1. Introduction to Econometrics
Definition of econometrics, its relationship to other scientific disciplines, explanation of basic concepts, origins of econometrics, and the process of econometric modeling.
2. Time Series Analysis
Types of time series, methods of time series analysis, decomposition of time series, regression analysis, and model verification.
3. Simple Linear Regression Model
Importance of regression analysis, population vs. sample regression line, the principle of the least squares method, goodness of fit, assumptions of the classical simple regression model and their testing.
4. Multiple Regression Model
Definition of the classical multivariate linear regression model, assumptions, matrix notation, and adjusted coefficient of determination.
5. Statistical Verification
Testing of regression coefficients and the model as a whole.
6. Econometric Verification
Detection and treatment of autocorrelation, heteroskedasticity, multicollinearity, normality, and model specification errors.
7. Functional Forms
Exponential model, LIN-LOG model, LOG-LIN model, reciprocal model, and economic interpretation of regression parameters.
8. Forecasting
Forecast error, point and interval forecasts, ex-post and ex-ante forecasting.
9. Dummy Variable Technique
10. Panel Data
Definition of panel data models and fixed effects.
More detailed information about the content of individual lectures, including the timetable, is provided in the electronic course Econometrics in the LMS Moodle.
Definition of econometrics, its relationship to other scientific disciplines, explanation of basic concepts, origins of econometrics, and the process of econometric modeling.
2. Time Series Analysis
Types of time series, methods of time series analysis, decomposition of time series, regression analysis, and model verification.
3. Simple Linear Regression Model
Importance of regression analysis, population vs. sample regression line, the principle of the least squares method, goodness of fit, assumptions of the classical simple regression model and their testing.
4. Multiple Regression Model
Definition of the classical multivariate linear regression model, assumptions, matrix notation, and adjusted coefficient of determination.
5. Statistical Verification
Testing of regression coefficients and the model as a whole.
6. Econometric Verification
Detection and treatment of autocorrelation, heteroskedasticity, multicollinearity, normality, and model specification errors.
7. Functional Forms
Exponential model, LIN-LOG model, LOG-LIN model, reciprocal model, and economic interpretation of regression parameters.
8. Forecasting
Forecast error, point and interval forecasts, ex-post and ex-ante forecasting.
9. Dummy Variable Technique
10. Panel Data
Definition of panel data models and fixed effects.
More detailed information about the content of individual lectures, including the timetable, is provided in the electronic course Econometrics in the LMS Moodle.