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Biblioteca

MARC Lista (tabellare)
Complete and incomplete econometric models [[electronic resource] /] / John Geweke
Complete and incomplete econometric models [[electronic resource] /] / John Geweke
Autore Geweke John
Edizione [Course Book]
Pubbl/distr/stampa Princeton, : Princeton University Press, c2010
Descrizione fisica 1 online resource (176 p.)
Disciplina 330.01/5195
Collana The Econometric and Tinbergen Institutes lecture series
Soggetto topico Econometric models
Econometrics
Soggetto genere / forma Electronic books.
ISBN 1-282-47315-8
1-282-93628-X
9786612473159
9786612936289
1-4008-3524-0
0-691-14002-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Frontmatter -- Contents -- Series Editors' Introduction -- Preface -- 1 Introduction -- 2. The Bayesian Paradigm -- 3. Prior Predictive Analysis And Model Evaluation -- 4. Incomplete Structural Models -- 5. An Incomplete Model Space -- References
Record Nr. UNINA-9910456817803321
Geweke John  
Princeton, : Princeton University Press, c2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Complete and incomplete econometric models [[electronic resource] /] / John Geweke
Complete and incomplete econometric models [[electronic resource] /] / John Geweke
Autore Geweke John
Edizione [Course Book]
Pubbl/distr/stampa Princeton, : Princeton University Press, c2010
Descrizione fisica 1 online resource (176 p.)
Disciplina 330.01/5195
Collana The Econometric and Tinbergen Institutes lecture series
Soggetto topico Econometric models
Econometrics
ISBN 1-282-47315-8
1-282-93628-X
9786612473159
9786612936289
1-4008-3524-0
0-691-14002-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Frontmatter -- Contents -- Series Editors' Introduction -- Preface -- 1 Introduction -- 2. The Bayesian Paradigm -- 3. Prior Predictive Analysis And Model Evaluation -- 4. Incomplete Structural Models -- 5. An Incomplete Model Space -- References
Record Nr. UNINA-9910781070103321
Geweke John  
Princeton, : Princeton University Press, c2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Complete and incomplete econometric models [[electronic resource] /] / John Geweke
Complete and incomplete econometric models [[electronic resource] /] / John Geweke
Autore Geweke John
Edizione [Course Book]
Pubbl/distr/stampa Princeton, : Princeton University Press, c2010
Descrizione fisica 1 online resource (176 p.)
Disciplina 330.01/5195
Collana The Econometric and Tinbergen Institutes lecture series
Soggetto topico Econometric models
Econometrics
ISBN 1-282-47315-8
1-282-93628-X
9786612473159
9786612936289
1-4008-3524-0
0-691-14002-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Frontmatter -- Contents -- Series Editors' Introduction -- Preface -- 1 Introduction -- 2. The Bayesian Paradigm -- 3. Prior Predictive Analysis And Model Evaluation -- 4. Incomplete Structural Models -- 5. An Incomplete Model Space -- References
Record Nr. UNINA-9910807716203321
Geweke John  
Princeton, : Princeton University Press, c2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Contemporary Bayesian econometrics and statistics [[electronic resource] /] / John Geweke
Contemporary Bayesian econometrics and statistics [[electronic resource] /] / John Geweke
Autore Geweke John
Pubbl/distr/stampa Hoboken, N.J., : John Wiley, c2005
Descrizione fisica 1 online resource (322 p.)
Disciplina 330.015195
330.01519542
330/.01/519542
Collana Wiley Series in Probability and Statistics
Soggetto topico Econometrics
Bayesian statistical decision theory
Decision making - Mathematical models
Soggetto genere / forma Electronic books.
