Econometric Modeling
- Credits: 6
- Ending: Examination
- Range: 2P + 2C
- Semester: winter
- Year: 1
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Upon successful completion of this course, students will have knowledge of advanced methods of econometric approach to the analysis and modeling of economic phenomena and processes and should be able to use econometric techniques and procedures for different types of data.
Students will gain practical skills and competencies with the application of advanced econometric methods in the analysis of economic problems using software R and Python.
Indicative content
1. Random variable and its distribution, linear regression model, least squares method, statistical properties of small samples, unbiasedness, efficiency, hypothesis testing, linear hypotheses.
2. Maximum likelihood method, Cramer-Rao theorem, information matrix.
3. Testing of nonlinear hypotheses, Wald test, Lagrange multiplier test, likelihood ratio test, delta method.
4. Estimation of models with restrictions and nonlinear models, Gauss and Newton method, Newton and Raphson method.
5. Generalized least squares method, spherical stochastic term, heteroskedasticity and autocorrelation robust estimators, White estimator and Newey and West estimator.
6. Dynamic models, dynamic multipliers and impulse response functions.
7. Introduction to asymptotic theory, endogenous explanatory variables, instrumental variables, introduction to the method of moments.
8. Generalized method of moments and estimation of forward-looking models.
9. Applications of the time series econometrics models and prognostic models.
10. Applications of the financial econometrics models.
11. Applications of the spatial econometrics models.
12. Applications of the models in macroeconometric modeling.
13. Applications of the quantitative economics models.
Support literature
1. Greene, W.H.: Econometric Analysis, 8th ed. Pearson, 2018
2. Kleiber, C., Zeileis, A.: Applied Econometrics with R. Springer, 2008
3. Pesaran, M.H.: Time Series and Panel Data Econometrics. Oxford University Press, 2015
4. Hatrák, M.: Ekonometria. Bratislava: IURA Edition, 2007
5. Angrist, J.D., Pischke, J.S.: Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press, 2009
6. Hayashi, F.: Econometrics. Princeton University Press, 2000
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: 10.02.2023
Date of the latest change: 16.05.2022