Introduced to econometrics by Nobel laureate Chris Sims and his students, Bayesian methods have recently become the workhorse models for forecasting macroeconomic variables and are routinely used by central banks to inform policy decisions.

The two key characteristics of these methods are the possibility of

• handling very large cross-section of data –thereby including a large information set to base forecasts on-
• specifying a-priori beliefs on the behavior of macroeconomic time series.

More recent developments extended these models to account for time variation in the coefficients and volatilities, which dramatically improve the accuracy of density forecasts and now-casts.

This hands-on course aims at introducing state-of-the-art methods for structural analysis and forecasting with Bayesian Vector Autoregressions (BVARs).  

Understand and apply the most recent techniques in Bayesian Macroeconomic Analysis

The course includes theory sessions (10 hours) and practical sessions (10 hours). In the practical sessions, you will be provided code and work with MATLAB to implement the techniques and methodologies in each of the topics covered in the theory lectures.

Every participant will receive a time-limited personal free license of MATLAB several days before the start of the course. You will need to install the MATLAB software on your own computer for use during the practical sessions. Additional class materials will be provided before and during the sessions. The instructors will also be available throughout the course to discuss your individual research ideas and projects.

You will participate in an active and fruitful environment with international colleagues without incurring high costs, thanks to the online live delivery of this course.

Sessions will be recorded and videos will be available for a month once the course has finished.

Learn how to estimate and use a variety of BVAR models featuring drifting coefficients and volatilities