Advanced Analytical Methods I

  • Credits: 12
  • Ending: Examination
  • Range: 16sP
  • Semester: winter
  • Year: 1
  • Faculty of Economic Informatics

Teachers

Included in study programs

Data Science in Economics

Data Science in Economics

Teaching results

In particular, students will acquire the following abilities:
- knowledge of economic data analysis,
- knowledge of the construction of mathematical models,

Students will acquire in particular the following skills:
- ability to construct and use mathematical models,
- solving economic problems using adequate software.

Students will acquire the following competencies:
- skills and competences in the creation of mathematical models using adequate software.

Indicative content

The course is focused on the creation of their own mathematical models usable in economic practice. Based on knowledge from economic theory, the principles of creating mathematical models are explained. Attention is paid to the issue of mathematical economics and its analysis based on optimization and economic-statistical models. Another area is the use of modern information tools focused on the construction of mathematical models. Software tools (eg R language, Python language, GAMS, Eviews) are used to solve tasks.
• Decision theory.
• Classification of models and methods for solving mathematical models.
• Mathematical programming and alternative ways of solving problems of mathematical programming.
• Modelling of economic systems.
• Modelling in the field of mathematical economics.
• Statistical-econometric modelling.

Support literature

1. Banerjee, S. (2014). Mathematical Modeling: Models, Analysis and Applications (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b16526
2. Williams, H. P. (2013). Model building in mathematical programming. John Wiley & Sons.
3. Neogy, S. K., Bapat, R. B. and Dubey, D. (Eds.). (2018). Mathematical Programming and Game Theory. Springer Singapore.
4. Steele, Katie and H. Orri Stefánsson, "Decision Theory", The Stanford Encyclopedia of Philosophy (Winter 2020 Edition), Edward N. Zalta (ed.), URL = .
5. Davendra, D. and Zelinka, I. (2016). Self-organizing migrating algorithm. New optimization techniques in engineering.
6. Greene, W.H.: Econometric Analysis, 8th ed. Pearson, 2018

Requirements to complete the course

15 % - active participation on lectures
25 % - semester project processed in statistical software and/or free software environment (e.g. R, Python, GAMS)
25 % - presentation of the semester project
35 % - final exam

Student workload

312 hours
Distribution of study load
Lectures participation: 16 hours
Preparation for the lectures: 80 hours
Elaboration of the semester project: 128 hours
Preparation for the final exam: 88 hours

Language whose command is required to complete the course

Slovak, English

Date of approval: 11.03.2024

Date of the latest change: 16.05.2022