Bayesian Model Comparison

Online-Ressource (361 p)

Advances in econometrics : a research annual. - Bingley : Emerald, ZDB-ID 2401775-9. - Vol. v.34

Description based upon print version of record

Front Cover; Bayesian Model Comparison; Copyright page; Contents; List of Contributors; Preface; Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments; 1. Introduction; 2. Posterior Simulation in a Massively Parallel Computing Environment; 2.1. Computing Environment; 2.2. Models and Conditions; 2.3. Assessing Numerical Accuracy and Relative Numerical Efficiency; 3. Parallel Sequential Posterior Simulators; 3.1. Nonadaptive Simulators; 3.2. Adaptive Simulators; 3.3. A Specific Adaptive Simulator; 3.4. Software; 4. Predictive and Marginal Likelihood

4.1. Predictive Likelihood4.2. Marginal Likelihood; 5. Application: Exponential Generalized Autoregressive Conditional Heteroskedasticity Model; 5.1. Model and Data; 5.2. Performance; 5.3. Posterior Moments; 5.4. Robustness to Irregular Posterior Distributions; 5.5. Comparison with Markov chain Monte Carlo; 6. Conclusion; Acknowledgment; References; Model Switching and Model Averaging in Time-Varying Parameter Regression Models; 1. Introduction; 2. DMA and DMS Using Switching Linear Gaussian State Space Models; 3. Application: Selecting the Best Inflation Forecasts; 3.1. Introduction

3.2. Data3.3. Which Inflation Forecasts Are Best?; 3.3.1. Comparison to DMA and DMS Using Forgetting Factors; 3.4. Forecasting Comparison of Different Implementations of DMA/DMS; 3.5. Forecasting Comparison of Different Implementations of DMA/DMS in a Larger Model Space; 4. Conclusions; Notes; Acknowledgments; References; Appendix A: Bayesian Inference in the Switching Linear Gaussian State Space Model; Appendix B: Dynamic Model Averaging Using Forgetting Factors; Assessing Bayesian Model Comparison in Small Samples; 1. Introduction; 2. Economic Model; 3. Findings

3.1. Sample Size of Observables for Estimation3.1.1. Experiment with the Policy Parameter ψπ; 3.1.2. Experiment with the Structural Parameter ξ; 3.2. Selection of Observables for Estimation; 4. Discussion; 4.1. Interpreting Our Findings: The Role of Sample Size; 4.1.1. Laplace's Approximation Method: Accuracy and Sample Size; 4.1.2. Laplace's Approximation Method: Overfitting Penalization and Sample Size; 4.1.3. BIC's Approximation Method: An Alternative Trade-off Between Accuracy at a Given Sample Size and the Role of Priors ...

4.2. Other Considerations in Evaluating Bayesian Posterior Odds for Model Comparison4.2.1. Parameter Identification; 4.2.2. Variable Selection; 4.2.3. Prior Selection; 4.2.4. Nested versus Non-nested Models in Bayesian Model Comparison; 5. Concluding Remarks; Notes; Acknowledgments; References; Bayesian Selection of Systemic Risk Networks; 1. Motivation; 2. Methodology; 2.1. Bayesian Graphical Models; 2.2. Hierarchical Graphical Models; 2.3. Efficient Structural Inference Scheme; 2.4. Centrality Measures; 3. Simulation; 4. Application; 4.1. Data; 4.2. Convergence Diagnostics; 4.3. Results

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