Bayesian econometrics (ECNM11060).

Course website.

Gary’s website.

Ping’s website.

Overview.

Bayesian methods are increasingly used in econometrics, particularly in the field of macroeconomics. This is a course in Bayesian econometrics with a focus on the models used in empirical macroeconomics. It begins with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and showing how Bayesian methods work in the familiar context of the regression model. Computational methods are of great importance in modern Bayesian econometrics and these are discussed in in detail. In light of the Big Data revolution, applied economists often face the situation where the number of variables under consideration is large relative to the number of observations and conventional econometric methods do not work well. We describe various methods that can be used with Big Data in the context of the regression model and emphasize the wider applicability of these methods in other modelling contexts. Subsequently, the course shows how Bayesian methods are used with models which are currently popular in macroeconomics such as Vector Autoregressions, state space models (including factor models and stochastic volatility models) and nonparametric methods such as regression trees.

List of lectures.

1. An overview of Bayesian econometrics
2. Bayesian inference in the normal linear regression model
3. Overview of recent advances in macroeconomic forecasting
4. Introduction to Bayesian machine learning methods
5. Introduction to Bayesian nonparametrics
6. Bayesian vector autoregressions (VARs)
7. Introduction to Bayesian state space models
8. TVP-VARs with stochastic volatility
9. Bayesian inference in factor models
10. Mixed frequency methods for macroeconomics