MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2022
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Taught in person.
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský (lecturer)
Mgr. Jakub Chalmovianský (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (assistant)
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
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(! NOWANY ( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
basic matrix algebra, elementary probability and mathematical statistics, pssing the course Introduction to econometrics (BPE_ZAEK) (recommended)
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 12 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
Learning outcomes
The course is designed to provide students with a working knowledge of basic and advanced 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 econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004. xxv, 787. ISBN 9780199268016. info
    recommended literature
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008. 538 s. ISBN 9788086929439. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008. xii, 585. ISBN 9781405182584. info
  • BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008. xiii, 351. ISBN 9780470518861. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012. 1228 s. ISBN 9780273753568. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
semestral projects, oral exam
Language of instruction
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Přednášky jsou dostupné online a ze záznamu.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021.
  • Enrolment Statistics (Spring 2022, recent)
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