BKE_CARA Time Series

Faculty of Economics and Administration
Spring 2025
Extent and Intensity
26/0/0. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching
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
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
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.
The course is taught: in blocks.
Note related to how often the course is taught: 12 hodin.
General note: Přednášky jsou dostupné online a ze záznamu.
Information on the extent and intensity of the course: tutorial 12 hodin.
The course is also listed under the following terms Spring 2023, Spring 2024.
  • Enrolment Statistics (recent)
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