Econometrics I (in English)
- Ending: Examination
- Range: 2P + 2C
- Semester: winter
- Year: 3
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Upon successful completion of this course, students will have knowledge of the econometric
approach to the analysis, modeling and prediction of economic phenomena and processes and
should be able to use the basics of econometric techniques.
Students will gain practical skills and competencies with the application of econometric methods
in the analysis of economic problems using econometric software.
Indicative content
1. Characteristics of econometric approach to the analysis of economic phenomena. Econometric model. Phases of econometric modeling
2. Two-variable regression model. Deterministic and stochastic part of the model, nature of stochastic term. Standard assumptions of a linear model.
3. Estimation of linear model parameters. Statistical properties of estimators. Least squares
method. Properties of the least squares method.
4. General linear model. Model in matrix form. Least squares method for k-variable model.
5. Model verification. Coefficient of determination. Testing the statistical significance of
individual parameters of the model. Interval estimation and hypothesis testing.
6. Functional forms of regression models – log-log model, semi-log models, reciprocal models.
7. Qualitative variables and their modeling.
8. Regression on dummy variables. Seasonality, fluctuations, structural breaks, and their testing. 9. Violations of the assumptions of the classical model. Autocorrelation – detecting and implications. 10. Violations of the assumptions of the classical model. Autocorrelation – solving, model dynamization and generalized least squares method. 11. Violations of the assumptions of the classical model. Heteroskedasticity – detecting and implications, solving, weighted least squares method. 12. Violations of the assumptions of the classical model. Multicollinearity – detecting and implications, solution options. 13. Forecasting with single-equation model. Forecasting error. Confidence interval for the forecasts.
Support literature
1. Gujarati, D., Porter, D. Gunasekar, S.: Basic Econometrics. McGraw 5th ed., New York, 2017
2. Gujarati, D.: Econometrics by Example 2nd ed., Red Globe Press, 2014
3. Wooldridge, J.: Introductory Econometrics: A Modern Approach 7th ed., Cengage Learning, 2019
4. Stock, J., Watson, M.: Introduction to Econometrics 4th ed., Pearson, 2018
Requirements to complete the course
individual work and continuous tests 30%
project for the final exam 30%
final exam 40%
Student workload
student workload: 156 h
participation in lectures 26 h,
participation in seminars 26 h,
elaboration of a semester project 52 h,
preparation for the final exam 52 h
Language whose command is required to complete the course
English
Date of approval: 11.03.2024
Date of the latest change: 13.05.2022