BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2024
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Taught in person.
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
Timetable
Tue 10:00–11:50 P106, except Tue 2. 4.
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:00–13:50 VT204, except Tue 2. 4., V. Reichel
BPE_CARA/02: Tue 14:00–15:50 VT206, except Tue 2. 4., D. Němec
BPE_CARA/03: Tue 16:00–17:50 VT206, except Tue 2. 4., D. Němec
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
there are 26 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.
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)
Study Materials
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2025
Extent and Intensity
2/2/0. 8 credit(s). Type of Completion: zk (examination).
Taught in person.
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
there are 28 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.
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.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2023
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (lecturer)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (assistant)
Ing. Jakub Moučka (assistant)
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
Tue 10:00–11:50 P106, except Tue 28. 3.
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:00–13:50 VT204, except Tue 28. 3., V. Reichel
BPE_CARA/02: Tue 14:00–15:50 VT206, except Tue 28. 3., D. Němec
BPE_CARA/03: Tue 16:00–17:50 VT206, except Tue 28. 3., D. Němec
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
there are 26 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.
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
  • 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
Czech
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.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2022
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (lecturer)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (assistant)
Ing. Jakub Moučka (assistant)
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
Tue 10:00–11:50 P106, except Tue 29. 3.
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:00–13:50 VT204, except Tue 29. 3., V. Reichel
BPE_CARA/02: Tue 14:00–15:50 VT206, except Tue 29. 3., D. Němec
BPE_CARA/03: Tue 16:00–17:50 VT206, except Tue 29. 3., D. Němec
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
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.
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
  • 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
Czech
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.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2021
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Taught online.
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
Ing. Mgr. Vlastimil Reichel, Ph.D. (lecturer)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (assistant)
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
Tue 10:00–11:50 P106
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:00–13:50 VT204, D. Němec, V. Reichel
BPE_CARA/02: Tue 14:00–15:50 VT206, D. Němec, V. Reichel
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
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.
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
  • 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
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2020
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
Ing. Mgr. Vlastimil Reichel, Ph.D. (lecturer)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (assistant)
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
Tue 10:00–11:50 P106
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:00–13:50 VT204, D. Němec, V. Reichel
BPE_CARA/02: Tue 14:00–15:50 VT206, D. Němec, V. Reichel
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
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.
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
  • 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
Czech
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.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2019
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (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
Timetable
Tue 10:00–11:50 P106
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:00–13:50 VT204, D. Němec, V. Reichel
BPE_CARA/02: Tue 14:00–15:50 VT206, D. Němec, V. Reichel
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 21 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1.Decomposition approach to the time series analysis: time series and its components: trend, eventual seasonality or cycles, and stochastic component. Trend models based on the modifications of the linear regression model: recognition and estimation of its parameters. Special methods designed for non-linearized trend forms.
  • 2.Moving averages and their exploitations in the process of the determination of the trend and/or seasonality. Their building at the local adjustment with the polynomial curves. Exponential smoothing(Brown),Holt's and Winters' adjustment method.
  • 3.Modelling of the one-dimensioned time series: autocorrelation properties of the time series, the basic models based on the Box-Jenkins methodology(AR,MA a ARMA modely),identification and diagnostics of the model(choice of rank od the model, tests of stability). ARIMA-models and some of their generalized forms.
  • 4.Forms of the possible non-stationarity of the time series, and approaches leading to the stationary state of the series. Random walk model. Unit-root tests (Dickey-Fuller´s test and others) indicating the non-stationary character of the time series. Autoregressive model of the distributed lags.
  • 5.Modelling of the volatility. Autoregressive models with conditioned heteroskedasticity: ARCH models,GARCH models and modifications. Models non-linear in the mean. Applications focusing on the financial time-series analysis.
  • 6.Modelling of the multi-dimensional time series: the principles and estimation methods. Vector autoregression. Testing of dependencies among variables: Granger non-causality. Impuls response. Cointegration among several time series, ECM(error correction models).
