BKE_ZAEE Introduction to Econometrics

Faculty of Economics and Administration
Autumn 2023
Extent and Intensity
0/0/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
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
Timetable
Sat 14. 10. 12:00–15:50 P302b, Sat 25. 11. 12:00–15:50 P302b, Fri 15. 12. 16:00–19:50 P302b
Prerequisites
FORMA ( K )
elementary probability and mathematical statistics
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 econometric methods important in economics, finance and other business subjects. It provides skills in regression essential for understanding much of the literature of economics, finance, and empirical studies in other areas of business.
We begin with the simple regression and multiple regression models. They are treated in depth and in range of applications. Careful attention is given to the interpretations of regression results and hypothesis testing. Moreover, further topics in regression analysis are presented including regression with panel data and binary dependent variable.
By the end of the course students should be able to use regression models in many different applications, and to critically examine reported regression results in empirical research in economics and other business studies. They will be able to identify and deal with a number of econometric problems in the analysis of time series and cross-section data, and will have experience of a range of basic econometric methods.
Learning outcomes
The course is designed to give students an understanding of why econometrics is necessary and to provide them with a working knowledge of basic econometric tools so that:
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 econometric tools.
They have a foundation and understanding for further study of econometrics.
They have an appreciation of the range of more advanced techniques that exists and that may be covered in later econometric courses.
Syllabus
  • 1. Introduction to econometrics and working with data
  • 2. A non-technical introduction to regression
  • 3. Simple regression model
  • 4. Multiple regression model
  • 5. Freeing up the classical assumptions - heteroskedasticity
  • 6. Freeing up the classical assumptions - autocorrelated errors
  • 7. Instrumental variables method
  • 8. Qualitative choice and limited dependent variable models
  • 9. Models for panel data
Literature
    required literature
  • KOOP, Gary. Introduction to econometrics. Chichester: John Wiley & Sons, 2008, 371 s. ISBN 9780470032701. info
    recommended literature
  • 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
  • WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. Seventh edition. Boston: Cengage Learning, 2020, xxi, 826. ISBN 9781337558860. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
  • HEISS, Florian and Daniel BRUNNER. Using Python for introductory econometrics. 1st edition. Düsseldorf: Florian Heiss, 2020, 418 stran. ISBN 9788648436763. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
homeworks assignments (30% of the final grade), final project and its oral defense (oral exam, 40 % of the final grade), written exam (30 % of the final grade)
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
Information on the extent and intensity of the course: tutorial 12 hodin.
The course is also listed under the following terms Autumn 2022, Autumn 2024.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/econ/autumn2023/BKE_ZAEE