Applied Macroeconometrics

  • Credits: 5
  • Ending: Examination
  • Range: 2P + 2C
  • Semester: winter
  • Year: 2
  • Faculty of Economic Informatics

Teachers

Included in study programs

Data Science in Economics

Teaching results

Upon successful completion of this course, students will have knowledge of advanced methods of econometric approach to the analysis and modeling of macroeconomic phenomena and should be able to use basic Bayesian econometric techniques.
Students will gain practical skills and competencies with the application of advanced econometric methods in the analysis of macroeconomic problems using software R and Python.

Indicative content

1. Introduction to Bayesian analysis, basic concepts, prior, likelihood, posterior.
2. Markov chains Monte Carlo (MCMC), Metropolis algorithm, Gibbs sampler.
3. Bayesian estimation and analysis of a simple econometric model.
4. Models of Bayesian econometrics.
5. Bayesian estimation of VAR models.
6. Bayesian estimation of RBC/DSGE models.
7. Introduction to discrete dynamic models.
8. Dynamic stochastic dynamic economic processes.
9. Models in linear state space.
10. Introduction to Kalman filter.
11. Introduction to dynamic programming.
12. Chosen economic applications (growth models, search models).
13. Chosen economic applications (business cycle models).

Support literature

1. Bårdsen, G., Eitrheim, Ø., Jansen, E.S., Nymoen, R.: The Econometrics of Macroeconomic Modelling, Oxford, 2005
2. Chan, J., Koop, G., Poirier, D., Tobias, J.: Bayesian Econometric Methods, Cambridge University Press, 2019
3. Canova, F.: Methods for Applied Macroeconomic Research, Princeton University Press, 2007
4. DeJong, D.N., Dave, C.: Structural Macroeconometrics. Princeton University Press, 2011
5. Geweke, J.: Contemporary Bayesian Econometrics and Statistics, Wiley-Interscience, 2005
6. Ljungqvist, L., Sargent, T.J.: Recursive Macroeconomic Theory. 4. vydanie. MIT Press, 2018
7. Lukáčik, M., Lukáčiková, A., Szomolányi, K.: Bayesovská ekonometria. Letra Interactive, 2017
8. Sargent, T.J., Stachurski, J.: Quantitative Economics in Discrete and Continous Time. quantecon.org, 2020
9. Stachurski, J.: Economic Dynamics: Theory and Computation. MIT Press, 2009

Requirements to complete the course

projects for the final exam 40%
final exam 40%

Student workload

student workload: 130 h, participation in lectures 26 h, participation in seminars 26 h,
elaboration of a semester project 49 h, preparation for the final exam 29 h

Language whose command is required to complete the course

Slovak, English

Date of approval: 10.02.2023

Date of the latest change: 16.05.2022