Time Series Econometrics

  • Credits: 6
  • Ending: Examination
  • Range: 2P + 2C
  • Semester: summer
  • 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 currently used methods of time series econometrics and should be able to use econometric techniques and procedures for standard time series (not high frequency data).
Students will gain practical skills and competencies with the application of advanced econometric methods in the analysis of economic problems of time series using software R and Python.

Indicative content

1. Dynamic single-equation models.
2. Impulse response function.
3. Stochastic processes and trend in processes.
4. Stationarity and testing of stationarity.
5. Cointegration in single-equation model, ECM, Engle-Granger, Bounds tests.
6. Vector autoregressive models and their estimation.
7. Testing and identification of vector autoregressive models
8. Structural vector autoregressive models - Choleski decomposition.
9. Structural vector autoregressive models - different methods of identification.
10. Vector cointegration models, VECM.
11. Structural vector cointegration models.
12. Dynamic state-space models.
13. Kalman filter and estimation of state-space models.

Support literature

1. Kleiber, C., Zeileis, A.: Applied Econometrics with R. Springer, 2008.
2. Pfaff, B.: Analysis of Integrated and Cointegrated Time Series with R, 2nd Edition. Springer-Verlag, 2008.
3. Neusser, K.: Time Series Econometrics. Springer-Verlag, 2016.
4. Enders, W.: Applied Econometric Time Series, Second edition. John Wiley and Sons, 2004.
5. Lütkepohl, H., Krätzig, M.: Applied Time Series Econometrics. New York: Cambridge University Press, 2005.
6. Lütkepohl, H.: New Introduction to Multiple Time Series Analysis. Berlin: Springer Verlag, 2005.

Requirements to complete the course

individual work and continuous tests 20%
project for the final exam 40%
final exam 40%

Student workload

student workload: 156 h, participation in lectures 26 h, participation in seminars 26 h,
elaboration of a semester project 62 h, preparation for the final exam 42 h

Language whose command is required to complete the course

Slovak, English

Date of approval: 11.03.2024

Date of the latest change: 16.05.2022