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Ex.: Linear regression model. Ordinary least squares method.
Lab.: Use of Gretl software package. Introduction to the linear regression model.
Ex.: Gauss-Markov theorem. Validation and testing of the linear regression model.
Lab.: Selection of explanatory variables, interpretation of model parameters, examination of the statistical significance of model parameters.
Ex.: Autocorrelation and heteroskedasticity of the error term. Multicollinearity of the explanatory variables.
Lab.: Validation of the assumptions of the Gauss-Markov theorem. Testing of the linear regression model.
Ex.: Econometric prediction based on the linear regression models. Examples of the application of linear regression models in economics, finance and management.
Lab.: Testing of the statistical properties of the error term. Introduction to the weighted least squares method.
Ex.: Nonlinear models. Linearized models. Production and consumption functions. Marginal measures. Substitution.
Lab.: Econometric prediction. Point and interval prediction. Mean error of prediction ex ante, errors ex post.
Ex.: Models of qualitative variables. Linear probability model, logit model, probit model.
Lab.: Transformations of variables in econometric models. The use of binary variables.
Ex.: Examples of the application of logistic regression models in economics, finance and management.
Lab.: Models of qualitative variables in the Gretl software package.
Ex.: Introduction to time series. Stationarity of time series. Integration of time series.
Lab.: Logistic regression - model quality assessment.
Ex.: Time series econometrics. Cointegration. Distributed lags models. Error correction model.
Lab.: Time series analysis. Testing the integration of time series in the Gretl software package.
Ex.: Macroeconomic models. Applied general equilibrium models.
Lab.: Time series modeling. Cointegration analysis.
Ex.: Decision problem. Optimization model. Linear optimization problem. Graphical method of solving LP model. Properties of LP models.
Lab.: Econometric nonlinear models. Linearized models.
Ex.: Main types of optimization problems: product mix, diet, transportation problem, investment portfolio etc.
Lab.: Presentation of the Solver from Excel spreadsheet.
Ex.: Post-optimization analysis.
Lab.: Using the Solver from Excel spreadsheet for solving LP models.
Ex.: Modeling of the exemplary decision problems.
Lab.: Post-optimization analysis using Solver from Excel spreadsheet.
Ex.: Examples of the application of optimization in economics, finance and management.
Lab.: Integer optimization problems and examples of decision models with and without constraints.
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