Introduction to Bayesian Econometrics: A GUIded tour using R
Introduction
Preface
To instructors and students
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
1.5
Exercises
2
Conceptual differences between the 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
2.8
Exercises
3
Cornerstone models: Conjugate families
3.1
Motivation of conjugate families
3.2
Conjugate prior to exponential family
3.3
Linear regression: The conjugate normal-normal/inverse gamma model
3.4
Multivariate linear regression: The conjugate normal-normal/inverse Wishart model
3.5
Computational examples
3.6
Summary: Chapter 4
3.7
Exercises: Chapter 4
4
Simulation methods
4.1
Markov Chain Monte Carlo methods
4.1.1
Gibbs sampler
4.1.2
Metropolis-Hastings
4.1.3
Hamiltonian Monte Carlo
4.2
Importance sampling
4.3
Particle filtering
4.4
Convergence diagnostics
4.4.1
Numerical standard error
4.4.2
Effective number of simulation draws
4.4.3
Tests of convergence
4.4.4
Checking for errors in the posterior simulator
4.5
Summary
4.6
Exercises
5
Graphical user interface
6
Univariate regression
6.1
The Gaussian linear model
6.2
The logit model
6.3
The probit model
6.4
The multinomial probit model
6.5
The multinomial logit model
6.6
Ordered probit model
6.7
Negative binomial model
6.8
Tobit model
6.9
Quantile regression
6.10
Bayesian bootstrap regression
6.11
Summary
6.12
Exercises
7
Multivariate regression
7.1
Multivariate regression
7.2
Seemingly Unrelated Regression
7.3
Instrumental variable
7.4
Multivariate probit model
7.5
Summary
7.6
Exercises
8
Time series
References
Published with bookdown
Introduction to Bayesian Data Modeling
3.6
Summary: Chapter 4