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).
- prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
RNDr. Dalibor Moravanský, CSc. (seminar tutor)
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: Lydie Pravdová
- Wed 11:05–12:45 P106
- Timetable of Seminar Groups:
MPE_EKON/02: Thu 14:35–16:15 VT105, D. Moravanský
MPE_EKON/03: Thu 16:20–17:55 VT105, D. Moravanský
- ! PMTEII Theory of Econometrics
basic matrix algebra, elementary probability and mathematical statistics
- 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
- Mathematics - Economics (programme PřF, N-AM)
- 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, 2SLS, 3SLS, GMM, LIML, FIML 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, De Boer, Franses, Kloek, and Van Dijk: Econometric Methods with Applications in Business and Economics. Oxford University Press, 2004.
- recommended literature
- CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008. 538 s. ISBN 9788086929439. info
- HAYASHI, Fumio. Econometrics. Princeton: Princeton University Press, 2000. xxiii, 683. ISBN 0691010188. info
- KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008. xii, 585. ISBN 9781405182584. info
- GREENE, William H. Econometric analysis. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008. xxxvii, 11. ISBN 9780135132456. info
- HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994. xiv, 799 s. ISBN 0-691-04289-6. 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: Nezapisují si studenti, kteří absolvovali předmět PMTEII.
- Listed among pre-requisites of other courses