This course provides a self-contained introduction to Bayesian analysis of panel data models. We will start with an introduction to Bayesian inference, covering the basic building blocks, which involve inference in a heteroskedastic linear regression model and Gibbs sampling to generate draws from the posterior distribution. Moreover, we will consider nonparametric inference. We then proceed with ...