MPE_MOIR Modelling in R

Faculty of Economics and Administration
Autumn 2024

The course is not taught in Autumn 2024

Extent and Intensity
0/2/0. 4 credit(s). Type of Completion: z (credit).
In-person direct teaching
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration
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
Course objectives
The course is designed to give students experience of using R fo data analysis and for applying basic statistical and econometric methods important in aaplied economics, finance and business.
we start with the basics of working with R in exploratory data analysis. We will proceed further with introductory statistics (hypothesis testing) and basic econometric modelling. The statistical and econometric models are treated in depth and in range of applications. Careful attention is given to the interpretations of statistical and econometrics results and hypothesis testing.
By the end of the course students should be able to use R in data analysis, statistics and econometrics, and to critically examine reported results in empirical research in applied economics.
Learning outcomes
The course is designed to give students an understanding of why econometric and statistical modelling is necessary and to provide them with a working knowledge of basic statistical and econometric tools using R so that:
They can apply these tools to exploratory data analysis.
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use basic statistical and econometric tools.
They have a foundation and understanding for further study of econometrics, statistics and data science.
They have an appreciation of the range of more advanced techniques that exists and that may be covered in later statistical and econometric courses.
Syllabus
  • 1. Introduction to R
  • 2. Preparing data for analysis
  • 3. Exploratory data analysis
  • 4. Comparison and Correlation
  • 5. Generalizing from data
  • 6. Testing hypotheses
  • 7. Simple regression
  • 8. Complicated patterns and messy data
  • 9. Generalizing results of a regression
  • 10. Multiple Linear regression
  • 11. Modeling Probabilities
  • 12. Regression with Time Series Data
Literature
    required literature
  • BÉKÉS, Gábor and Gábor KÉZDI. Data analysis for business, economics, and policy. First published. Cambridge: Cambridge University Press, 2021, xxiii, 714. ISBN 9781108483018. info
    recommended literature
  • VERZANI, John. Using R for introductory statistics. Second edition. Boca Raton: CRC press, Taylor & Francis Group, 2014, xvii, 502. ISBN 9781466590731. info
  • MANN, Prem S. Introductory statistics. Ninth edition. Hoboken: Wiley, 2019, 171 stran. ISBN 9781119148296. info
  • HILL, R. Carter, William E. GRIFFITHS and Guay C. LIM. Principles of econometrics. Fifth edition. Hoboken: Wiley Custom, 2018, xxvi, 878. ISBN 9781119510567. info
  • HEISS, Florian. Using R for introductory econometrics. 2nd edition. Düsseldorf: Florian Heiss, 2020, 368 stran. ISBN 9788648424364. info
Teaching methods
class discussion, computer labs practices, projects
Assessment methods
homeworks, final project
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
  • Permalink: https://is.muni.cz/course/econ/autumn2024/MPE_MOIR