ESF:BPE_CARA Time Series - Course Information
BPE_CARA Time SeriesFaculty of Economics and Administration
- Extent and Intensity
- 2/2/0. 10 credit(s). Type of Completion: zk (examination).
- Ing. Daniel Němec, Ph.D. (lecturer)
Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
Ing. Mgr. Vlastimil Reichel (lecturer)
Ing. Mgr. Vlastimil Reichel (seminar tutor)
Mgr. Jakub Chalmovianský (assistant)
- Guaranteed by
- 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
- Tue 10:00–11:50 P106
- Timetable of Seminar Groups:
BPE_CARA/02: Tue 14:00–15:50 VT206, D. Němec, V. Reichel
- 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 27 fields of study the course is directly associated with, display
- Course objectives
- The course is devoted to mathematical-statistical approaches to the analysis of economic processes described by time series.
The introductory part of the course is focused on univariate time series analysis using the Box-Jenkins methodology. The students will learn the procedures of identification of a suitable model of the time series, the criteria for the suitable model verification (including the statistics and tests based on model predictions), and the problems of seasonality. The second part of the course deals with the models with trend, unit root tests and univariate trend decomposition. The last section of the course will be devoted to multiequation time-series models.
All studied areas will place emphasis on the students' ability to use the gained knowledge in practice.
The main objective of the course is to provide the students with knowledge and skills, which are necessary for practical utilization of the time series analysis.
- Learning outcomes
- At the end of the course the students should be able:
- to analyze real data;
- to create a suitable model for the data;
- to construct future forecasts;
- to evaluate and interpret obtained model outcomes;
- to basically understand scientific texts from the field of time series econometrics.
- 1. Stationary time-series models (ARMA models, stationarity, ACF, PACF, Box-Jenkins model selection, forecasting, seasonality and structural changes).
- 2. Models with trend (deterministic and stochastic trends, unit root tests, univariate decomposition methods).
- 3. Multiequation time series models (intervention analysis, VAR models, impulse response function, structural VAR models, Blanchard-Quah decomposition).
- 4. Cointegration and error-correction models (cointegration and common trends, testing for cointegration, VEC models).
- required literature
- Applied econometric time series. Edited by Walter Enders. 4th ed. Hoboken: Wiley, 2015. x, 485. ISBN 9781118808566. info
- recommended literature
- CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008. 538 s. ISBN 9788086929439. info
- Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
- Teaching methods
- lectures, computer labs practices, class discussion, group semestral projects, oral exam
- Assessment methods
- The course consists of lectures and seminars. The course is concluded by the oral exam. Students can attend the exam if they fulfill these conditions: active attendance at the seminars, successful solution of the semestral projects (homeworks).
- Language of instruction
- 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.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/econ/spring2020/BPE_CARA