JEM158 - Tools for Modern Macroeconometrics Tools for Modern Macroeconometrics (not offered in 2017/2018)

Credit: 6
Status: CFS - elective
EEI and EP - elective
English
ET - elective
F,FM and B - elective
Masters - all
MEF - elective
NOT AVAILABLE
Semester - winter
Course supervisors: PhDr. Marek Rusnák
Course homepage: JEM158
Literature:
Description: The primary objective of this course is to provide the students with the basic tools used in the contemporary macroeconometrics. Specifically, Bayesian and state space techniques will be introduced. These techniques are the workhorse models in the state-of-art macroeconomic research and are heavily used in practice as well (e.g central banks, international insititutions). The course will provide introduction to basic methodological and theoretical concepts. The main focus, however, will be on practical examples in Matlab. After successful completion of the course, the students should be able to understand and use these techniques in their applied research. Moreover, they should be well prepared to apply and extend baseline macroeconometric models in their bachelor or master thesis. The knowledge of these models will allow the students to pursue research that can be publishable in quality international journals.

Organization:
Winter semester, every Friday in room 016, 1530 – 1650 lecture, 1700 – 1820 exercise session
Because of capacity of computer room (016), the maximum number of students for the course is limited to 30, please register in Student Information System

Schedule:
07/10/2016 - Lecture 1 - Course overview / Introduction to Bayesian Econometrics
14/10/2016 - Lecture 2 - Normal linear regression with natural conjugate prior
21/10/2016 - Lecture 3 - Normal linear regression with other priors / Gibbs sampling
28/10/2016 - No Lecture/no exercise session (public holiday)
04/11/2016 - Lecture 4 - Nonlinear regression model / Metropolis Hastings algorithm
11/11/2016 - Lecture 5 - Bayesian model averaging
18/11/2016 - Lecture 6 - Bayesian vector autoregressions
25/11/2016 - No Lecture/no exercise session (Dean's holiday)
02/12/2016 - Lecture 7 - Introduction to state space modelling & Kalman filter
09/12/2016 - Lecture 8 - Estimation of state-space models (classical)
16/12/2016 - Lecture 9 - Estimation of state-space models (Bayesian)

Lecture slides and exercise session materials will be available at the course website in Student Information System.

Partners

ČSOB
Deloitte
McKinsey & Company

Sponsors

CRIF
EY