Bayesian methods are now central to empirical macroeconomics, with routine use in academic research and at central banks for forecasting and policy analysis. As the models applied in practice have become increasingly high-dimensional and computationally intensive, a working understanding of the estimation algorithms—not only the models themselves—has become essential. While many existing texts treat Bayesian econometrics broadly, Bayesian Macroeconometrics focuses specifically on the models and methods used in modern Bayesian macroeconomic analysis, for readers who want both to understand these methods and to implement them.
Bringing together modeling, posterior derivations, algorithmic design, and computation, the book derives each method step by step and integrates MATLAB code directly into the text. It pays particular attention to how posterior samplers are constructed in high-dimensional settings, where implementation choices can determine whether a method is practical for empirical work.
A single idea ties the material together: linear regression as a recurring building block, with state space models and vector autoregressions treated as collections of linear regressions linked by latent states or dynamic dependence. From this foundation, the book moves to the models at the center of modern practice, including stochastic volatility, dynamic factor models, vector autoregressions, time-varying parameter VARs, and large VARs, alongside global-local shrinkage priors and Bayesian nonparametrics.
Each chapter is anchored by worked examples using real macroeconomic data, closes with exercises, and is supported by a companion website providing datasets and code in MATLAB, Python, and R. The book is suitable as a primary text for graduate courses in Bayesian macroeconometrics and Bayesian time-series analysis, and as a reference for researchers and central-bank practitioners.
Key Features:

• Comprehensive coverage of Bayesian macroeconometric models — from basic Bayesian regressions and mixture models to linear Gaussian state space models, stochastic volatility models, and vector autoregressions.
• Emphasis on practical implementation — each model is illustrated with empirical applications using macroeconomic data and supported by reproducible code in MATLAB, Python, and R.
• Discussion of modern computation techniques — covering a range of advanced Markov chain Monte Carlo methods, including Hamiltonian Monte Carlo.
• Accessible style — technical results are derived in a step-by-step manner and are complemented by intuitive explanations and worked examples.
• Coverage of cutting-edge models and methods — including Dirichlet process mixtures, Gaussian process regressions, large VARs, and global-local priors.