ISBN 1-280-27761-0
9786610277612
0-470-23694-9
0-471-74473-5
0-471-74472-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contemporary Bayesian Econometrics and Statistics; Contents; Preface; 1. Introduction; 1.1 Two Examples; 1.1.1 Public School Class Sizes; 1.1.2 Value at Risk; 1.2 Observables, Unobservables, and Objects of Interest; 1.3 Conditioning and Updating; 1.4 Simulators; 1.5 Modeling; 1.6 Decisionmaking; 2. Elements of Bayesian Inference; 2.1 Basics; 2.2 Sufficiency, Ancillarity, and Nuisance Parameters; 2.2.1 Sufficiency; 2.2.2 Ancillarity; 2.2.3 Nuisance Parameters; 2.3 Conjugate Prior Distributions; 2.4 Bayesian Decision Theory and Point Estimation; 2.5 Credible Sets; 2.6 Model Comparison
2.6.1 Marginal Likelihoods2.6.2 Predictive Densities; 3. Topics in Bayesian Inference; 3.1 Hierarchical Priors and Latent Variables; 3.2 Improper Prior Distributions; 3.3 Prior Robustness and the Density Ratio Class; 3.4 Asymptotic Analysis; 3.5 The Likelihood Principle; 4. Posterior Simulation; 4.1 Direct Sampling; 4.2 Acceptance and Importance Sampling; 4.2.1 Acceptance Sampling; 4.2.2 Importance Sampling; 4.3 Markov Chain Monte Carlo; 4.3.1 The Gibbs Sampler; 4.3.2 The Metropolis-Hastings Algorithm; 4.4 Variance Reduction; 4.4.1 Concentrated Expectations; 4.4.2 Antithetic Sampling
4.5 Some Continuous State Space Markov Chain Theory4.5.1 Convergence of the Gibbs Sampler; 4.5.2 Convergence of the Metropolis-Hastings Algorithm; 4.6 Hybrid Markov Chain Monte Carlo Methods; 4.6.1 Transition Mixtures; 4.6.2 Metropolis within Gibbs; 4.7 Numerical Accuracy and Convergence in Markov Chain Monte Carlo; 5. Linear Models; 5.1 BACC and the Normal Linear Regression Model; 5.2 Seemingly Unrelated Regressions Models; 5.3 Linear Constraints in the Linear Model; 5.3.1 Linear Inequality Constraints
5.3.2 Conjectured Linear Restrictions, Linear Inequality Constraints, and Covariate Selection5.4 Nonlinear Regression; 5.4.1 Nonlinear Regression with Smoothness Priors; 5.4.2 Nonlinear Regression with Basis Functions; 6. Modeling with Latent Variables; 6.1 Censored Normal Linear Models; 6.2 Probit Linear Models; 6.3 The Independent Finite State Model; 6.4 Modeling with Mixtures of Normal Distributions; 6.4.1 The Independent Student-t Linear Model; 6.4.2 Normal Mixture Linear Models; 6.4.3 Generalizing the Observable Outcomes; 7. Modeling for Time Series
7.1 Linear Models with Serial Correlation7.2 The First-Order Markov Finite State Model; 7.2.1 Inference in the Nonstationary Model; 7.2.2 Inference in the Stationary Model; 7.3 Markov Normal Mixture Linear Model; 8. Bayesian Investigation; 8.1 Implementing Simulation Methods; 8.1.1 Density Ratio Tests; 8.1.2 Joint Distribution Tests; 8.2 Formal Model Comparison; 8.2.1 Bayes Factors for Modeling with Common Likelihoods; 8.2.2 Marginal Likelihood Approximation Using Importance Sampling; 8.2.3 Marginal Likelihood Approximation Using Gibbs Sampling
8.2.4 Density Ratio Marginal Likelihood Approximation
Record Nr. UNINA-9910145035003321
Geweke John  
Hoboken, N.J., : John Wiley, c2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Contemporary Bayesian econometrics and statistics [[electronic resource] /] / John Geweke
Contemporary Bayesian econometrics and statistics [[electronic resource] /] / John Geweke
Autore Geweke John
Pubbl/distr/stampa Hoboken, N.J., : John Wiley, c2005
Descrizione fisica 1 online resource (322 p.)
