ESF:MPE_EKON Econometrics - Course Information
MPE_EKON EconometricsFaculty of Economics and Administration
- Extent and Intensity
- 2/2/0. 12 credit(s). Type of Completion: zk (examination).
- doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
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
- Wed 11:05–12:45 P106
- Timetable of Seminar Groups:
MPE_EKON/02: Wed 18:00–19:35 VT204, D. Němec
- (! 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
- 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.
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. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
- 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
- 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
- 9. State space models - Kalman filter and maximal likelihood estimation;
- required literature
- HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004. xxv, 787. ISBN 9780199268016. info
- CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008. 538 s. ISBN 9788086929439. info
- recommended literature
- KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008. xii, 585. ISBN 9781405182584. info
- ENDERS, Walter. Applied econometric time series. 4th ed. Hoboken: Wiley, 2015. x, 485. ISBN 9781118808566. info
- HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994. xiv, 799 s. ISBN 0-691-04289-6. info
- GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012. 1228 s. ISBN 9780273753568. info
- BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008. xiii, 351. ISBN 9780470518861. info
- Teaching methods
- lectures, class discussion, computer labs practices, drills
- Assessment methods
- final project, written and oral exam
- Language of instruction
- 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