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| EKO5080 | Time Series Analyses - I | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | 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. Rahmi YAMAK | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course is about time series econometrics and its applications. The main objective of this course is to introduce some of the new techniques that are used in time series analysis. Applications are made by using EViews or WinRATS statistical softwares. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | understand those time series techniques that have been recently developed. | 1,6 | 1,3, | | PO - 2 : | recognize when and how to use these new techniques. | 1,6 | 1,3, | | PO - 3 : | produce forecasts and test economic, financial or fiscal hypothesis by using time series. | 1,6 | 1,3, | | PO - 4 : | determine the best forecast by evaluating various forecasts and choose acceptable hypotheses that are suitable for Turkish case. | 1,6 | 1,3, | | PO - 5 : | produce micro or macro policies based on hypothesis investigated and forecast chosen. | 1,6 | 1,3, | | 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 | | |
| Review of Stationary Time Series Models: ARMA Models, Stationary, The Autocorrelation Function, The Partial Autocorrelation Function and Box-Jenkis Model Selection. Discussion on Forecast Evaluation Criteria. Modeling Volatility: ARCH and GARCH Models, Testing for ARCH Errors, ARCH-M Model, IGARCH Model, Explanatory Variables in Conditional Variance Equation, Testing for Leverage Effects and Model with Asymmetry. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Stationary Time Series Models: ARMA Models, stationarity, Autocorrelation Function, Partial Autocorrelation Function. | | | Week 2 | Sample autocorrelations of stationary series, Box-Jenkis model selection. | | | Week 3 | Forecast Evaulation: The Granger-Newbold and The Diebold-Mariano Tests. | | | Week 4 | Seasonality: models of seasonal data, seasonal differencing. | | | Week 5 | Introduction to Autoregressive Conditional Heteroscedastic (ARCH) Models: main properties of ARCH processes. | | | Week 6 | Examination for ARCH Affect: correlogram of squared residuals and ARCH LM Test. | | | Week 7 | Assessing the fit of ARCH model, diagnostic check of ARCH model. | | | Week 8 | ARCH-M Model, GARCH Model, Maximum Likelihood Estimation of GARCH Models. | | | Week 9 | Mid-term exam | | | Week 10 | Integrated GARCH model, explanatory variables in conditional variance equation. | | | Week 11 | An Asymmetric Model: Threshold ARCH (TARCH) Model. | | | Week 12 | Quiz | | | Week 13 | An Asymmetric Another Model: Exponential GARCH (EGARCH) Model, Testing for leverage effect. | | | Week 14 | Discussion on distribution of errors: non-normal Errors. | | | Week 15 | Estimation of ARCH Model in which errors have student's t distribution, estimation of ARCH Model in which errors have generalized error (GED) distribution. | | | Week 16 | End-of-term exam | | | |
| 1 | Enders, W. 2004; Applied Econometric Time Series, John Wiley and Sons, 2.,USA. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 11/2023 | 1 | 30 | | Homework/Assignment/Term-paper | 12 | 12/2023 | 2 | 20 | | End-of-term exam | 16 | 01/2024 | 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 | 7 | 14 | 98 | | Arasınav için hazırlık | 12 | 3 | 36 | | Arasınav | 1 | 1 | 1 | | Ödev | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 15 | 3 | 45 | | Dönem sonu sınavı | 1 | 1 | 1 | | Diğer 1 | 1 | 1 | 1 | | Total work load | | | 225 |
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