This course builds on Advanced Econometrics 1 and seeks to familiarize students with a set of Bayesian methods recently developed for estimating and evaluating linear and non-linear macroeconomic models. In particular, the course will focus on Bayesian filtering methods such as Kalman filtering, unscented Kalman filtering, and particle filtering, which allow us to estimate non-linear models with unobserved variables and time-varying parameters. These methods will be applied to post-Keynesian Stock-Flow Consistent models, Dynamic Stochastic General Equilibrium models, and Dynamic Stochastic Disequilibrium models. Each topic will be covered in theory sessions as well as in application sessions. The course will be held in seminar style. Active student participation will be encouraged by appropriate assignments on the required readings. Students are expected to apply the methods discussed in small-scale projects and finish a research paper until the end of the term. Students will be encouraged to use this paper for their dissertation.