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  • 1
    Online-Ressource
    Online-Ressource
    Bingley, U.K. :Emerald,
    UID:
    almahu_9949068930702882
    Umfang: 1 online resource (xi, 348 p.) : , ill.
    ISBN: 9781784411848 (electronic bk.) :
    Serie: Advances in econometrics, v. 34
    Inhalt: This volume of Advances in econometrics is devoted to Bayesian model comparison. It reflects the recent progress in model building and evaluation that has been achieved in the Bayesian paradigm and provides new state-of-the-art techniques, methodology, and findings that should stimulate future research. The volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests. Methodological issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian thresholding methods in hierarchical graphical models, adaptive reversible jump MCMC, LASSO estimators, parameter expansion algorithms, the implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection methodology, and a careful look at the modeling of endogeneity in discrete data settings. Important contemporary questions are examined in applications in macroeconomics, finance, banking, labor economics, industrial organization, and transportation, among others, in which model uncertainty is a central consideration.
    Anmerkung: Adaptive sequential posterior simulators for massively parallel computing environments / Garland Durham, John Geweke -- Model switching and model averaging in time-varying parameter regression models / Miguel Belmonte, Gary Koop -- Assessing Bayesian model comparison in small samples / Enrique Martínez-García, Mark A. Wynne -- Bayesian selection of systemic risk networks / Daniel Felix Ahelegbey, Paolo Giudici -- Parallel constrained Hamiltonian Monte Carlo for BEKK model comparison / Martin Burda -- Factor selection in dynamic hedge fund replication models : a Bayesian approach / Guillaume Weisang -- Determining the proper specification for endogenous covariates in discrete data settings / Angela Vossmeyer -- Variable selection in Bayesian models : using parameter estimation and non parameter estimation methods / Gail Blattenberger, Richard Fowles, Peter D. Loeb -- Intrinsic priors for objective Bayesian model selection / Elías Moreno, Luís Raúl Pericchi -- Demand estimation with high-dimensional product characteristics / Benjamin J. Gillen, Matthew Shum, Hyungsik Roger Moon -- Copula analysis of correlated counts / Esther Hee Lee.
    Weitere Ausg.: ISBN 9781784411855
    Sprache: Englisch
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (lizenzpflichtig)
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    Online-Ressource
    Online-Ressource
    Bingley, U.K. : Emerald
    UID:
    b3kat_BV048846135
    Umfang: 1 Online-Ressource (xi, 348 Seiten) , ill
    ISBN: 9781784411848
    Serie: Advances in econometrics v. 34
    Inhalt: This volume of Advances in econometrics is devoted to Bayesian model comparison. It reflects the recent progress in model building and evaluation that has been achieved in the Bayesian paradigm and provides new state-of-the-art techniques, methodology, and findings that should stimulate future research. The volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests. Methodological issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian thresholding methods in hierarchical graphical models, adaptive reversible jump MCMC, LASSO estimators, parameter expansion algorithms, the implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection methodology, and a careful look at the modeling of endogeneity in discrete data settings. Important contemporary questions are examined in applications in macroeconomics, finance, banking, labor economics, industrial organization, and transportation, among others, in which model uncertainty is a central consideration
    Anmerkung: Adaptive sequential posterior simulators for massively parallel computing environments / Garland Durham, John Geweke -- Model switching and model averaging in time-varying parameter regression models / Miguel Belmonte, Gary Koop -- Assessing Bayesian model comparison in small samples / Enrique Martínez-García, Mark A. Wynne -- Bayesian selection of systemic risk networks / Daniel Felix Ahelegbey, Paolo Giudici -- Parallel constrained Hamiltonian Monte Carlo for BEKK model comparison / Martin Burda -- Factor selection in dynamic hedge fund replication models : a Bayesian approach / Guillaume Weisang -- Determining the proper specification for endogenous covariates in discrete data settings / Angela Vossmeyer -- Variable selection in Bayesian models : using parameter estimation and non parameter estimation methods / Gail Blattenberger, Richard Fowles, Peter D. Loeb -- Intrinsic priors for objective Bayesian model selection / Elías Moreno, Luís Raúl Pericchi -- Demand estimation with high-dimensional product characteristics / Benjamin J. Gillen, Matthew Shum, Hyungsik Roger Moon -- Copula analysis of correlated counts / Esther Hee Lee
    Sprache: Englisch
    URL: Volltext  (URL des Erstveröffentlichers)
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 3
    Online-Ressource
    Online-Ressource
    Bradford : Emerald Group Publishing Limited
    UID:
    gbv_813268907
    Umfang: Online-Ressource (361 p)
    Ausgabe: Online-Ausg.
    ISBN: 9781784411855
    Serie: Advances in Econometrics v.34
    Inhalt: This volume of Advances in Econometrics 34 focusses on Bayesian model comparison. It reflects the recent progress in model building and evaluation that has been achieved in the Bayesian paradigm and provides new state-of-the-art techniques, methodology, and findings that should stimulate future research
    Anmerkung: 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 , Italy
    Weitere Ausg.: ISBN 9781784411848
    Weitere Ausg.: Erscheint auch als Druck-Ausgabe Bayesian model comparison Bingley [u.a.] : Emerald, 2014 ISBN 9781784411855
    Sprache: Englisch
    Schlagwort(e): Electronic books
    URL: Volltext  (lizenzpflichtig)
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 4
    Online-Ressource
    Online-Ressource
    Bradford, [England] :Emerald Group Publishing Limited,
    UID:
    almafu_9959246006202883
    Umfang: 1 online resource (361 p.)
    Ausgabe: First edition.
    ISBN: 1-78441-184-1
    Serie: Advances in econometrics, v. 34
    Inhalt: This volume of Advances in econometrics is devoted to Bayesian model comparison. It reflects the recent progress in model building and evaluation that has been achieved in the Bayesian paradigm and provides new state-of-the-art techniques, methodology, and findings that should stimulate future research. The volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests. Methodological issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian thresholding methods in hierarchical graphical models, adaptive reversible jump MCMC, LASSO estimators, parameter expansion algorithms, the implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection methodology, and a careful look at the modeling of endogeneity in discrete data settings. Important contemporary questions are examined in applications in macroeconomics, finance, banking, labor economics, industrial organization, and transportation, among others, in which model uncertainty is a central consideration.
    Anmerkung: Description based upon print version of record. , Adaptive sequential posterior simulators for massively parallel computing environments / Garland Durham, John Geweke -- Model switching and model averaging in time-varying parameter regression models / Miguel Belmonte, Gary Koop -- Assessing Bayesian model comparison in small samples / Enrique Martínez-García, Mark A. Wynne -- Bayesian selection of systemic risk networks / Daniel Felix Ahelegbey, Paolo Giudici -- Parallel constrained Hamiltonian Monte Carlo for BEKK model comparison / Martin Burda -- Factor selection in dynamic hedge fund replication models : a Bayesian approach / Guillaume Weisang -- Determining the proper specification for endogenous covariates in discrete data settings / Angela Vossmeyer -- Variable selection in Bayesian models : using parameter estimation and non parameter estimation methods / Gail Blattenberger, Richard Fowles, Peter D. Loeb -- Intrinsic priors for objective Bayesian model selection / Elías Moreno, Luís Raúl Pericchi -- Demand estimation with high-dimensional product characteristics / Benjamin J. Gillen, Matthew Shum, Hyungsik Roger Moon -- Copula analysis of correlated counts / Esther Hee Lee. , English
    Weitere Ausg.: ISBN 1-78441-185-X
    Weitere Ausg.: ISBN 1-322-44826-4
    Sprache: Englisch
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
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