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Course info
KMA / MME
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Course description
Department/Unit / Abbreviation
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KMA
/
MME
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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,
4
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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Czech, English
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Occ/max
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|
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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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3 / -
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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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Czech, 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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N/A
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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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KIV/EPMM, 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.
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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. Finanční ekonometrie. 1. vyd. Praha : Ekopress, 2008. ISBN 978-80-86929-43-9.
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Basic:
Heiss. Using R for Introductiory Econometrics.
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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:
Cipra, Tomáš. Ekonometrie. SPN Praha, 1984.
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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: |
describe and explain the basic concepts of differential and integral calculus (within the scope of subjects KMA / M1 and KMA / M2) |
describe and explain the basic operations of matrix calculus (within the scope of the course KMA / LA) |
describe and explain the principles of statistical inference - especially the principles of point and interval estimates and the principles of testing statistical hypotheses (within the scope of the course KMA / PSA) |
explain basic microeconomic and macroeconomic theories of mainstream economics (in the range of subjects KEM / EK1 and KEM / EK2) |
formulate and explain the definition of probability (within the scope of the subject KMA / PSA) |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
apply theoretical economic knowledge to model situations |
differentiate between different types of random variables (discrete, continuous) and different types of distributions |
use knowledge of basic statistical methods and procedures for simple data analysis |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
describe and explain the assumptions of regression analysis and the consequence of non-compliance with the assumption on the quality of model parameter estimation |
describe and interpret regression models with binary variables |
explain the principle of regression models and describe various possibilities of estimating the parameters of regression models |
formulate econometric models as regression linear and nonlinear models |
Skills - skills resulting from the course: |
apply the formal and content side correctly in mathematical expression, both written and oral |
estimate the parameters of linear and nonlinear regression models in at least one SW environment |
formulate a regression model suitable for specific data |
interpret econometric models of the general professional public and is able to assess the adequacy of the use of the proposed models |
use knowledge of the assumptions of regression models for regression diagnostics |
Competences - competences resulting from the course: |
N/A |
N/A |
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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 |
Seminar work |
Skills - skills achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
Competences - competence achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Interactive lecture |
Individual study |
Skills - the following training methods are used to achieve the required skills: |
Individual study |
Interactive lecture |
Competences - the following training methods are used to achieve the required competences: |
Individual study |
Interactive lecture |
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