【下载】Introduction to Bayesian Econometrics~Edward Greenberg.Cambridge.2008

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Introduction to Bayesian Econometrics (Hardcover)
Edward Greenberg(Author)

Editorial Reviews

Review
"This book provides an excellent introduction to Bayesian econometrics and statistics with many references to the recent literature that will be very helpful for students and others who have a good background in the calculus. Basic Bayesian estimation, testing, prediction and decision techniques are clearly explained with applications to a broad range of models and many computed examples are provided to illustrate general principles. Classical and modern computing techniques are clearly explained and applied to solve central inference problems. Also, references to downloadable computer algorithms are included in this impressive book." - Arnold Zellner, Graduate School of Business, University of Chicago

"This concise book provides an excellent introduction to modern, simulation-based Bayesian econometrics. It covers the theoretical underpinnings, the MCMC algorithm, and a large number of important econometric applications in an accessible yet rigorous manner. I highly recommend Greenberg's book as a Ph.D.-level textbook and as a source of reference for researchers entering the field." - Rainer Winkelmann, University of Zurich

"Professor Greenberg has assembled a tremendously valuable resource for anyone who wants to learn more about the Bayesian world. The book begins at an introductory level that should be accessible to a wide range of readers. Professor Greenberg then builds on these fundamental ideas to help the reader develop an in-depth understanding of the major concepts and methods used in modern Bayesian econometrics. The explanations are very clearly written, and the content is supported with many detailed examples and real-data applications." - Douglas J. Miller, University of Missouri - Columbia

"In Introduction to Bayesian Econometrics, Greenberg skillfully guides us through the fundamentals of Bayesian inference, provides a detailed review of methods for posterior simulation and carefully illustrates the use of such methods for fitting a wide array of popular micro-econometric and time series models. The writing style is accessible and lucid, the coverage is comprehensive, and the associated web site provides data and computer code to clearly illustrate how modern Bayesian methods are implemented in practice. This text is a must-have for the Bayesian and will appeal to statisticians/econometricians of all persuasions." - Justin L. Tobias, Iowa State University

Product Description
This concise textbook is an introduction to econometrics at the graduate or advanced undergraduate level. It differs from other books in econometrics in its use of the Bayesian approach to statistics. This approach, in contrast to the frequentist approach to statistics, makes explicit use of prior information and is based on the subjective view of probability, which takes probability theory as applying to all situations in which uncertainty exists, including uncertainty over the values of parameters.


Product Details

• Hardcover: 224 pages
• Publisher: Cambridge University Press; 1 edition (October 8, 2007)
• Language: English
• ISBN-10: 0521858712
• ISBN-13: 978-0521858717

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关键词：econometrics introduction Econometric troduction Greenberg econometrics Bayesian introduction Edward Greenberg

Contents

List of Figures page ix

List of Tables xi

Preface xiii

Part I Fundamentals of Bayesian Inference

1 Introduction 3

1.1 Econometrics 3

1.2 Plan of the Book 4

1.3 Historical Note and Further Reading 5

2 Basic Concepts of Probability and Inference 7

2.1 Probability 7

2.1.1 Frequentist Probabilities 8

2.1.2 Subjective Probabilities 9

2.2 Prior, Likelihood, and Posterior 12

2.3 Summary 18

2.4 Further Reading and References 19

2.5 Exercises 19

3 Posterior Distributions and Inference 20

3.1 Properties of Posterior Distributions 20

3.1.1 The Likelihood Function 20

3.1.2 Vectors of Parameters 22

3.1.3 Bayesian Updating 24

3.1.4 Large Samples 25

3.1.5 Identification 28

3.2 Inference 29

3.2.1 Point Estimates 29

3.2.2 Interval Estimates 31

3.2.3 Prediction 32

3.2.4 Model Comparison 33

3.3 Summary 38

3.4 Further Reading and References 38

3.5 Exercises 39

4 Prior Distributions 41

4.1 Normal Linear Regression Model 41

4.2 Proper and Improper Priors 43

4.3 Conjugate Priors 44

4.4 Subject-Matter Considerations 47

4.5 Exchangeability 50

4.6 Hierarchical Models 52

4.7 Training Sample Priors 53

4.8 Sensitivity and Robustness 54

4.9 Conditionally Conjugate Priors 54

4.10 A Look Ahead 56

4.11 Further Reading and References 57

4.12 Exercises 58

Part II Simulation

5 Classical Simulation 63

5.1 Probability Integral Transformation Method 63

5.2 Method of Composition 65

5.3 Accept–Reject Algorithm 66

5.4 Importance Sampling 70

5.5 Multivariate Simulation 72

5.6 Using Simulated Output 72

5.7 Further Reading and References 74

5.8 Exercises 75

6 Basics of Markov Chains 76

6.1 Finite State Spaces 76

6.2 Countable State Spaces 81

6.3 Continuous State Spaces 85

6.4 Further Reading and References 87

6.5 Exercises 87

7 Simulation by MCMC Methods 90

7.1 Gibbs Algorithm 91

7.1.1 Basic Algorithm 91

7.1.2 Calculation of Marginal Likelihood 95

7.2 Metropolis–Hastings Algorithm 96

7.2.1 Basic Algorithm 96

7.2.2 Calculation of Marginal Likelihood 101

7.3 Numerical Standard Errors and Convergence 102

7.4 Further Reading and References 103

7.5 Exercises 105

Part III Applications

8 Linear Regression and Extensions 111

8.1 Continuous Dependent Variables 111

8.1.1 Normally Distributed Errors 111

8.1.2 Student-t Distributed Errors 114

8.2 Limited Dependent Variables 117

8.2.1 Tobit Model for Censored Data 117

8.2.2 Binary Probit Model 122

8.2.3 Binary Logit Model 126

8.3 Further Reading and References 129

8.4 Exercises 132

9 Multivariate Responses 134

9.1 SUR Model 134

9.2 Multivariate Probit Model 139

9.3 Panel Data 144

9.4 Further Reading and References 149

9.5 Exercises 151

10 Time Series 153

10.1 Autoregressive Models 153

10.2 Regime-Switching Models 158

10.3 Time-Varying Parameters 161

10.4 Time Series Properties of Models for Panel Data 165

10.5 Further Reading and References 166

10.6 Exercises 167

11 Endogenous Covariates and Sample Selection 168

11.1 Treatment Models 168

11.2 Endogenous Covariates 173

11.3 Incidental Truncation 175

11.4 Further Reading and References 179

11.5 Exercises 180

A Probability Distributions and Matrix Theorems 182

A.1 Probability Distributions 182

A.1.1 Bernoulli 182

A.1.2 Binomial 182

A.1.3 Negative Binomial 183

A.1.4 Multinomial 183

A.1.5 Poisson 183

A.1.6 Uniform 183

A.1.7 Gamma 184

A.1.8 Inverted or Inverse Gamma 184

A.1.9 Beta 185

A.1.10 Dirichlet 185

A.1.11 Normal or Gaussian 186

A.1.12 Multivariate and Matricvariate Normal or Gaussian 186

A.1.13 Truncated Normal 188

A.1.14 Univariate Student-t 188

A.1.15 Multivariate t 188

A.1.16 Wishart 190

A.1.17 Inverted or Inverse Wishart 190

A.1.18 Multiplication Rule of Probability 190

A.2 Matrix Theorems 191

B Computer Programs for MCMC Calculations 192

Bibliography 194

Author Index 200

Subject Index 202

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