Bayesian Macroeconometrics and Time Series
My teaching and short courses focus on scalable Bayesian methods for macroeconomic forecasting, large Bayesian VARs, stochastic volatility, and high-dimensional state space models.
Start here
If you are new to the area, a good starting point is the sample chapters of my forthcoming book Bayesian Macroeconometrics: Methods and Applications or my Bayesian Macroeconometrics notes. For large Bayesian VARs (with examples and code), see Large Bayesian VARs.
Research overviews: Large Bayesian VARs | High-dimensional state space models | Trend inflation models.
Recent Short Courses
- A 2-day short course at the Deutsche Bundesbank
- A 5-day course at Halle Institute for Economic Research
- A 2-day short course at the European Central Bank
- A 5-day workshop at the SIDE Summer School
Core Textbooks and References
My forthcoming book Bayesian Macroeconometrics: Methods and Applications (Chapman & Hall/CRC) develops the methods on this page from the ground up. A sample containing the preface and the first four chapters is available here, and the MATLAB code for all 14 chapters is available on GitHub.
For beginners who want to learn Bayesian econometrics and computation, I recommend Gary Koop's Bayesian Econometrics.
Our book Bayesian Econometric Methods (Second Edition) contains theoretical and programming exercises with detailed solutions.
My other textbook Statistical Modeling and Computation (Second Edition) includes chapters on Bayesian inference and Markov chain Monte Carlo methods.
Suggested Learning Path
1. Linear Gaussian State Space Models
A computational foundation is Chan and Jeliazkov (2009), which develops a precision-based simulation and integrated likelihood algorithm.
2. Stochastic Volatility
For univariate stochastic volatility models, see Chan and Hsiao (2014). For more complex SV models, see Chan (2013).
3. Large Bayesian VARs (shrinkage, stochastic volatility, and order invariance)
Large VARs require appropriate shrinkage priors, scalable stochastic volatility, and robustness to variable ordering. A practical entry point (with code) is Large Bayesian VARs.
- Shrinkage priors for large BVAR forecasting: Chan (2021).
- Stochastic volatility specification choice in large BVARs: Chan (2023).
- Order-invariant Bayesian VARs with stochastic volatility: Chan, Koop and Yu (2024).
4. Nonlinear State Space Models
For nonlinear state space models and accept-reject Metropolis-Hastings algorithms, see Chan (2017). Applications to inflation modeling can be found in Chan, Koop and Potter (2013) and Chan, Koop and Potter (2016).