BPE_ZAEK Introduction to Econometrics

Faculty of Economics and Administration
Autumn 2024
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
Mgr. Hana Fitzová, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (seminar tutor)
Ing. Jakub Moučka (seminar tutor)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Ing. Alexander Lipka (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
Timetable
Wed 16:00–17:50 P101, except Wed 18. 9., except Wed 6. 11.
  • Timetable of Seminar Groups:
BPE_ZAEK/01: Wed 18:00–19:50 VT204, except Wed 18. 9., except Wed 6. 11., D. Němec
BPE_ZAEK/02: Wed 8:00–9:50 VT206, except Wed 18. 9., except Wed 6. 11., J. Chalmovianský
BPE_ZAEK/03: Wed 10:00–11:50 VT206, except Wed 18. 9., except Wed 6. 11., J. Chalmovianský
BPE_ZAEK/04: Tue 16:00–17:50 VT202, except Tue 17. 9., except Tue 5. 11., V. Reichel
BPE_ZAEK/05: Tue 12:00–13:50 VT314, except Tue 17. 9., except Tue 5. 11., J. Chalmovianský
BPE_ZAEK/06: Tue 14:00–15:50 VT206, except Tue 17. 9., except Tue 5. 11., H. Fitzová
BPE_ZAEK/07: Tue 10:00–11:50 VT202, except Tue 17. 9., except Tue 5. 11., H. Fitzová
BPE_ZAEK/08: Wed 12:00–13:50 VT314, except Wed 18. 9., except Wed 6. 11., J. Chalmovianský
BPE_ZAEK/10: Mon 14:00–15:50 VT314, except Mon 16. 9., except Mon 4. 11., H. Fitzová
BPE_ZAEK/11: Fri 10:00–11:50 VT314, except Fri 20. 9., except Fri 8. 11., J. Moučka
BPE_ZAEK/12: Mon 16:00–17:50 VT206, except Mon 16. 9., except Mon 4. 11., A. Lipka
BPE_ZAEK/13: Mon 18:00–19:50 VT206, except Mon 16. 9., except Mon 4. 11., A. Lipka
BPE_ZAEK/14: Fri 8:00–9:50 VT204, except Fri 20. 9., except Fri 8. 11., J. Moučka
Prerequisites
knowledge of the elementary probability and mathematical statistics may be an advantage, but even without it, all the necessary basic knowledge will be discussed and mastered during the course
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 11 fields of study the course is directly associated with, display
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. A part of the course introduces various modern tools for analyzing economic time series regression. 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. Introduction to regression with time series variables and
  • 10. Introduction to panel data models
Literature
    required literature
  • KOOP, Gary. Introduction to econometrics. Chichester: John Wiley & Sons, 2008, 371 s. ISBN 9780470032701. info
  • WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. Seventh edition. Boston: Cengage Learning, 2020, xxi, 826. ISBN 9781337558860. 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
  • BÉKÉS, Gábor and Gábor KÉZDI. Data analysis for business, economics, and policy. First published. Cambridge: Cambridge University Press, 2021, xxiii, 714. ISBN 9781108483018. 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 and seminar activity (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); details of the course completion for students going abroad are contained in the Organisational guidelines (see study materials in IS)
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
Listed among pre-requisites of other courses
Teacher's information
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.
The course is also listed under the following terms Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)
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