Introduction to Bayesian Econometrics: A GUIded tour using R
Introduction
Preface
To instructors and students
A brief presentation of R software
Acknowledgments
1
Basic formal concepts
1.1
The Bayes’ rule
1.2
Bayesian framework: A brief summary of theory
1.3
Bayesian reports: Decision theory under uncertainty
1.4
Summary: Chapter 1
1.5
Exercises: Chapter 1
2
Conceptual differences: Bayesian and Frequentist approaches
2.1
The concept of probability
2.2
Subjectivity is not the key
2.3
Estimation, hypothesis testing and prediction
2.4
The likelihood principle
2.5
Why is not the Bayesian approach that popular?
2.6
A simple working example
2.7
Summary: Chapter 2
2.8
Exercises: Chapter 2
3
Objective and subjective Bayesian approaches
3.1
Objective Bayesian priors
3.1.1
Empirical Bayes
3.2
Subjective Bayesian priors
3.2.1
Human heuristics
3.2.2
Elicitation
4
Cornerstone models: Conjugate families
4.1
Motivation of conjugate families
4.2
Conjugate prior to exponential family
4.3
Linear regression: The conjugate normal-normal/inverse gamma model
4.4
Multivariate linear regression: The conjugate normal-normal/inverse Wishart model
4.5
Computational examples
4.6
Summary: Chapter 4
4.7
Exercises: Chapter 4
5
Simulation methods
6
Univariate regression
6.1
Normal model
6.2
Logit model
6.3
Probit model
6.4
Summary: Chapter 6
6.5
Exercises: Chapter 6
7
Multivariate regression
8
Time series
9
Longitudinal regression
10
Convergence diagnostics
11
Bayesian model averaging in variable selection
12
Nonparametric regression
13
Recent developments
Appendix
Published with bookdown
Introduction to Bayesian Econometrics
Chapter 12
Nonparametric regression
We present Dirichlet process and Gaussian process.