| Course Title | Code | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|
| Econometrics II | İKT302 | 6. Semester | 3 + 0 | 3.0 | 5.0 |
| Prerequisites | None |
| Language of Instruction | Turkish |
| Course Level | Undergraduate |
| Course Type | |
| Mode of delivery | Face to face. |
| Course Coordinator |
Assist. Prof. Dr. HANDE ÇALIŞKAN TERZİOĞLU |
| Instructors |
HANDE ÇALIŞKAN TERZİOĞLU |
| Assistants | |
| Goals | The aim of this course is to extend and deepen students' understanding of the topics learned in the basic Econometrics I course. This course aims to provide students with more advanced skills in econometric analysis by focusing on the more complex structures of multiple regression models and the theories of time series and panel data analysis. |
| Course Content | Dummy Variables, Lagged Regression Models, Time series, Panel data, qualitative variables |
| Learning Outcomes |
- Understanding multiple regression models - Learning dummy variable models. - Examining the use of econometric package programs and monitoring how to interpret the outputs in models - Analyzing the effects of model results on economic policy recommendations and interpreting the results - Understanding the structure of time series and panel data analysis and how these analyses can be used to understand changes in economic data over time. |
| Week | Topics | Learning Methods |
|---|---|---|
| 1. Week | Multiple regression models with dummy variables: Identification of qualitative information, single independent dummy variable, use of dummy variables for multiple cases, interaction between dummy variables | Practice Course Hours |
| 2. Week | Multiple regression models with dummy variables: Identification of qualitative information, single independent dummy variable, use of dummy variables for multiple cases, interaction between dummy variables | Course Hours Practice |
| 3. Week | Models with dummy variables: Two-state dependent variable, linear probability model, logit and probit models | Course Hours Practice |
| 4. Week | Lagged regression models: Almon multinomial lag model, Koyck model | Course Hours Practice |
| 5. Week | Lagged regression models: Almon multinomial lag model, Koyck model | Course Hours Practice |
| 6. Week | Lagged regression models: Nerlove's partial adjustment model, Cagan's adjusted expectation model | Course Hours Practice |
| 7. Week | Lagged regression models: Nerlove's partial adjustment model, Cagan's adjusted expectation model | Practice Course Hours |
| 8. Week | MIDTERM EXAM | |
| 9. Week | Simultaneous equation systems: Structure of simultaneous systems of equations, simultaneity problem in EKK estimation | Practice Course Hours |
| 10. Week | Simultaneous systems of equations: Identification and estimation of structural equations, solving systems of more than two structural equations | Practice Course Hours |
| 11. Week | Model selection and selection criteria | Course Hours Practice |
| 12. Week | Model selection and selection criteria | Practice Course Hours |
| 13. Week | Time series analysis: Unit root test, spurious regression | Practice Course Hours |
| 14. Week | Time series analysis: Cointegration and error correction model | Practice Course Hours |
| Tarı, Recep. (2015). Ekonometri. 11. Baskı, Umuttepe Yayınları |
| Program Requirements | Contribution Level | DK1 | DK2 | DK3 | DK4 | DK5 | Measurement Method |
|---|---|---|---|---|---|---|---|
| PY1 | 5 | 0 | 0 | 0 | 0 | 0 | - |
| PY2 | 4 | 0 | 0 | 0 | 0 | 0 | - |
| PY3 | 4 | 0 | 0 | 0 | 0 | 0 | - |
| PY4 | 3 | 0 | 0 | 0 | 0 | 0 | - |
| PY5 | 5 | 0 | 0 | 0 | 0 | 0 | - |
| PY6 | 5 | 0 | 0 | 0 | 0 | 0 | - |
| PY7 | 3 | 0 | 0 | 0 | 0 | 0 | - |
| PY8 | 4 | 0 | 0 | 0 | 0 | 0 | - |
| PY9 | 4 | 0 | 0 | 0 | 0 | 0 | - |
| PY10 | 4 | 0 | 0 | 0 | 0 | 0 | - |
| PY11 | 4 | 0 | 0 | 0 | 0 | 0 | - |
| PY12 | 3 | 0 | 0 | 0 | 0 | 0 | - |
| PY13 | 3 | 0 | 0 | 0 | 0 | 0 | - |
| PY14 | 3 | 0 | 0 | 0 | 0 | 0 | - |
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| Course's Level of contribution | None | Very Low | Low | Fair | High | Very High |
| Method of assessment/evaluation | Written exam | Oral Exams | Assignment/Project | Laboratory work | Presentation/Seminar |
| Event | Quantity | Duration (Hour) | Total Workload (Hour) |
|---|---|---|---|
| Course Hours | 14 | 3 | 42 |
| Preparation, After Class Study | 14 | 2 | 28 |
| Research | 6 | 3 | 18 |
| Other Activities | 14 | 2 | 28 |
| Midterm 1 | 1 | 1 | 1 |
| Final | 1 | 1 | 1 |
| Practice | 1 | 1.5 | 1.5 |
| Classroom Activities | 4 | 2 | 8 |
| Total Workload | 127.5 | ||
| ECTS Credit of the Course | 5.0 | ||
