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| EKO 306 | Time Series - II | 3+0+0 | ECTS:7 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of ECONOMETRICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Rahmi YAMAK | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course is a continuation of Time Series-I course and has the same objectives. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | understand mathematical and statistical techniques that are used in time series analysis. | 3 | 1 | | LO - 2 : | recognize when and how to use these techniques. | 4 | 1 | | LO - 3 : | produce forecasts by using time series. | 2,4,5 | 4 | | LO - 4 : | evaluate various forecasts and determine the best. | 2,4 | 4 | | LO - 5 : | produce micro or macro policies based on series investigated and forecast chosen. | 6,7 | 6 | | 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), LO : Learning Outcome | | |
| Forecasting with multiple regression. Examining correlations in time series: the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Stationarity. ARIMA Models and forecasting with ARIMA Models. Introduction to autoregressive conditional heteroscedastic (ARCH) models. Vector Autoregressive Models. Co-integration.
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Definition and characteristics of stanionary | | | Week 2 | Unit Root Tests | | | Week 3 | Unit Root Tests | | | Week 4 | Co-integration Test, Engle-Granger | | | Week 5 | Co-integration Test, Johansen-Juselius | | | Week 6 | Causality Tests | | | Week 7 | VAR Analysises | | | Week 8 | Mid-Term Exam | | | Week 9 | Error-Correction Models | | | Week 10 | Box-Jenkins Models | | | Week 11 | Box-Jenkins Models | | | Week 12 | ARCH Models | | | Week 13 | GARCH Models | | | Week 14 | E-GARCH and M-GACRH Models | | | Week 15 | E-Views Applications | | | Week 16 | The end of term Exam | | | |
| 1 | Makridakis, S., Wheelwright, S.C. ve Hyndman, R.J. 1998; Forecasting Methods ans Applications, John Wiley & Sons, USA. | | | 2 | Enders, W. 2004; Applied Econometric Time Series, John Wiley & Sons, USA. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | 04/2016 | 1 | 30 | | Homework/Assignment/Term-paper | 12 | 04/2016 | 2 | 20 | | End-of-term exam | 16 | 06/2016 | 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 | 8 | 14 | 112 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 10 | 2 | 20 | | Arasınav | 1 | 1 | 1 | | Uygulama | 0 | 0 | 0 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 0 | 0 | 0 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 11 | 3 | 33 | | Dönem sonu sınavı | 2 | 1 | 2 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 210 |
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