Imprint:United Kingdom : Emerald Group Publishing Limited, 2016.
Description:1 online resource
Language:English
Series:Advances in econometrics ; vol. 35
Advances in econometrics ; vol. 35.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11306661
Hidden Bibliographic Details
Other authors / contributors:Hillebrand, Eric, editor.
Koopman, S. J. (Siem Jan), editor.
ISBN:9781785603525
1785603523
1785603531
9781785603532
9781785603532
Notes:Includes bibliographical references.
Vendor-supplied metadata.
Summary:This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.
Other form:Print version: Hillebrand, Eric. Dynamic Factor Models. Bradford, West Yorkshire : Emerald Group Publishing Limited, ©2016 9781785603532
Table of Contents:
  • 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.