Literature
    required literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
Teaching methods
lectures, computer labs practices, class discussion, homework, group projects
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, and successful solution of the semestral projects (homeworks).
Language of instruction
Czech
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.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2018
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
Ing. Mgr. Jakub Buček (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (assistant)
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
Tue 11:05–12:45 P106
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:50–14:30 VT204, D. Němec
BPE_CARA/02: Tue 14:35–16:15 VT206, D. Němec
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 21 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1.Decomposition approach to the time series analysis: time series and its components: trend, eventual seasonality or cycles, and stochastic component. Trend models based on the modifications of the linear regression model: recognition and estimation of its parameters. Special methods designed for non-linearized trend forms.
  • 2.Moving averages and their exploitations in the process of the determination of the trend and/or seasonality. Their building at the local adjustment with the polynomial curves. Exponential smoothing(Brown),Holt's and Winters' adjustment method.
  • 3.Modelling of the one-dimensioned time series: autocorrelation properties of the time series, the basic models based on the Box-Jenkins methodology(AR,MA a ARMA modely),identification and diagnostics of the model(choice of rank od the model, tests of stability). ARIMA-models and some of their generalized forms.
  • 4.Forms of the possible non-stationarity of the time series, and approaches leading to the stationary state of the series. Random walk model. Unit-root tests (Dickey-Fuller´s test and others) indicating the non-stationary character of the time series. Autoregressive model of the distributed lags.
  • 5.Modelling of the volatility. Autoregressive models with conditioned heteroskedasticity: ARCH models,GARCH models and modifications. Models non-linear in the mean. Applications focusing on the financial time-series analysis.
  • 6.Modelling of the multi-dimensional time series: the principles and estimation methods. Vector autoregression. Testing of dependencies among variables: Granger non-causality. Impuls response. Cointegration among several time series, ECM(error correction models).
Literature
    required literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
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, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
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.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2017
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
Ing. Mgr. Jakub Buček (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
Timetable
Tue 11:05–12:45 P106
  • Timetable of Seminar Groups:
BPE_CARA/T01: Thu 23. 2. to Mon 22. 5. Thu 13:00–14:35 115, J. Buček, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
BPE_CARA/01: Tue 12:50–14:30 VT204, D. Němec
BPE_CARA/02: Tue 14:35–16:15 VT204, D. Němec
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 21 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1.Decomposition approach to the time series analysis: time series and its components: trend, eventual seasonality or cycles, and stochastic component. Trend models based on the modifications of the linear regression model: recognition and estimation of its parameters. Special methods designed for non-linearized trend forms.
  • 2.Moving averages and their exploitations in the process of the determination of the trend and/or seasonality. Their building at the local adjustment with the polynomial curves. Exponential smoothing(Brown),Holt's and Winters' adjustment method.
  • 3.Modelling of the one-dimensioned time series: autocorrelation properties of the time series, the basic models based on the Box-Jenkins methodology(AR,MA a ARMA modely),identification and diagnostics of the model(choice of rank od the model, tests of stability). ARIMA-models and some of their generalized forms.
  • 4.Forms of the possible non-stationarity of the time series, and approaches leading to the stationary state of the series. Random walk model. Unit-root tests (Dickey-Fuller´s test and others) indicating the non-stationary character of the time series. Autoregressive model of the distributed lags.
  • 5.Modelling of the volatility. Autoregressive models with conditioned heteroskedasticity: ARCH models,GARCH models and modifications. Models non-linear in the mean. Applications focusing on the financial time-series analysis.
  • 6.Modelling of the multi-dimensional time series: the principles and estimation methods. Vector autoregression. Testing of dependencies among variables: Granger non-causality. Impuls response. Cointegration among several time series, ECM(error correction models).
