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Full Description
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.
Contents
Part I: Foundations: Theory, simulation methods and programming. 1. Basic formal concepts. 2. Conceptual differences between the Bayesian and Frequentist approaches. 3. Cornerstone models: Conjugate families. 4. Simulation methods. Part II: Regression models: A GUIded toolkit. 5. Graphical user interface. 6. Univariate models. 7. Multivariate models. 8. Time series models. 9. Longitudinal/Panel data models. 10. Bayesian model averaging. Part III: Advanced methods: A brief introduction. 11. Semi-parametric and non-parametric models. 12. Bayesian machine learning. 13. Causal inference. 14. Approximate Bayesian methods.