Disciplina 330.015195
330.01519542
330/.01/519542
Collana Wiley Series in Probability and Statistics
Soggetto topico Econometrics
Bayesian statistical decision theory
Decision making - Mathematical models
ISBN 1-280-27761-0
9786610277612
0-470-23694-9
0-471-74473-5
0-471-74472-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contemporary Bayesian Econometrics and Statistics; Contents; Preface; 1. Introduction; 1.1 Two Examples; 1.1.1 Public School Class Sizes; 1.1.2 Value at Risk; 1.2 Observables, Unobservables, and Objects of Interest; 1.3 Conditioning and Updating; 1.4 Simulators; 1.5 Modeling; 1.6 Decisionmaking; 2. Elements of Bayesian Inference; 2.1 Basics; 2.2 Sufficiency, Ancillarity, and Nuisance Parameters; 2.2.1 Sufficiency; 2.2.2 Ancillarity; 2.2.3 Nuisance Parameters; 2.3 Conjugate Prior Distributions; 2.4 Bayesian Decision Theory and Point Estimation; 2.5 Credible Sets; 2.6 Model Comparison
2.6.1 Marginal Likelihoods2.6.2 Predictive Densities; 3. Topics in Bayesian Inference; 3.1 Hierarchical Priors and Latent Variables; 3.2 Improper Prior Distributions; 3.3 Prior Robustness and the Density Ratio Class; 3.4 Asymptotic Analysis; 3.5 The Likelihood Principle; 4. Posterior Simulation; 4.1 Direct Sampling; 4.2 Acceptance and Importance Sampling; 4.2.1 Acceptance Sampling; 4.2.2 Importance Sampling; 4.3 Markov Chain Monte Carlo; 4.3.1 The Gibbs Sampler; 4.3.2 The Metropolis-Hastings Algorithm; 4.4 Variance Reduction; 4.4.1 Concentrated Expectations; 4.4.2 Antithetic Sampling
4.5 Some Continuous State Space Markov Chain Theory4.5.1 Convergence of the Gibbs Sampler; 4.5.2 Convergence of the Metropolis-Hastings Algorithm; 4.6 Hybrid Markov Chain Monte Carlo Methods; 4.6.1 Transition Mixtures; 4.6.2 Metropolis within Gibbs; 4.7 Numerical Accuracy and Convergence in Markov Chain Monte Carlo; 5. Linear Models; 5.1 BACC and the Normal Linear Regression Model; 5.2 Seemingly Unrelated Regressions Models; 5.3 Linear Constraints in the Linear Model; 5.3.1 Linear Inequality Constraints
5.3.2 Conjectured Linear Restrictions, Linear Inequality Constraints, and Covariate Selection5.4 Nonlinear Regression; 5.4.1 Nonlinear Regression with Smoothness Priors; 5.4.2 Nonlinear Regression with Basis Functions; 6. Modeling with Latent Variables; 6.1 Censored Normal Linear Models; 6.2 Probit Linear Models; 6.3 The Independent Finite State Model; 6.4 Modeling with Mixtures of Normal Distributions; 6.4.1 The Independent Student-t Linear Model; 6.4.2 Normal Mixture Linear Models; 6.4.3 Generalizing the Observable Outcomes; 7. Modeling for Time Series
7.1 Linear Models with Serial Correlation7.2 The First-Order Markov Finite State Model; 7.2.1 Inference in the Nonstationary Model; 7.2.2 Inference in the Stationary Model; 7.3 Markov Normal Mixture Linear Model; 8. Bayesian Investigation; 8.1 Implementing Simulation Methods; 8.1.1 Density Ratio Tests; 8.1.2 Joint Distribution Tests; 8.2 Formal Model Comparison; 8.2.1 Bayes Factors for Modeling with Common Likelihoods; 8.2.2 Marginal Likelihood Approximation Using Importance Sampling; 8.2.3 Marginal Likelihood Approximation Using Gibbs Sampling
8.2.4 Density Ratio Marginal Likelihood Approximation
Record Nr. UNINA-9910831067803321
Geweke John  
Hoboken, N.J., : John Wiley, c2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Contemporary Bayesian econometrics and statistics [[electronic resource] /] / John Geweke
Contemporary Bayesian econometrics and statistics [[electronic resource] /] / John Geweke
Autore Geweke John
Pubbl/distr/stampa Hoboken, N.J., : John Wiley, c2005
Descrizione fisica 1 online resource (322 p.)