Literature
    required literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
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, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2016
Extent and Intensity
2/2/0. 13 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
Ing. Mgr. Jakub Buček (seminar tutor)
Ing. Michal Chribik (seminar tutor)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Tue 11:05–12:45 P106
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:50–14:30 VT204, D. Němec
BPE_CARA/02: Tue 14:35–16:15 VT204, M. Chribik
BPE_CARA/03: Tue 16:20–17:55 VT203, J. Buček
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 21 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1.Decomposition approach to the time series analysis: time series and its components: trend, eventual seasonality or cycles, and stochastic component. Trend models based on the modifications of the linear regression model: recognition and estimation of its parameters. Special methods designed for non-linearized trend forms.
  • 2.Moving averages and their exploitations in the process of the determination of the trend and/or seasonality. Their building at the local adjustment with the polynomial curves. Exponential smoothing(Brown),Holt's and Winters' adjustment method.
  • 3.Modelling of the one-dimensioned time series: autocorrelation properties of the time series, the basic models based on the Box-Jenkins methodology(AR,MA a ARMA modely),identification and diagnostics of the model(choice of rank od the model, tests of stability). ARIMA-models and some of their generalized forms.
  • 4.Forms of the possible non-stationarity of the time series, and approaches leading to the stationary state of the series. Random walk model. Unit-root tests (Dickey-Fuller´s test and others) indicating the non-stationary character of the time series. Autoregressive model of the distributed lags.
  • 5.Modelling of the volatility. Autoregressive models with conditioned heteroskedasticity: ARCH models,GARCH models and modifications. Models non-linear in the mean. Applications focusing on the financial time-series analysis.
  • 6.Modelling of the multi-dimensional time series: the principles and estimation methods. Vector autoregression. Testing of dependencies among variables: Granger non-causality. Impuls response. Cointegration among several time series, ECM(error correction models).
Literature
    required literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
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, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
Information about innovation of course.
This course has been innovated under the project "Inovace studia ekonomických disciplín v souladu s požadavky znalostní ekonomiky (CZ.1.07/2.2.00/28.0227)" which is cofinanced by the European Social Fond and the national budget of the Czech Republic.

logo image
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2015
Extent and Intensity
2/2/0. 13 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
Mgr. Hana Fitzová, Ph.D. (lecturer)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Tue 11:05–12:45 P106
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:50–14:30 VT204, D. Němec
BPE_CARA/02: Tue 14:35–16:15 VT204, D. Němec
BPE_CARA/03: No timetable has been entered into IS.
Prerequisites (in Czech)
! PMEM2A Math Methods in Economics II
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 330 student(s).
Current registration and enrolment status: enrolled: 0/330, only registered: 0/330, only registered with preference (fields directly associated with the programme): 0/330
fields of study / plans the course is directly associated with
there are 21 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1.Decomposition approach to the time series analysis: time series and its components: trend, eventual seasonality or cycles, and stochastic component. Trend models based on the modifications of the linear regression model: recognition and estimation of its parameters. Special methods designed for non-linearized trend forms.
  • 2.Moving averages and their exploitations in the process of the determination of the trend and/or seasonality. Their building at the local adjustment with the polynomial curves. Exponential smoothing(Brown),Holt's and Winters' adjustment method.
  • 3.Modelling of the one-dimensioned time series: autocorrelation properties of the time series, the basic models based on the Box-Jenkins methodology(AR,MA a ARMA modely),identification and diagnostics of the model(choice of rank od the model, tests of stability). ARIMA-models and some of their generalized forms.
  • 4.Forms of the possible non-stationarity of the time series, and approaches leading to the stationary state of the series. Random walk model. Unit-root tests (Dickey-Fuller´s test and others) indicating the non-stationary character of the time series. Autoregressive model of the distributed lags.
  • 5.Modelling of the volatility. Autoregressive models with conditioned heteroskedasticity: ARCH models,GARCH models and modifications. Models non-linear in the mean. Applications focusing on the financial time-series analysis.
