ESF:BKE_CARA Time Series - Course Information
BKE_CARA Time Series
Faculty of Economics and AdministrationSpring 2024
- Extent and Intensity
- 26/0/0. 6 credit(s). Type of Completion: zk (examination).
- Teacher(s)
- doc. Ing. Daniel Němec, Ph.D. (lecturer)
- 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
- Sat 16. 3. 8:00–11:50 P302a, Sat 13. 4. 12:00–15:50 P302a, Sat 4. 5. 12:00–15:50 P302b
- Prerequisites
- FORMA ( K )
basic matrix algebra, elementary probability and mathematical statistics, passing the course Introduction to econometrics (BKE_ZAEK) (recommended) - Course Enrolment Limitations
- The course is only offered to the students of the study fields the course is directly associated with.
- fields of study / plans the course is directly associated with
- Business Analytics (programme ESF, B-BA)
- 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.
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 basic 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).
- Literature
- required literature
- ENDERS, Walter. Applied econometric time series. 4th ed. Hoboken: Wiley, 2015, x, 485. ISBN 9781118808566. info
- recommended literature
- BROOKS, Chris. Introductory econometrics for finance. Fourth edition. Cambridge: Cambridge University Press, 2019, xxxi, 696. ISBN 9781108422536. info
- 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. Using R for introductory econometrics. 2nd edition. Düsseldorf: Florian Heiss, 2020, 368 stran. ISBN 9788648424364. 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 (including empirical illustrations). The course is concluded by the oral exam. Students can attend the exam if they fulfill the condition of successful solution of the semestral projects (homeworks).
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
Information on the extent and intensity of the course: tutorial 12 hodin.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/econ/spring2024/BKE_CARA