ESF:BPE_CARA Time Series - Course Information
BPE_CARA Time Series
Faculty of Economics and AdministrationSpring 2025
- Extent and Intensity
- 2/2/0. 8 credit(s). Type of Completion: zk (examination).
- Teacher(s)
- doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (lecturer)
Ing. Mgr. Vlastimil Reichel, Ph.D. (lecturer)
Ing. Mgr. Vlastimil Reichel, 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 - Prerequisites
- 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
- Economics (programme ESF, B-EKON) (3)
- Economics (programme ESF, B-EKT) (2)
- Economics (programme ESF, B-HE)
- Economics (programme ESF, B-KS)
- Economics (programme ESF, B-MA)
- Economics (programme ESF, B-MS)
- Economics (programme ESF, B-PL)
- Economics (programme ESF, B-PS)
- Economics (programme ESF, B-SO)
- Economics (programme ESF, B-SP)
- Economics (programme ESF, B-EKON) (2)
- Economic Policy (programme ESF, B-HE)
- Economic Policy (programme ESF, B-HOSP) (3)
- Economic Policy (programme ESF, B-HPS) (2)
- Economic Policy (programme ESF, B-KS)
- Economic Policy (programme ESF, B-MS)
- Economic Policy (programme ESF, B-PL)
- Economic Policy (programme ESF, B-PS)
- Economic Policy (programme ESF, B-SO)
- Economic Policy (programme ESF, B-SP)
- Modelling and Calculations (programme PřF, B-MA)
- 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. - Syllabus
- 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).
- Literature
- required literature
- ENDERS, Walter. Applied econometric time series. 4th ed. Hoboken: Wiley, 2015, x, 485. ISBN 9781118808566. info
- recommended literature
- HEISS, Florian. Using R for introductory econometrics. 2nd edition. Düsseldorf: Florian Heiss, 2020, 368 stran. ISBN 9788648424364. info
- CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
- not specified
- KRISPIN, Rami. Hands-on time series analysis with R : perform time series analysis and forecasting using R. First published. Birmingham: Packt, 2019, vi, 433. ISBN 9781788629157. info
- HEISS, Florian and Daniel BRUNNER. Using Python for introductory econometrics. 1st edition. Düsseldorf: Florian Heiss, 2020, 418 stran. ISBN 9788648436763. info
- 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). In the case of going abroad (Erasmus), it is not mandatory to fulfill the condition of active participation in exercises. The remaining requirements remain unchanged.
- Language of instruction
- Czech
- Follow-Up Courses
- Further comments (probably available only in Czech)
- The course is taught annually.
The course is taught: every week.
General note: Přednášky jsou dostupné online a ze záznamu.
- Enrolment Statistics (Spring 2025, recent)
- Permalink: https://is.muni.cz/course/econ/spring2025/BPE_CARA