  • 6.Modelling of the multi-dimensional time series: the principles and estimation methods. Vector autoregression. Testing of dependencies among variables: Granger non-causality. Impuls response. Cointegration among several time series, ECM(error correction models).
Literature
    required literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
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, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMEM2A.
Information on course enrolment limitations: max. 30 cizích studentů; cvičení pouze pro studenty ESF
Information about innovation of course.
This course has been innovated under the project "Inovace studia ekonomických disciplín v souladu s požadavky znalostní ekonomiky (CZ.1.07/2.2.00/28.0227)" which is cofinanced by the European Social Fond and the national budget of the Czech Republic.

logo image
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2014
Extent and Intensity
2/2/0. 13 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
Mgr. Hana Fitzová, Ph.D. (lecturer)
RNDr. Dalibor Moravanský, CSc. (lecturer)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Tue 11:05–12:45 P106
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:50–14:30 VT204, D. Němec
BPE_CARA/02: Tue 14:35–16:15 VT204, D. Němec
BPE_CARA/03: Thu 16:20–17:55 VT105
Prerequisites (in Czech)
! PMEM2A Math Methods in Economics II
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 330 student(s).
Current registration and enrolment status: enrolled: 0/330, only registered: 0/330, only registered with preference (fields directly associated with the programme): 0/330
fields of study / plans the course is directly associated with
there are 12 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1.Decomposition approach to the time series analysis: time series and its components: trend, eventual seasonality or cycles, and stochastic component. Trend models based on the modifications of the linear regression model: recognition and estimation of its parameters. Special methods designed for non-linearized trend forms.
  • 2.Moving averages and their exploitations in the process of the determination of the trend and/or seasonality. Their building at the local adjustment with the polynomial curves. Exponential smoothing(Brown),Holt's and Winters' adjustment method.
  • 3.Modelling of the one-dimensioned time series: autocorrelation properties of the time series, the basic models based on the Box-Jenkins methodology(AR,MA a ARMA modely),identification and diagnostics of the model(choice of rank od the model, tests of stability). ARIMA-models and some of their generalized forms.
  • 4.Forms of the possible non-stationarity of the time series, and approaches leading to the stationary state of the series. Random walk model. Unit-root tests (Dickey-Fuller´s test and others) indicating the non-stationary character of the time series. Autoregressive model of the distributed lags.
  • 5.Modelling of the volatility. Autoregressive models with conditioned heteroskedasticity: ARCH models,GARCH models and modifications. Models non-linear in the mean. Applications focusing on the financial time-series analysis.
  • 6.Modelling of the multi-dimensional time series: the principles and estimation methods. Vector autoregression. Testing of dependencies among variables: Granger non-causality. Impuls response. Cointegration among several time series, ECM(error correction models).
Literature
    required literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
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, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMEM2A.
Information on course enrolment limitations: max. 30 cizích studentů; cvičení pouze pro studenty ESF
Information about innovation of course.
This course has been innovated under the project "Inovace studia ekonomických disciplín v souladu s požadavky znalostní ekonomiky (CZ.1.07/2.2.00/28.0227)" which is cofinanced by the European Social Fond and the national budget of the Czech Republic.

logo image
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2013
Extent and Intensity
2/2/0. 13 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Hana Fitzová, Ph.D. (lecturer)
Mgr. Hana Fitzová, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
RNDr. Dalibor Moravanský, CSc. (lecturer)
RNDr. Dalibor Moravanský, CSc. (seminar tutor)
Guaranteed by
Mgr. Hana Fitzová, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Tue 16:20–17:55 P104
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:50–14:30 VT204, D. Moravanský
BPE_CARA/02: Tue 14:35–16:15 VT204, D. Moravanský
BPE_CARA/03: Thu 16:20–17:55 VT105, D. Moravanský
Prerequisites (in Czech)
! PMEM2A Math Methods in Economics II
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 330 student(s).
