MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2019
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Bechný, 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
Timetable
Wed 10:00–11:50 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:00–15:50 VT204, D. Němec
MPE_EKON/02: Wed 12:00–13:50 VT204, D. Němec
Prerequisites
(! 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 7 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.
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.
Syllabus
  • 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;
Literature
    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
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
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 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2019, recent)
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