MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2025
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (lecturer)
Mgr. Jakub Chalmovianský, Ph.D. (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
Prerequisites
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(!NOWANY( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
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 10 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
Learning outcomes
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • HANSEN, Bruce E. Econometrics. Princeton, New Jersey: Princeton University Press, 2022, xxxi, 1044. ISBN 9780691235899. info
    recommended literature
  • BALTAGI, Badi H. Econometric analysis of panel data. Sixth edition. Cham: Springer, 2021, xx, 424. ISBN 9783030539528. info
  • GREENE, William H. Econometric analysis. Eighth edition. Harlow, England: Pearson, 2020, 1166 stran. ISBN 9781292231136. 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, class discussion, computer labs practices, drills
Assessment methods
homeworks assignments and seminar activity (50% of the final grade), oral exam (50 % of the final grade); details of the course completion for students going abroad are contained in the Organisational guidelines (see study materials in IS)
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.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2024
Extent and Intensity
2/2/0. 12 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)
Mgr. Jakub Chalmovianský, Ph.D. (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
Wed 10:00–11:50 P106, except Wed 3. 4.
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:00–15:50 VT204, except Wed 3. 4., D. Němec
MPE_EKON/02: Wed 12:00–13:50 VT204, except Wed 3. 4., J. Chalmovianský
Prerequisites
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(!NOWANY( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
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 10 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
Learning outcomes
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • HANSEN, Bruce E. Econometrics. Princeton, New Jersey: Princeton University Press, 2022, xxxi, 1044. ISBN 9780691235899. info
    recommended literature
  • BALTAGI, Badi H. Econometric analysis of panel data. Sixth edition. Cham: Springer, 2021, xx, 424. ISBN 9783030539528. info
  • GREENE, William H. Econometric analysis. Eighth edition. Harlow, England: Pearson, 2020, 1166 stran. ISBN 9781292231136. 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, class discussion, computer labs practices, drills
Assessment methods
homeworks assignments and seminar activity (50% of the final grade), oral exam (50 % of the final grade); details of the course completion for students going abroad are contained in the Organisational guidelines (see study materials in IS)
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.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2023
Extent and Intensity
2/2/0. 12 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)
Mgr. Jakub Chalmovianský, Ph.D. (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
Wed 10:00–11:50 P106, except Wed 29. 3.
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:00–15:50 VT204, except Wed 29. 3., D. Němec
MPE_EKON/02: Wed 12:00–13:50 VT204, except Wed 29. 3., J. Chalmovianský
Prerequisites
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(!NOWANY( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
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 10 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
Learning outcomes
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
    recommended literature
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008, xiii, 351. ISBN 9780470518861. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012, 1228 s. ISBN 9780273753568. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
semestral projects, oral exam
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.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2022
Extent and Intensity
2/2/0. 12 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)
Mgr. Jakub Chalmovianský, Ph.D. (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
Wed 10:00–11:50 P106, except Wed 30. 3.
