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Introduction to Bayesian Econometrics: A GUIded tour using R
Introduction
1
About Me
1.1
BEsmarter
1.2
Contact
1.3
License
Preface
To instructors and students
A brief presentation of R software
Acknowledgments
2
Basic formal concepts
2.1
The Bayes’ rule
2.2
Bayesian framework: A brief summary of theory
2.3
Bayesian reports: Decision theory under uncertainty
2.4
Summary: Chapter 1
2.5
Exercises: Chapter 1
3
Conceptual differences: Bayesian and Frequentist approaches
3.1
The concept of probability
3.2
Subjectivity is not the key
3.3
Estimation, hypothesis testing and prediction
3.4
The likelihood principle
3.5
Why is not the Bayesian approach that popular?
3.6
A simple working example
3.7
Summary: Chapter 2
3.8
Exercises: Chapter 2
4
Objective and subjective Bayesian approaches
4.1
Objective Bayesian priors
4.1.1
Empirical Bayes
4.2
Subjective Bayesian priors
4.2.1
Human heuristics
4.2.2
Elicitation
5
Cornerstone models: Conjugate families
5.1
Motivation of conjugate families
5.2
Conjugate prior to exponential family
5.3
Linear regression: The conjugate normal-normal/inverse gamma model
5.4
Multivariate linear regression: The conjugate normal-normal/inverse Wishart model
5.5
Computational examples
5.6
Summary: Chapter 4
5.7
Exercises: Chapter 4
6
Simulation methods
7
Univariate regression
7.1
Normal model
7.2
Logit model
7.3
Probit model
7.4
Summary: Chapter 6
7.5
Exercises: Chapter 6
8
Multivariate regression
9
Time series
10
Longitudinal regression
11
Convergence diagnostics
12
Bayesian model averaging in variable selection
13
Nonparametric regression
14
Recent developments
Appendix
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Introduction to Bayesian Data Modeling
7.4
Summary: Chapter 6