Disciplina 330.015195
330.01519542
330/.01/519542
Collana Wiley Series in Probability and Statistics
Soggetto topico Econometrics
Bayesian statistical decision theory
Decision making - Mathematical models
ISBN 1-280-27761-0
9786610277612
0-470-23694-9
0-471-74473-5
0-471-74472-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contemporary Bayesian Econometrics and Statistics; Contents; Preface; 1. Introduction; 1.1 Two Examples; 1.1.1 Public School Class Sizes; 1.1.2 Value at Risk; 1.2 Observables, Unobservables, and Objects of Interest; 1.3 Conditioning and Updating; 1.4 Simulators; 1.5 Modeling; 1.6 Decisionmaking; 2. Elements of Bayesian Inference; 2.1 Basics; 2.2 Sufficiency, Ancillarity, and Nuisance Parameters; 2.2.1 Sufficiency; 2.2.2 Ancillarity; 2.2.3 Nuisance Parameters; 2.3 Conjugate Prior Distributions; 2.4 Bayesian Decision Theory and Point Estimation; 2.5 Credible Sets; 2.6 Model Comparison
2.6.1 Marginal Likelihoods2.6.2 Predictive Densities; 3. Topics in Bayesian Inference; 3.1 Hierarchical Priors and Latent Variables; 3.2 Improper Prior Distributions; 3.3 Prior Robustness and the Density Ratio Class; 3.4 Asymptotic Analysis; 3.5 The Likelihood Principle; 4. Posterior Simulation; 4.1 Direct Sampling; 4.2 Acceptance and Importance Sampling; 4.2.1 Acceptance Sampling; 4.2.2 Importance Sampling; 4.3 Markov Chain Monte Carlo; 4.3.1 The Gibbs Sampler; 4.3.2 The Metropolis-Hastings Algorithm; 4.4 Variance Reduction; 4.4.1 Concentrated Expectations; 4.4.2 Antithetic Sampling
4.5 Some Continuous State Space Markov Chain Theory4.5.1 Convergence of the Gibbs Sampler; 4.5.2 Convergence of the Metropolis-Hastings Algorithm; 4.6 Hybrid Markov Chain Monte Carlo Methods; 4.6.1 Transition Mixtures; 4.6.2 Metropolis within Gibbs; 4.7 Numerical Accuracy and Convergence in Markov Chain Monte Carlo; 5. Linear Models; 5.1 BACC and the Normal Linear Regression Model; 5.2 Seemingly Unrelated Regressions Models; 5.3 Linear Constraints in the Linear Model; 5.3.1 Linear Inequality Constraints
5.3.2 Conjectured Linear Restrictions, Linear Inequality Constraints, and Covariate Selection5.4 Nonlinear Regression; 5.4.1 Nonlinear Regression with Smoothness Priors; 5.4.2 Nonlinear Regression with Basis Functions; 6. Modeling with Latent Variables; 6.1 Censored Normal Linear Models; 6.2 Probit Linear Models; 6.3 The Independent Finite State Model; 6.4 Modeling with Mixtures of Normal Distributions; 6.4.1 The Independent Student-t Linear Model; 6.4.2 Normal Mixture Linear Models; 6.4.3 Generalizing the Observable Outcomes; 7. Modeling for Time Series
7.1 Linear Models with Serial Correlation7.2 The First-Order Markov Finite State Model; 7.2.1 Inference in the Nonstationary Model; 7.2.2 Inference in the Stationary Model; 7.3 Markov Normal Mixture Linear Model; 8. Bayesian Investigation; 8.1 Implementing Simulation Methods; 8.1.1 Density Ratio Tests; 8.1.2 Joint Distribution Tests; 8.2 Formal Model Comparison; 8.2.1 Bayes Factors for Modeling with Common Likelihoods; 8.2.2 Marginal Likelihood Approximation Using Importance Sampling; 8.2.3 Marginal Likelihood Approximation Using Gibbs Sampling
8.2.4 Density Ratio Marginal Likelihood Approximation
Record Nr. UNINA-9910841339303321
Geweke John  
Hoboken, N.J., : John Wiley, c2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui