Course objectives:
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The aim of this course is an introduction and active understanding of the concepts the regression and time series and show their applicability to practical exercises.
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Requirements on student
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Demonstrate knowledge and understanding of the material treated in the course, including the mathematical apparatus used. Use rigorous arguments in calculus and ability to apply them in solving problems on the topics in the syllabus.
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Content
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(1) Time series. Decomposition of time series. Trend estimation
(2) Regression analysis. Statistical properties of the least squares estimator. Linear and nonlinear regression models.
(3) Single and centered moving average.
(4) Exponential smoothing.
(5) Seasonality.
(6) The Box-Jenkins methodology. Stationarity. Modesl AR, MA, ARMA,....Estimation and diagnostics.
(7) Linear dynamic models.
(8) Spectral analysis.
(9) Multivariate Time Series Models.
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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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Recommended:
Cipra, Tomáš. Analýza časových řad s aplikacemi v ekonomii. SNTL Praha, 1986.
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Recommended:
Kozák, Josef; Arlt, Josef,; Hindls, Richard. Úvod od analýzy ekonomických časových řad. 1. vyd. Praha : VŠE, 1994. ISBN 80-7079-760-6.
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Recommended:
Antoch, Jaromír; Vorlíčková, Dana. Vybrané metody statistické analýzy dat. Vyd. 1. Praha : Academia, 1992. ISBN 80-200-0204-9.
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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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Preparation for an examination (30-60)
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50
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Preparation for comprehensive test (10-40)
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40
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Contact hours
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52
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Total
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142
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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: |
orientovat se v problémech pravděpodobnosti a statistiky v rozsahu předmětu KMA/PSA (popř. KMA/PSB nebo KMA/PSE) |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
používat diferenciální a integrální počet v rozsahu úvodních kurzů matematiky na vysokých školách |
pracovat v alespoň jednom výpočetním prostředí typu Matlab, Mathematica, R a podobně |
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: |
formulovat jednoduché i složitější modely časových řad |
popsat, vysvětlit a porozumět modelům časových řad (dekompozičním modelů, adaptivním modelům) |
vysvětlit principy různých přístupů k odhadům parametrů modelů časových řad |
Skills - skills resulting from the course: |
použít studované modely na konkrétní data |
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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 |
Skills demonstration during practicum |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Interactive lecture |
Task-based study method |
Skills - the following training methods are used to achieve the required skills: |
Interactive lecture |
Task-based study method |
Competences - the following training methods are used to achieve the required competences: |
Interactive lecture |
Task-based study method |
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