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:00–15:50 VT204, except Wed 30. 3., D. Němec
MPE_EKON/02: Wed 12:00–13:50 VT204, except Wed 30. 3., J. Chalmovianský
Prerequisites
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(!NOWANY( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
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 12 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
Learning outcomes
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
    recommended literature
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008, xiii, 351. ISBN 9780470518861. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012, 1228 s. ISBN 9780273753568. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
semestral projects, oral exam
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.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2021
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (lecturer)
Mgr. Jakub Chalmovianský, Ph.D. (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
Wed 10:00–11:50 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:00–15:50 VT204, D. Němec
MPE_EKON/02: Wed 12:00–13:50 VT204, J. Chalmovianský
Prerequisites
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(!NOWANY( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
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 12 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
Learning outcomes
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
    recommended literature
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008, xiii, 351. ISBN 9780470518861. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012, 1228 s. ISBN 9780273753568. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
semestral projects, oral exam
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.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2020
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (lecturer)
Mgr. Jakub Chalmovianský, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (assistant)
Guaranteed by
prof. Ing. Osvald Vašíček, CSc.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Wed 10:00–11:50 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:00–15:50 VT204, J. Chalmovianský, D. Němec
MPE_EKON/02: Wed 12:00–13:50 VT204, J. Chalmovianský, D. Němec
Prerequisites
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(!NOWANY( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
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 12 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
Learning outcomes
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
    recommended literature
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008, xiii, 351. ISBN 9780470518861. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012, 1228 s. ISBN 9780273753568. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
semestral projects, oral exam
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.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2019
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Bechný, 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
Wed 10:00–11:50 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:00–15:50 VT204, D. Němec
MPE_EKON/02: Wed 12:00–13:50 VT204, D. Němec
Prerequisites
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(!NOWANY( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
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 7 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • ENDERS, Walter. Applied econometric time series. 4th ed. Hoboken: Wiley, 2015, x, 485. ISBN 9781118808566. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012, 1228 s. ISBN 9780273753568. info
  • BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008, xiii, 351. ISBN 9780470518861. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2018
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Bechný, 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
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
MPE_EKON/T01: Mon 19. 2. to Wed 23. 5. Mon 16:30–18:05 115, J. Bechný, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
MPE_EKON/01: Wed 14:35–16:15 VT204, D. Němec
MPE_EKON/02: Wed 18:00–19:35 VT204, D. Němec
Prerequisites
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(!NOWANY( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
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 7 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • ENDERS, Walter. Applied econometric time series. 4th ed. Hoboken: Wiley, 2015, x, 485. ISBN 9781118808566. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012, 1228 s. ISBN 9780273753568. info
  • BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008, xiii, 351. ISBN 9780470518861. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2017
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, 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
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:35–16:15 VT204, D. Němec
MPE_EKON/02: Wed 18:00–19:35 VT204, D. Němec
Prerequisites
(! MPE_ECNM Econometrics )&&(! MPE_AECM Econometrics )&&(!NOWANY( MPE_ECNM Econometrics , MPE_AECM Econometrics ))
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 7 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • ENDERS, Walter. Applied econometric time series. 4th ed. Hoboken: Wiley, 2015, x, 485. ISBN 9781118808566. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012, 1228 s. ISBN 9780273753568. info
  • BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008, xiii, 351. ISBN 9780470518861. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2016
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Ing. Mgr. Jakub Buček (assistant)
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
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:35–16:15 VT204, D. Němec
MPE_EKON/02: Wed 18:00–19:35 VT204, 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 7 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • ENDERS, Walter. Applied econometric time series. 4th ed. Hoboken: Wiley, 2015, x, 485. ISBN 9781118808566. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012, 1228 s. ISBN 9780273753568. info
  • BALTAGI, Badi H. Econometric analysis of panel data. 4th ed. Chichester: John Wiley & Sons, 2008, xiii, 351. ISBN 9780470518861. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2015
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Bc. Karolína Zábojníková (assistant)
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
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 14:35–16:15 VT204, D. Němec
MPE_EKON/02: No timetable has been entered into IS. D. Němec
Prerequisites
! PMTEII Theory of Econometrics
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 7 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 3rd ed. Hoboken: Wiley, 2010, xiv, 516. ISBN 9780470505397. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • GREENE, William H. Econometric analysis. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008, xxxvii, 11. ISBN 9780135132456. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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 PMTEII.
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
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2014
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (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
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 15:30–17:05 VT204, D. Němec
MPE_EKON/02: Wed 18:00–19:35 VT204, D. Němec
Prerequisites
! PMTEII Theory of Econometrics
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 7 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 3rd ed. Hoboken: Wiley, 2010, xiv, 516. ISBN 9780470505397. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • GREENE, William H. Econometric analysis. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008, xxxvii, 11. ISBN 9780135132456. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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 PMTEII.
