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Tools for Modern Macroeconometrics 2023.docx | Syllabus | PhDr. Jaromír Baxa, Ph.D. |
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THE COURSE WILL NOT BE OFFERED IN 2017/2018 ACADEMIC YEAR.
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. Poslední úprava: Čuprová Michaela, Mgr. (20.11.2019)
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Assignment: Final paper Choose one country for which you will estimate the propagation of a shock of your interest and forecast the GDP growth and inflation. To do that, you will utilize the methods covered in the course and explore their properties, forecast performance, and robustness. The final paper will be prepared throughout the whole semester, with three intermediate deadlines:
The outcomes of these intermediate stages will be presented and discussed during the seminars. Grades: Intermediate stages and presentations - 20 points each (60 in total), final paper + presentation, and participation at the workshop 40 points. The final paper and participation at the workshop are necessary conditions to pass the course, even if the sum of intermediate points exceeds 50.5 Grading scale: 100 - 91 A; 90 - 81 B; 80 - 71 C; 70 - 61 D ; 60 - 51 E; 50 - 0 F
Poslední úprava: Baxa Jaromír, PhDr., Ph.D. (15.02.2023)
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Recommended textbooks:
Poslední úprava: Bednařík Petr, PhDr., Ph.D. (06.06.2020)
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Lectures will provide context and description of the empirical methods. Students are supposed to cover selected methods in regular problem sets, that are based on replications of academic papers. Sample R codes will be provided. Problem sets are presented and discussed during the seminars. Poslední úprava: Baxa Jaromír, PhDr., Ph.D. (07.02.2022)
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THE COURSE WILL NOT BE OFFERED IN 2017/2018 ACADEMIC YEAR.
Lecture 1 - Course overview / Introduction to Bayesian Econometrics Lecture 2 - Normal linear regression with natural conjugate prior Lecture 3 - Normal linear regression with other priors / Gibbs sampling Lecture 4 - Nonlinear regression model / Metropolis Hastings algorithm Lecture 5 - Bayesian model averaging Lecture 6 - Bayesian vector autoregressions Lecture 7 - Introduction to state space modelling & Kalman filter Lecture 8 - Estimation of state-space models (classical) Lecture 9 - Estimation of state-space models (Bayesian) Poslední úprava: Bednařík Petr, PhDr., Ph.D. (06.06.2020)
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