Essays in Honor of Cheng Hsiao

Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometricsis published in honour of Cheng Hsiao.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Terrell, Dek
Otros Autores: Li, Tong, Pesaran, M. Hashem
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bingley : Emerald Publishing Limited, 2020.
Colección:Advances in Econometrics Ser.
Temas:
Econometrics.
Économétrie.
Business & Economics > Econometrics.
Econometrics
Acceso en línea:Texto completo
Texto completo
Tabla de Contenidos:
  • Intro
  • Title Page
  • Copyright Page
  • Contents
  • Introduction
  • References
  • Chapter 1: Correction for the Asymptotical Bias of the Arellano-Bond type GMM Estimation of Dynamic Panel Models
  • 1. Introduction
  • 2. Model and the Arellano-Bond GMM Estimation
  • 2.1. The Arellano-Bond GMM Estimation and its Asymptotical Bias
  • 2.2. JIVE Estimation
  • 3. Model with Exogenous Variables
  • 4. Monte Carlo Simulation
  • 5. Conclusion
  • Notes
  • References
  • Appendix
  • Chapter 2: Testing Convergence Using HAR Inference
  • 1. Introduction
  • 2. Preliminaries on Robust Inference Concerning Trend
  • 3. Testing Convergence
  • 4. Robust Testing
  • 4.1. Null and Alternative Hypotheses
  • 4.2. Test Statistics and Alternative Nonparametric Studentization
  • 4.3. Limit Theory under the Null
  • 4.4. Limit Theory under the Alternative of Convergence
  • 5. Monte Carlo Simulations and an Empirical Example
  • 5.1. Monte Carlo Simulations
  • 5.2. Empirical Example: State Unemployment Rates
  • 6. Concluding Remarks
  • References
  • Appendix
  • Assumptions
  • Proof of Theorem 2
  • Chapter 3: Model Selection for Explosive Models
  • 1. Introduction
  • 2. Models, Information Criteria, and a Literature Review
  • 3. Limit Properties Based on the OLS Estimator
  • 4. Limit Properties Based on the Indirect Inference Estimator
  • 5. Monte Carlo Study
  • 6. Conclusion
  • References
  • Appendix
  • A. Proof of Theorem 3.1
  • B. Proof of Theorem 3.4
  • C. Proof of Theorem 3.6
  • D. Proof of Theorem 3.8
  • E. Proof of Proposition 3.10
  • F. Proof of Theorem 4.1
  • G. Proof of Theorem 4.4
  • H. Proof of Theorem 4.6
  • I. Proof of Theorem 4.8
  • Chapter 4: A VAR Approach to Forecasting Multivariate Long Memory Processes Subject to Structural Breaks
  • 1. Introduction
  • 2. Model and Theoretical Insights
  • 2.1. Basic Model
  • 2.2. The Criteria for Selecting k
  • 3. Forecasting Methods
  • 3.1. Post-break approach
  • 3.2. VNVNO method
  • 3.3. VNV method
  • 3.4. Optimal weighting averaging approach
  • 3.5. VAR approximation approach
  • 4. Comparison of Forecasting Methods: Simulation Results
  • 4.1. Simulation Design I
  • 4.2. Simulation Design II
  • 4.3. Comparison When the Break Occurs at the End of the Sample
  • 5. Forecasting Multivariate Realized Volatility
  • 6. Concluding Remarks
  • Notes
  • References
  • Appendix
  • Appendix 1. The simulation and empirical support for Lemma 1
  • Apppendix 2. Proof of Lemma 1
  • Appendix 3. Proof of Lemma 2
  • Appendix 3. Proof of Lemma 2
  • Chapter 5: Identifying Global and National Output and Fiscal Policy Shocks Using
  • 1. Introduction
  • 2. Literature on Debt and Growth
  • 3. Gvar Representation of Factor-Augmented Panel Var Models
  • 4. Long-Run Perspective on Public Debt and Output
  • 5. Global Output and Fiscal Policy Shocks and Their Effects
  • 5.1. Evidence on CS Dependence
  • 5.2. Estimated Global Shocks
  • 5.3. Country-specific Effects of the Global Shocks

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