Syllabus

Overview

This course covers methods designed to deal with univariate and multivariate prediction with “big data” in macroeconomics, and to conduct semi-structural analysis. 

The three main subjects of the course are: (i) predictive regressions with big data; (ii) Bayesian Vector Autoregressions (BVARs), as a popular example of big data multivariate models, which also represent a bridge between reduced-form and structural models; (iii) Structural VARs, which have become the most popular tool for structural shock identification. 

The course consists of lectures where we will develop the theoretical models and discuss the approach of how to solve them. In addition to lectures, the course also includes tutorial sessions. These are intended to allow students to program their own code solving the models discussed during the lectures.

The  School will be organized over 5 days. Overall there will be 15 hours lectures (3 hours each day) and 6 hours tutorials.  The teachers are  Domenico Giannone (IMF, University of Washington, and CEPR) and Giorgio Primiceri (Northwestern University, CEPR and NBER)

The Syllabus is available here.

The Timetable is available here.