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
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2013
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (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
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 15:30–17:05 VT204, D. Němec
Prerequisites
! PMTEII Theory of Econometrics
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 7 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 3rd ed. Hoboken: Wiley, 2010, xiv, 516. ISBN 9780470505397. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • GREENE, William H. Econometric analysis. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008, xxxvii, 11. ISBN 9780135132456. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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 PMTEII.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2012
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (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
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 15:30–17:05 VT206, D. Němec
MPE_EKON/02: Thu 14:35–16:15 VT105, D. Němec
Prerequisites
! PMTEII Theory of Econometrics
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 6 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 3rd ed. Hoboken: Wiley, 2010, xiv, 516. ISBN 9780470505397. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • GREENE, William H. Econometric analysis. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008, xxxvii, 11. ISBN 9780135132456. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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 PMTEII.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2011
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Timetable
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 15:30–17:05 VT206, D. Němec
MPE_EKON/02: Thu 14:35–16:15 VT105, D. Němec
MPE_EKON/03: Thu 16:20–17:55 VT105, D. Němec
Prerequisites
! PMTEII Theory of Econometrics
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 6 fields of study the course is directly associated with, display
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, GMM etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • HEIJ, Christiaan. Econometric methods with applications in business and economics. 1st ed. Oxford: Oxford University Press, 2004, xxv, 787. ISBN 9780199268016. info
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 3rd ed. Hoboken: Wiley, 2010, xiv, 516. ISBN 9780470505397. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • GREENE, William H. Econometric analysis. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008, xxxvii, 11. ISBN 9780135132456. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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 PMTEII.
Listed among pre-requisites of other courses
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.

MPE_EKON Econometrics

Faculty of Economics and Administration
Spring 2010
Extent and Intensity
2/2/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
RNDr. Dalibor Moravanský, CSc. (seminar tutor)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Timetable
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
MPE_EKON/01: Wed 15:30–17:05 VT206, D. Němec
MPE_EKON/02: Thu 14:35–16:15 VT105, D. Moravanský
MPE_EKON/03: Thu 16:20–17:55 VT105, D. Moravanský
Prerequisites
! PMTEII Theory of Econometrics
basic matrix algebra, elementary probability and mathematical statistics
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
Course objectives
Topics of introductory econometrics (covered in "Introduction to Econometrics") will be reviewed and expanded into more advanced level, in terms of both the econometric theory and the level of complexity of the models. Advanced econometric topics include instrumental variable estimations, maximum likelihood estimation, 2SLS, 3SLS, GMM, LIML, FIML etc.
The course is designed to provide students with a working knowledge of basic and advanced econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use econometric methods and tools.
They have a foundation and understanding for further study of econometric theory.
Syllabus
  • 1. Introduction to linear regression model – normal linear regression model, least squares method, testing of hypothesis;
  • 2. Heteroskedascity and autocorrelation – causes, consequences, testing, solution;
  • 3. Other estimation tools and techniques – method of instrumental variables, GMM, maximal likelihood (principles and examples of use), tests of specifications;
  • 4. Panel data models – basic principles and variations, estimation methods
  • 5. Discrete choice models – probit, logit, tobit models and their alternatives (principles, use and interpretation of results of estimation);
  • 6. Univariate time series models – ARMA processes, unit root tests, cointegration of time series and error-correction models, ARCH and GARCH models of volatility;
  • 7. Simultaneous equations models - structural and reduced form, 2SLS, 3SLS, LIML, FIML;
  • 8. Multivariate time series models – VAR models, VECM models (principles and examples of use);
  • 9. State space models - Kalman filter and maximal likelihood estimation;
Literature
    required literature
  • Heij, De Boer, Franses, Kloek, and Van Dijk: Econometric Methods with Applications in Business and Economics. Oxford University Press, 2004.
    recommended literature
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
  • HAYASHI, Fumio. Econometrics. Princeton: Princeton University Press, 2000, xxiii, 683. ISBN 0691010188. info
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
  • GREENE, William H. Econometric analysis. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008, xxxvii, 11. ISBN 9780135132456. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
    not specified
  • VERBEEK, Marno. A guide to modern econometrics. 2nd ed. Chichester: John Wiley & Sons, 2004, xv, 429. ISBN 0470857730. info
  • GUJARATI, Damodar N. and Dawn C. PORTER. Basic econometrics. 5th ed. Boston: McGraw-Hill, 2009, xx, 922. ISBN 9780071276252. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final project, written and oral exam
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 PMTEII.
Listed among pre-requisites of other courses
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.