ESF:DXE_EREC Essentials of R for Econometri - Course Information

DXE_EREC Essentials of R for Econometrics

Extent and Intensity

0/5/0. 1 credit(s). Type of Completion: z (credit).

Teacher(s)

doc. Ing. Štěpán Mikula, Ph.D. (lecturer)

Guaranteed by

doc. Ing. Štěpán Mikula, Ph.D.
Department of Economics – Faculty of Economics and Administration
Supplier department: Department of Economics – Faculty of Economics and Administration

Timetable

Fri 21. 10. 9:00–11:00 S313, 13:00–15:00 S313

Course Enrolment Limitations

The course is only offered to the students of the study fields the course is directly associated with.

fields of study / plans the course is directly associated with

• Business Economy and Management (programme ESF, D-PEMA) (2)
• Economic Policy (programme ESF, D-HOSPA) (2)
• Economics (programme ESF, D-EKONA) (2)
• Economics (programme ESF, D-EKON) (2)
• Finance (programme ESF, D-FIN) (2)
• Finance (programme ESF, D-FINA) (2)
• Finance (programme ESF, D-FINN) (2)
• Economic Policy (programme ESF, D-HOSP) (2)
• Business Economy and Management (programme ESF, D-PEM) (2)
• Public Economics (programme ESF, D-VEEKA) (2)
• Regional Economics (programme ESF, D-REEKA) (2)
• Regional economics (programme ESF, D-REEK) (2)
• Public Economics (programme ESF, D-VEEK) (2)

Course objectives (in Czech)

The course is designed for PhD students of the Econometrics course who have zero experience with R or programming in general. The course provides an overview of essential concepts that are necessary for learning Econometrics in R.

Learning outcomes (in Czech)

After completing the course, a student will be able:
- to read simple R codes and understand basic concepts;
- to load/read data clean data;
- to conduct simple econometric analysis;
- to continue studying R

Syllabus (in Czech)

• - R and RStudio/Posit IDE
• - Workspace and variables
• - Installing/loading packages
• - Scripts
• - Basic data types and classes
• - Loading/reading data
• - Formulas
• - Estimation functions (lm(), and feols() from fixest package)

Literature

required literature
• WICKHAM, Hadley and Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. First edition. Sebastopol, CA: O'Reilly, 2016, xxv, 492. ISBN 9781491910399. info

Teaching methods (in Czech)

Lecture and class demonstrations/discussions

Assessment methods (in Czech)

Attendance and active participation in classes

Language of instruction

English

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.

• Enrolment Statistics (Autumn 2022, recent)
  
• Permalink: https://is.muni.cz/course/econ/autumn2022/DXE_EREC