Tools for Modern Macroeconometrics - JEM158

• Title: Tools for Modern Macroeconometrics
• Czech title: Tools for Modern Macroeconometrics
• Guaranteed by: Institute of Economic Studies (23-IES)
• Faculty: Faculty of Social Sciences
• Actual: from 2014 to 2016
• Semester: winter
• E-Credits: 6
• Examination process: winter s.:
• Hours per week, examination: winter s.:2/2, Ex [HT]
• Capacity: 30 / 30 (30)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Teaching methods: full-time
• Note: course can be enrolled in outside the study plan; enabled for web enrollment; priority enrollment if the course is part of the study plan
• Guarantor: PhDr. Marek Rusnák, Ph.D.
• Teacher(s): PhDr. Marek Rusnák, Ph.D.
• Class: Courses for incoming students

Annotation

Last update: Mgr. Michaela Čuprová (20.11.2019)

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.

Course completion requirements

Last update: PhDr. Jaromír Baxa, Ph.D. (15.02.2023)

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:

1. after lecture 4: ARIMA model of inflation and GDP, estimation of potential structural breaks, estimation of spectra of both series, forecast 1 period and 1 year ahead; evaluation of a cyclical position of the economy
2. after lecture 8: VAR I - short-run restrictions, sign restrictions + VAR forecasts.
3. after lecture 11: VAR II - local projections, Bayesian VAR + BVAR forecasts.

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

 

Literature

Last update: PhDr. Petr Bednařík, Ph.D. (06.06.2020)

Recommended textbooks:

• Koop, G. (2003): Bayesian Econometrics, John Wiley & Sons
• Kim, Ch. & Nelson, C.R. (1999): State-Space Models with Regime Switching, MIT Press

Teaching methods

Last update: PhDr. Jaromír Baxa, Ph.D. (07.02.2022)

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.

Syllabus

Last update: PhDr. Petr Bednařík, Ph.D. (06.06.2020)

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)