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| EKOZ6080 | Selected Topics in Time Series I | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Compulsory | | Department | DEPARTMENT of ECONOMETRICS | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Zehra ABDİOĞLU | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The main objective of this course is to introduce some of the techniques that are used in time series analysis. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | recognize some of the frequently-used time series techniques. | 2 - 3 | 1, | | PO - 2 : | recognize when and how to use these new techniques | 2 - 3 | 1, | | PO - 3 : | make predictions by using time series. | 2 - 3 | 1, | | PO - 4 : | evaluate the findings of applications about time series. | 2 - 3 | 1, | | PO - 5 : | produce micro or macro policies based on hypothesis investigated and forecast chosen. | 2 - 3 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Stationarity, Unit Root Tests, Seasonal Unit Root Tests, Structural Breaks and Unit Root Tests, Box-Jenkins Models, ARCH-GARCH Models, Eviews Applications. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Stationarity | | | Week 2 | The Autocorrelation Function | | | Week 3 | The Partial Autocorrelation Function | | | Week 4 | Unit Root Tests | | | Week 5 | Seasonal Unit Root Tests | | | Week 6 | Structural Breaks and Unit Root Tests | | | Week 7 | AR, MA and ARMA Processes | | | Week 8 | Box-Jenkins Models | | | Week 9 | Mid-term exam | | | Week 10 | Autoregressive Conditional Heteroscedasticity Models (ARCH and GARCH) | | | Week 11 | GARCH in Mean Model | | | Week 12 | Quiz | | | Week 13 | Asymmetric Autoregressive Conditional Heteroscedasticity Models (TGARCH) | | | Week 14 | Asymmetric Autoregressive Conditional Heteroscedasticity Models (EGARCH) | | | Week 15 | ARCH-GARCH Estimates of Financial Time Series: Eviews Applications | | | Week 16 | End-of-term exam | | | |
| 1 | Hamilton, James.D. (1994) Time Series Analysis, Princeton University Press, Second Edition, USA | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 11/2024 | 1 | 30 | | Homework/Assignment/Term-paper | 12 | 12/2024 | 1 | 20 | | End-of-term exam | 16 | 01/2025 | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 4 | 14 | 56 | | Laboratuar çalışması | 4 | 14 | 56 | | Arasınav için hazırlık | 1 | 10 | 10 | | Arasınav | 1 | 1 | 1 | | Uygulama | 3 | 14 | 42 | | Ödev | 1 | 14 | 14 | | Kısa sınav | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 2 | 1 | 2 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 225 |
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