Syllabus ( ECON 589 )
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Basic information
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| Course title: |
Time Series Analysis |
| Course code: |
ECON 589 |
| Lecturer: |
Prof. Dr. Hüseyin İNCE
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| ECTS credits: |
7.5 |
| GTU credits: |
0 (3+0+0) |
| Year, Semester: |
1/2, Fall and Spring |
| Level of course: |
Second Cycle (Master's) |
| Type of course: |
Scientific preparation
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| Language of instruction: |
Turkish
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
none |
| Professional practice: |
No |
| Purpose of the course: |
The objective of this course is to teach and apply statistical methods for the analysis of data that have been observed over time. |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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Savvy the scope and limitations of time series analysis.
Contribution to Program Outcomes
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Discover, classify and analyze economic data
Method of assessment
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Written exam
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Homework assignment
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Read, write and properly interpret articles and reports of an applied time series nature which use these methods.
Contribution to Program Outcomes
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Discover, classify and analyze economic data
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Recognize and follow current economic problems
Method of assessment
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Homework assignment
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Identify the econometric techniques applicable (and not applicable) to the time series analysis of various economic, financial and management theories.
Contribution to Program Outcomes
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Discover, classify and analyze economic data
Method of assessment
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Written exam
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Contents
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| Week 1: |
Introduction |
| Week 2: |
Autocorrelation Function |
| Week 3: |
Stationary time series process (ARMA process) |
| Week 4: |
Stationary time series process (ARMA process) |
| Week 5: |
Model Identification |
| Week 6: |
Non stationary time series models |
| Week 7: |
Unit root test |
| Week 8: |
Unit root test: Examples
Midterm Exam |
| Week 9: |
Seasonal time series models |
| Week 10: |
Forecasting: ARIMA and Exponential forecasting |
| Week 11: |
Estimation |
| Week 12: |
Diagnostic checking |
| Week 13: |
Modelling Volatility: ARCH and GARCH models |
| Week 14: |
VAR models and Granger Causality |
| Week 15*: |
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| Week 16*: |
Final Exam |
| Textbooks and materials: |
William W.S: Wei., Time Series Analysis, 2nd Edition, Addison-Wesley, 2006 |
| Recommended readings: |
William W.S: Wei., Time Series Analysis, 2nd Edition, Addison-Wesley, 2006
Hamilton ,J. D., ,Time Series Analysis, Prenceton, 1998 |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
8 |
20 |
| Other in-term studies: |
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0 |
| Project: |
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0 |
| Homework: |
10,12 |
20 |
| Quiz: |
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0 |
| Final exam: |
16 |
60 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
14 |
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| Own studies outside class: |
4 |
14 |
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| Practice, Recitation: |
0 |
0 |
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| Homework: |
10 |
2 |
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| Term project: |
0 |
0 |
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| Term project presentation: |
0 |
0 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
15 |
2 |
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| Mid-term: |
1 |
1 |
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| Personal studies for final exam: |
20 |
2 |
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| Final exam: |
2 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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