Mastering the theoretical knowledge presented in the course. To enable students to independently understand, set up and evaluate econometric models with real data using software support.
1. Introduction to econometrics. Basic concepts of probability theory and inferential statistics (parameter estimation, properties of estimators, statistical hypothesis testing). Econometric models. Setting up an econometric model. Selecting variables for an econometric model.
2. Simple linear regression model. Multiple linear regression model (model parameter estimation, least squares method, classical assumptions). Testing the statistical significance of regression parameters. Measuring the fit of the regression model, coefficient of determination, standard error of regression. Testing the statistical significance of the regression model (ANOVA). Diagnostic tests (multicollinearity, heteroscedasticity, autocorrelation). Logistic regression.
1. Classify the theoretical foundations of econometric research
2. Test the assumptions of the econometric model
3. Interpret the parameters of an econometric model
4. Based on the implementation of appropriate econometric methods, independently draw conclusions about the hypotheses set
5. Review the established and tested econometric model