Current registration and enrolment status: enrolled: 0/330, only registered: 0/330, only registered with preference (fields directly associated with the programme): 0/330
fields of study / plans the course is directly associated with
there are 12 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1.Decomposition approach to the time series analysis: time series and its components: trend, eventual seasonality or cycles, and stochastic component. Trend models based on the modifications of the linear regression model: recognition and estimation of its parameters. Special methods designed for non-linearized trend forms.
  • 2.Moving averages and their exploitations in the process of the determination of the trend and/or seasonality. Their building at the local adjustment with the polynomial curves. Exponential smoothing(Brown),Holt's and Winters' adjustment method.
  • 3.Modelling of the one-dimensioned time series: autocorrelation properties of the time series, the basic models based on the Box-Jenkins methodology(AR,MA a ARMA modely),identification and diagnostics of the model(choice of rank od the model, tests of stability). ARIMA-models and some of their generalized forms.
  • 4.Forms of the possible non-stationarity of the time series, and approaches leading to the stationary state of the series. Random walk model. Unit-root tests (Dickey-Fuller´s test and others) indicating the non-stationary character of the time series. Autoregressive model of the distributed lags.
  • 5.Modelling of the volatility. Autoregressive models with conditioned heteroskedasticity: ARCH models,GARCH models and modifications. Models non-linear in the mean. Applications focusing on the financial time-series analysis.
  • 6.Modelling of the multi-dimensional time series: the principles and estimation methods. Vector autoregression. Testing of dependencies among variables: Granger non-causality. Impuls response. Cointegration among several time series, ECM(error correction models).
Literature
    required literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
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, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMEM2A.
Information on course enrolment limitations: max. 30 cizích studentů; cvičení pouze pro studenty ESF
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2012
Extent and Intensity
2/2/0. 13 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Hana Fitzová, Ph.D. (lecturer)
Mgr. Hana Fitzová, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
RNDr. Dalibor Moravanský, CSc. (lecturer)
RNDr. Dalibor Moravanský, CSc. (seminar tutor)
Guaranteed by
Mgr. Hana Fitzová, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Tue 16:20–17:55 P104
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:50–14:30 VT206, D. Moravanský
BPE_CARA/02: Tue 14:35–16:15 VT206, D. Moravanský
BPE_CARA/03: Thu 16:20–17:55 VT105, D. Moravanský
Prerequisites (in Czech)
! PMEM2A Math Methods in Economics II
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 330 student(s).
Current registration and enrolment status: enrolled: 0/330, only registered: 0/330, only registered with preference (fields directly associated with the programme): 0/330
fields of study / plans the course is directly associated with
there are 11 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1.Decomposition approach to the time series analysis: time series and its components: trend, eventual seasonality or cycles, and stochastic component. Trend models based on the modifications of the linear regression model: recognition and estimation of its parameters. Special methods designed for non-linearized trend forms.
  • 2.Moving averages and their exploitations in the process of the determination of the trend and/or seasonality. Their building at the local adjustment with the polynomial curves. Exponential smoothing(Brown),Holt's and Winters' adjustment method.
  • 3.Modelling of the one-dimensioned time series: autocorrelation properties of the time series, the basic models based on the Box-Jenkins methodology(AR,MA a ARMA modely),identification and diagnostics of the model(choice of rank od the model, tests of stability). ARIMA-models and some of their generalized forms.
  • 4.Forms of the possible non-stationarity of the time series, and approaches leading to the stationary state of the series. Random walk model. Unit-root tests (Dickey-Fuller´s test and others) indicating the non-stationary character of the time series. Autoregressive model of the distributed lags.
  • 5.Modelling of the volatility. Autoregressive models with conditioned heteroskedasticity: ARCH models,GARCH models and modifications. Models non-linear in the mean. Applications focusing on the financial time-series analysis.
  • 6.Modelling of the multi-dimensional time series: the principles and estimation methods. Vector autoregression. Testing of dependencies among variables: Granger non-causality. Impuls response. Cointegration among several time series, ECM(error correction models).
