MPE_ECNM Econometrics

Faculty of Economics and Administration
Spring 2024
Extent and Intensity
2/2/0. 8 credit(s). Type of Completion: zk (examination).
Taught in person.
Nicolás Blampied (lecturer)
Ing. Jaroslav Groero, MA, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
Nicolás Blampied (seminar tutor)
Ing. Jaroslav Groero, MA, Ph.D. (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: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration
Fri 10:00–11:50 VT314, except Fri 5. 4.
  • Timetable of Seminar Groups:
MPE_ECNM/01: Fri 12:00–13:50 VT314, except Fri 5. 4., N. Blampied, J. Groero
(! MPE_AECM Econometrics ) && (! MPE_EKON Econometrics ) && (! NOWANY ( MPE_AECM Econometrics , MPE_EKON Econometrics ))
For elementary probability, mathematical statistics and introductory econometrics, passing the courses Introduction to Econometrics (BPE_INEC) or Modelling in R (MPE_MOIR) is highly recommended.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 7/24, only registered: 0/24, only registered with preference (fields directly associated with the programme): 0/24
fields of study / plans the course is directly associated with
Course objectives
The course is designed to give students experience in using basic and advanced econometric methods important in economics, finance and other business subjects. It provides skills in regression essential for understanding much of the literature on economics, finance, and empirical studies in other business areas. Topics of introductory econometrics will be expanded to a more advanced level. Advanced econometric topics include time-series econometrics and methods, panel data models, and models with limited dependent variable models.
We start with regression analysis with time-series data. Careful attention is given to interpreting regression results, hypothesis testing and diagnostic tests. Further topics in regression analysis are presented, including panel data models and limited dependent variables models. By the end of the course, students should be able to use regression models in many different applications and critically examine reported regression results in empirical research in economics and other business studies. They can identify and deal with several econometric problems in analysing time series, cross-sectional and panel data. They will have experience with a range of advanced econometric tools and techniques.
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 modelling, estimation, inference, and forecasting in the context of real 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. Basic Regression Analysis with Time Series Data (Trends and Seasonality)
  • 2. Further Issues in Using OLS with Time Series Data (Stationary and Weakly Dependent Time Series)
  • 3. Serial Correlation and Heteroskedasticity in Time Series Regressions (Robust Inference, Diagnostic Tests)
  • 4. Advanced Time Series Topics (testing for unit roots, cointegration, forecasting)
  • 5. Pooling Cross Sections Across Time: Simple Panel Data Methods
  • 6. Advanced Panel Data Methods (Fixed Effects Estimation, Random Effects Models, The Correlated Random Effect Approach, General Policy Analysis with Panel Data)
  • 6. Limited Dependent Variable Models and Sample Selection Corrections
    required literature
  • WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. Seventh edition. Boston: Cengage Learning, 2020, xxi, 826. ISBN 9781337558860. info
  • HEISS, Florian. Using R for introductory econometrics. 2nd edition. Düsseldorf: Florian Heiss, 2020, 368 stran. ISBN 9788648424364. info
    recommended literature
  • BALTAGI, Badi H. Econometric analysis of panel data. Sixth edition. Cham: Springer, 2021, xx, 424. ISBN 9783030539528. info
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • HILL, R. Carter, William E. GRIFFITHS and Guay C. LIM. Principles of econometrics. Fifth edition. Hoboken: Wiley Custom, 2018, xxvi, 878. ISBN 9781119510567. info
Teaching methods
tutorials, class discussion, computer labs practices, drills
Assessment methods
assignments in the seminar group, homework, written exam
Language of instruction
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2025.
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