1. Publishing in research journals I
a. Information sources, citation databases.
b. Structure of the manuscript.
2. Publishing in research journals II
a. Review and publishing process.
c. Typesetting: MS Word vs. LaTeX.
d. Publication ethics.
f. Attachments to a manuscript (cover letter, highlights, graphical abstract).
3. LaTeX – professional typesetting (Overleaf)
a. Text organization and structure.
c. Math formulas.
d. Citations and references (bibtex).
e. Beamer for presentations.
4. Python – universal language for (not only) economic modelling
5. Decision-making models
a. Methodology of mathematical modelling.
b. Decision support models.
c. Different datatypes: random, uncertain, mssing data.
e. Modelling the preferences of a decision-maker.
f. Basic methods of multi-criteria decision-making.
6. Multi-criteria decision-making
a. Ranking and sorting.
b. Group decision-making.
c. Mathematical programming (Linear vs. non-linear models).
d. Stochastic programming.
e. Robust programming.
e. Monte Carlo simulation.
f. DEA models.
7. Advanced statistical models
a. Random variable and its description.
b. Hypothesis testing, statistical significance.
a. Selected statistical tests (ANOVA, tests in contingency tables).
8. Clustering methods and data reduction
a. Factor analysis.
b. Clustering analysis (hierarchical clustering, k-means).
c. Structural modelling (SEM).
9. Regression models I – estimates and parameters' forecasting
a. Linear regression.
b. OLS, Maximum likelihood, Generalized Method of Moments (GMM).
c. Quantile regression.
d. Logistic regression.
10. Regression models II
a. Panel regression.
b. Difference in differences (DD).
c. Event studies.
11. Regression models III
a. (S)VAR models.
b. VECM models.
c. Local projection.
12. Bayesian data analysis
a. Bayesian vs. classical approach to statistics.
b. Bayes theorem.
c. Bayesian inference in econometrics.
13. Selected areas of Computational Intelligence (AI)
a. Principles of neural networks.
b. Machine learning (ML) for forecasting.
c. Deep learning.
a. Information sources, citation databases.
b. Structure of the manuscript.
2. Publishing in research journals II
a. Review and publishing process.
c. Typesetting: MS Word vs. LaTeX.
d. Publication ethics.
f. Attachments to a manuscript (cover letter, highlights, graphical abstract).
3. LaTeX – professional typesetting (Overleaf)
a. Text organization and structure.
c. Math formulas.
d. Citations and references (bibtex).
e. Beamer for presentations.
4. Python – universal language for (not only) economic modelling
5. Decision-making models
a. Methodology of mathematical modelling.
b. Decision support models.
c. Different datatypes: random, uncertain, mssing data.
e. Modelling the preferences of a decision-maker.
f. Basic methods of multi-criteria decision-making.
6. Multi-criteria decision-making
a. Ranking and sorting.
b. Group decision-making.
c. Mathematical programming (Linear vs. non-linear models).
d. Stochastic programming.
e. Robust programming.
e. Monte Carlo simulation.
f. DEA models.
7. Advanced statistical models
a. Random variable and its description.
b. Hypothesis testing, statistical significance.
a. Selected statistical tests (ANOVA, tests in contingency tables).
8. Clustering methods and data reduction
a. Factor analysis.
b. Clustering analysis (hierarchical clustering, k-means).
c. Structural modelling (SEM).
9. Regression models I – estimates and parameters' forecasting
a. Linear regression.
b. OLS, Maximum likelihood, Generalized Method of Moments (GMM).
c. Quantile regression.
d. Logistic regression.
10. Regression models II
a. Panel regression.
b. Difference in differences (DD).
c. Event studies.
11. Regression models III
a. (S)VAR models.
b. VECM models.
c. Local projection.
12. Bayesian data analysis
a. Bayesian vs. classical approach to statistics.
b. Bayes theorem.
c. Bayesian inference in econometrics.
13. Selected areas of Computational Intelligence (AI)
a. Principles of neural networks.
b. Machine learning (ML) for forecasting.
c. Deep learning.