Literature
    required literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
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, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMEM2A.
Information on course enrolment limitations: max. 30 cizích studentů; cvičení pouze pro studenty ESF
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2011
Extent and Intensity
2/2/0. 13 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Hana Fitzová, Ph.D. (lecturer)
Mgr. Hana Fitzová, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
RNDr. Dalibor Moravanský, CSc. (lecturer)
RNDr. Dalibor Moravanský, CSc. (seminar tutor)
Guaranteed by
Mgr. Hana Fitzová, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Timetable
Tue 16:20–17:55 P104
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 12:50–14:30 VT206, D. Moravanský
BPE_CARA/02: Tue 14:35–16:15 VT206, D. Moravanský
Prerequisites (in Czech)
! PMEM2A Math Methods in Economics II
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 330 student(s).
Current registration and enrolment status: enrolled: 0/330, only registered: 0/330, only registered with preference (fields directly associated with the programme): 0/330
fields of study / plans the course is directly associated with
there are 10 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1. Decomposition approach to the time series analysis: time series, model of linear regression (summary of the required knowledge), trend in the time series, moving average, exponential adjustment, analysis of the seasonality.
  • 2. Modelling of one-dimensional time series: autocorrelation properties of the time series, basic models of the Box-Jenkins methodology (AR, MA and ARMA models), identification and diagnostics of the model (selection of the order of the model, stability tests), ARIMA models.
  • 3. Autoregression models with conditional heteroskedasticity: volatility modelling, ARCH models, GARCH models.
  • 4. Modelling of the multidimensional time series: principle and methods of the estimation, impulse responses, Granger causality, cointegration in the time series, error correction models.
  • 5. Business cycle analysis: selected problems of filtration e. g. Hodrick-Prescott filter, Band pass filter; Blanchard-Quah decomposition.
Literature
    required literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
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, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMEM2A.
Information on course enrolment limitations: max. 30 cizích studentů; cvičení pouze pro studenty ESF
The course is also listed under the following terms Spring 2010, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2010
Extent and Intensity
2/2/0. 13 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Hana Fitzová, Ph.D. (lecturer)
Mgr. Hana Fitzová, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
RNDr. Dalibor Moravanský, CSc. (lecturer)
Guaranteed by
Mgr. Hana Fitzová, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Timetable
Tue 12:50–14:30 VT206
  • Timetable of Seminar Groups:
BPE_CARA/01: Tue 14:35–16:15 VT206, D. Moravanský
Prerequisites (in Czech)
(( BPM_STA2 Statistics 2 || PMSTII Statistics II ) &&( BPE_ZAEK Introduction to Econometrics ))&&(! PMEM2A Math Methods in Economics II )
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 330 student(s).
Current registration and enrolment status: enrolled: 0/330, only registered: 0/330, only registered with preference (fields directly associated with the programme): 0/330
fields of study / plans the course is directly associated with
there are 12 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 the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
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. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1. Decomposition approach to the time series analysis: time series, model of linear regression (summary of the required knowledge), trend in the time series, moving average, exponential adjustment, analysis of the seasonality.
  • 2. Modelling of one-dimensional time series: autocorrelation properties of the time series, basic models of the Box-Jenkins methodology (AR, MA and ARMA models), identification and diagnostics of the model (selection of the order of the model, stability tests), ARIMA models.
  • 3. Autoregression models with conditional heteroskedasticity: volatility modelling, ARCH models, GARCH models.
  • 4. Modelling of the multidimensional time series: principle and methods of the estimation, impulse responses, Granger causality, cointegration in the time series, error correction models.
  • 5. Business cycle analysis: selected problems of filtration e. g. Hodrick-Prescott filter, Band pass filter; Blanchard-Quah decomposition.
Literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
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, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMEM2A.
Information on course enrolment limitations: max. 30 cizích studentů; cvičení pouze pro studenty ESF
The course is also listed under the following terms Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (recent)