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Course info
KMA / MME-A
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Course description
Department/Unit / Abbreviation
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KMA
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MME-A
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Mathematical Models in Econometrics
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
5
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
2
[Hours/Week]
Tutorial
1
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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English
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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0 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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1
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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English
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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KMA/MME
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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KMA/SZMM
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The aim of this course is to introduce with usage different mathematical methods and principles in economic and econometric models. This course is lectured in English, its subject is equivalent to KMA/MME.
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Requirements on student
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During semester, students have to hand at least 2/3 of the engaded exercises engaded and defend the exercises during the presentation
The final examination: Demonstrate knowledge of the definitions, fundamental theorems. Use rigorous arguments in calculus and ability to apply them in solving problems on the topics in the syllabus. Detailed information may be found on the server http://home.zcu.cz/~sediva
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Content
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1. Classical regression linear model with econometric applications.
2. Model selection and hypothesis tests.
3. Generalized regression model. Regression diagnosis. Weighted Least Square.
4. Special econometric models. regression models with dummy variables. Models with lagged variables.
5. Models for discrete choice. Limited dependent variables . truncation, censoring, sample selection.
6. Nonlinear regression models.
7. Systems of equations. Models for panel data. Simultaneous Equations models.
8. Models of interest rates and interest rate derivatives.
9. Quantitative risk management.
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Basic:
HUŠEK, R. Ekonometrická analýza. Praha : Ekopress, 1999. ISBN 80-86119-19-X.
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Basic:
Cipra, Tomáš. Ekonometrie. SPN Praha, 1984.
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Basic:
Hušek, Roman. Základy ekonometrické analýzy I : modely a metody. 1. vyd. Praha : VŠE, 1996. ISBN 80-7079-102-0.
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Basic:
HUŠEK R. Základy ekonometrické analýzy II. Speciální postupy a techniky. VŠE, Praha 1998. 1998.
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Recommended:
Cipra, Tomáš. Analýza časových řad s aplikacemi v ekonomii. SNTL Praha, 1986.
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Recommended:
Anděl, Jiří. Matematika náhody. Vyd. 2. Praha : Matfyzpress, 2003. ISBN 80-86732-07-X.
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Recommended:
ARLT, J. Moderní metody modelování ekonomických časových řad. Vyd. 1. Praha : Grada, 1999. ISBN 80-7169-539-4.
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Recommended:
Zvára, K. Regresní analýza. Academia Praha, 1989.
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Recommended:
G. Judge a spol. Theory and Practice of Econometrics.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Contact hours
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39
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Preparation for an examination (30-60)
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40
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Undergraduate study programme term essay (20-40)
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30
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Total
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109
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
Students should have a basic knowledge of differential and integral one variable functions calculus, basic knowledge of matrix theory and basic knowledge of probability and statistics. |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
On completion of this module the student will be able to:
- understand and apply the least-squares method to estimate linear regression,
- analyse regression models with dummy variables,
- analyse and use some special economic regression models,
- analyse and use probit and logit regression models,
- use and understand econometrics methods for modeling of interest rates,
- use and understand methods for quantitative risk management.
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Combined exam |
Individual presentation at a seminar |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Interactive lecture |
Self-study of literature |
Individual study |
Students' portfolio |
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