| Kredit: | 6 |
|---|---|
| Role předmětu: | Anglicky CFS - elective EEI a HP - povinně volitelný ET - povinně volitelný F,FT a B - povinně volitelný Magisterský - vše MEF - elective NEVYUČUJE SE Semestr - letní |
| Garanti: | Jakub Matějů |
| Stránky kurzu: | JEM158 |
| Literatura: | |
| Popis: | 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. |
