STAT563 MULTIVARIATE TIME SERIES
| Course Code: | 2460563 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 7.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Prof.Dr. CEYLAN YOZGATLIGİL |
| Offered Semester: | Spring Semesters. |
Course Objectives
This course aims to:
- Introduce statistical methods for analyzing multivariate data commonly encountered in biological, physical, and social sciences.
- Equip students with skills to model and interpret multivariate relationships using real-world datasets.
- Cover advanced time series methods including transfer function models, time series regression, GARCH, vector time series, cointegration, causality, state space models, Kalman filter, and long-memory and nonlinear processes.
- Develop students' ability to apply techniques such as cross-spectral analysis, temporal aggregation, and disaggregation in practical contexts.
Course Content
Transfer function models and cross-spectral analysis, time series regression and GARCH models, vector time series models, error-correction models, cointegration and causality, state space models and Kalman filter, long memory processes, nonlinear processes, temporal aggregation and disaggregation.
Course Learning Outcomes
By the end of this course, students will be able to:
- Analyze multivariate data and interpret relationships among variables.
- Apply time series regression and transfer function models.
- Use and estimate GARCH and ARCH models for volatility modeling.
- Build and interpret vector autoregressive and moving average models.
- Identify and apply cointegration and error correction models.
- Perform cross-spectral analysis and frequency domain techniques.
- Model and forecast using state space models and the Kalman filter.
- Analyze long-memory and nonlinear time series processes.
- Apply temporal aggregation and disaggregation techniques.
- Handle real-world datasets using advanced time series methods.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Ability for converting theoretical, methodological, and computational statistical knowledge into analytical solutions in researches requiring statistical analyses. | ✔ | |||
| 2 | Ability for specifiying problems in real life situations bearing uncertainty, forming hypotheses, modeling, application, and interpreting the results. | ✔ | |||
| 3 | Ability for using current technology, computer softwares for statistical applications, computer programming for specific problems when necessary, writing computer codes for speeding up statistical calculations, organizing and cleaning databases, and preparing them for statistical analyses, and data mining. | ✔ | |||
| 4 | Ability for taking part in intra/inter disciplinary team work, efficient use of time, taking responsibility as a team leader, and entrepreneurship. | ✔ | |||
| 5 | Ability for taking responsibility in solitary work and producing creative solutions. | ✔ | |||
| 6 | Ability for keeping up-to-date with current advancements in statistical sciences, doing research, being open-minded, and adopting critical thinking. | ✔ | |||
| 7 | Ability for effective communication both in Turkish and English in specification of statistical problems, analyes, and interpretation of findings. | ✔ | |||
| 8 | Ability for using the knowledge in the field of expertise for the welfare of the society. | ✔ | |||
| 9 | Ability for suggesting the researchers in a comprehensible way the appropriate statistical methods for problems in fields that use statistics such as economics, finance, industrial engineering, genetics, and medicine and apply if needed. | ✔ | |||
| 10 | Ability for catalyzing discussions and presentations, public speaking, making presentations, communicating topics of expertise to the audiance in a comprehensible way. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution