1st Edition
Introduction to Bayesian Econometrics A GUIded Toolkit using R
Introduction to Bayesian Econometrics: A GUIded Toolkit Using R offers a practical, conceptually clear, and computationally accessible pathway into Bayesian data analysis. Designed for readers who wish to apply Bayesian methods without necessarily investing years in programming, the book combines rigorous treatment of foundational ideas with a graphical user interface (GUI) that allows users to run Bayesian regression models in a user-friendly environment.
The first part develops the mathematical foundations of Bayesian inference by presenting all derivations step-by-step. This transparent treatment of conjugate models, including posterior analysis, marginal likelihoods, and posterior predictive distributions, provides readers with a strong theoretical base for the more advanced material that follows.
The second part focuses on implementation. It introduces the custom GUI for readers with little or no programming experience, demonstrates how to fit Bayesian models using established R packages, and guides more advanced users through programming key components of Bayesian samplers from scratch. This integrated approach enables readers with different backgrounds to engage with Bayesian methods at their preferred level of computational depth.
The third part extends the framework to modern Bayesian econometrics. It covers Bayesian machine learning, causal inference, and approximate methods, illustrating how Bayesian ideas can be applied to contemporary empirical challenges. By combining theory, software, and hands-on computation, the book provides a comprehensive entry point into both classical and modern Bayesian analysis.
Across all parts, the book is designed to support a wide range of users -beginners, intermediate programmers, and advanced learners-. To the best of the author’s knowledge, no existing text combines mathematical transparency, software accessibility, and modern Bayesian topics in a single, integrated resource.
Foreword
Preface
Introduction
Symbols
Part I: Foundations: Theory, simulation methods and programming
Chapter 1. Basic formal concepts
Chapter 2. Conceptual differences between the Bayesian and Frequentist approaches
Chapter 3. Cornerstone models: Conjugate families
Chapter 4. Simulation methods
Part II: Regression models: A GUIded toolkit
Chapter 5. Graphical user interface
Chapter 6. Univariate models
Chapter 7. Multivariate models
Chapter 8. Time series models
Chapter 9. Longitudinal/Panel data models
Chapter 10. Bayesian model averaging
Part III: Advanced methods: A brief introduction
Chapter 11. Semi-parametric and non-parametric models
Chapter 12. Bayesian machine learning
Chapter 13. Causal inference
Chapter 14. Approximate Bayesian methods
Bibliography
Appendix
Biography
Andrés Ramírez-Hassan is a Distinguished Professor at Universidad EAFIT whose work advances Bayesian econometrics and applied statistical modeling. His research has appeared in journals such as the Journal of Applied Econometrics, Econometric Reviews, and the International Journal of Forecasting. He has served as a researcher and consultant for global institutions, including the United Nations Development Programme and the Inter-American Development Bank.
