Econometrics II

  • Credits: 5
  • Ending: Examination
  • Range: 2P + 2C
  • Semester: summer
  • Year: 3
  • Faculty of Economic Informatics

Teachers

Included in study programs

Data Science in Economic

Teaching results

Upon successful completion of this course, students will have knowledge of the basic areas of econometric modeling, with emphasis on the study of empirical applications.
Students will gain practical skills and competencies through the development of a project and the implementation of simple empirical research. At the same time, they will gain skills in using the software R.

Indicative content

1. General linear model with more than one explanatory variables.
2. Structural changes of variables and their consequences on the estimation of models.
3. Introduction to panel data analysis. Pool model. Least Squares Dummy Variable (LSDV).
4. Introduction to panel data analysis. Cross section fixed effects and random effects model.
5. Estimation using instrumental variables, testing of instruments and endogeneity.
6. Introduction to multi-equation models. Two-stage least squares method.
7. Multi-equation models, recursive models, and models with seemingly unrelated regressions.
8. Basic stochastic processes, white noise, random walk and their properties.
9. Autoregressive processes and moving average processes. Box-Jenkins and ARIMA models.
10. Seasonal time series, Box-Jenkins methodology of SARIMA models.
11. Stationarity of processes and its testing using unit root tests.
12. Non-stationarity of processes with respect to mean and variance, transformation of time series generated by non-stationary processes, differentiation and logarithmization.
13. Co-integration of non-stationary time series, Engle and Granger procedure, error correction models and their estimation.

Support literature

1. Lukáčiková, A., Lukáčik, M., Szomolányi, K.: Úvod do ekonometrie s jazykom R. Bratislava: Letra Edu, 2022
2. Brooks, C.: Introductory Econometrics for Finance, 4th ed. Cambridge University Press, 2019
3. Stewart, K.G.: Introduction to Applied Econometrics. Thomson, Brooks/Cole, 2005
4. Levendis, J. D.: Time Series Econometrics: Learning Through Replication. Springer, 2018
5. Hill, R.C., Griffiths, W.E., Lim, G.C.: Principles of Econometrics, 5th ed. John Wiley, 2018
6. Gujarati, D., Porter, D. Gunasekar, S.: Basic Econometrics. McGraw 5th ed, New York, 2017

Syllabus

1. General linear model with more than one explanatory variables. 2. Structural changes of variables and their consequences on the estimation of models. 3. Introduction to panel data analysis. Pool model. Least Squares Dummy Variable (LSDV). 4. Introduction to panel data analysis. Cross section fixed effects and random effects model. 5. Estimation using instrumental variables, testing of instruments and endogeneity. 6. Introduction to multi-equation models. Two-stage least squares method. 7. Multi-equation models, recursive models, and models with seemingly unrelated regressions. 8. Basic stochastic processes, white noise, random walk and their properties. 9. Autoregressive processes and moving average processes. Box-Jenkins and ARIMA models. 10. Seasonal time series, Box-Jenkins methodology of SARIMA models. 11. Stationarity of processes and its testing using unit root tests. 12. Non-stationarity of processes with respect to mean and variance, transformation of time series generated by non-stationary processes, differentiation and logarithmization. 13. Co-integration of non-stationary time series, Engle and Granger procedure, error correction models and their estimation.

Requirements to complete the course

individual work and continuous tests 20%
project 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 39 h, preparation for the final exam 39 h)

Language whose command is required to complete the course

Slovak, English

Date of approval: 10.02.2023

Date of the latest change: 17.05.2022