Bayesian Econometrics

Bayesian Econometrics plays an important role in quantitative economics, marketing research and finance. This course discusses the basic tools which are needed to perform Bayesian analyses. It starts with a discussion on the difference between Bayesian and frequentist statistical approach. Next, Bayesian parameter estimation, forecasting and Bayesian testing is considered, where we deal with univariate models, multivariate models and panel data models (Hierarchical Bayes techniques). To perform a Bayesian analysis, knowledge of advanced simulation methods is necessary. Part of the course is devoted to Monte Carlo integration using Importance Sampling and MCMC methods like Gibbs sampling, Metropolis-Hasting sampling, Slice sampling and Hamiltonian MC sampling, The course ends with variational Bayesian inference which allows for fast approximate posterior inference. The topics are illustrated using simple computer examples in Python which are demonstrated during the lectures.