1. Introduction to econometrics (definition of econometrics, relation to other scientific disciplines, clarification of basic concepts, origin
econometrics, process of econometric modeling)
2. Analysis of time series (types of time series (TS), methods of analysis of TS, decomposition of TS, Box-Jenkins methodology, regression analysis, spectral analysis, model verification)
3. Simple linear regression model (meaning of regression analysis, population versus selective regression line, essence of least squares approximation (LSA), fit of regression line to data, assumptions of classical simple regression model and their verification)
4. Multiple regression model_1 (definition of classical multivariate linear regression model (CMLRM), assumptions of CMLRM, matrix notation of CMLRM, corrected determination coefficient)
5. Multiple regression model_2 (residue normality testing)
6. Statistical verification (regression coefficients, model as a whole)
7. Econometric verification - problem of autocorrelation
8. Econometric verification - problem of heteroskedasticity
9. Econometric verification - problem of multicolinearity
10. Economic verification - model specification
11. Prediction (error of prediction, point or interval prediction, ex-post and ex-ante prediction, mean value prediction or individual values prediction of the explained variable)
12. Functional forms (exponential model, LIN-LOG model, LOG-LIN model, reciprocal model)
13. Technique of artificial variables (qualitative or discrete character of factors and technique of artificial variables, ANOVA models, ANCOVA models, regression models with 1 quantitative and 1 qualitative variable with broader scale, application of technology artificial variables)
14. Panel data (definition of panel models, fixed effects (time or space, level constants or slope constant), random effects)
econometrics, process of econometric modeling)
2. Analysis of time series (types of time series (TS), methods of analysis of TS, decomposition of TS, Box-Jenkins methodology, regression analysis, spectral analysis, model verification)
3. Simple linear regression model (meaning of regression analysis, population versus selective regression line, essence of least squares approximation (LSA), fit of regression line to data, assumptions of classical simple regression model and their verification)
4. Multiple regression model_1 (definition of classical multivariate linear regression model (CMLRM), assumptions of CMLRM, matrix notation of CMLRM, corrected determination coefficient)
5. Multiple regression model_2 (residue normality testing)
6. Statistical verification (regression coefficients, model as a whole)
7. Econometric verification - problem of autocorrelation
8. Econometric verification - problem of heteroskedasticity
9. Econometric verification - problem of multicolinearity
10. Economic verification - model specification
11. Prediction (error of prediction, point or interval prediction, ex-post and ex-ante prediction, mean value prediction or individual values prediction of the explained variable)
12. Functional forms (exponential model, LIN-LOG model, LOG-LIN model, reciprocal model)
13. Technique of artificial variables (qualitative or discrete character of factors and technique of artificial variables, ANOVA models, ANCOVA models, regression models with 1 quantitative and 1 qualitative variable with broader scale, application of technology artificial variables)
14. Panel data (definition of panel models, fixed effects (time or space, level constants or slope constant), random effects)