| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Basic Econometrics I | ITM123 | Elective | Master's degree | 1 | Fall | 6 |
Name of Lecturer(s)
Prof. Dr. Selçuk KOÇ
Prof. Dr. Recep TARI
Learning Outcomes of the Course Unit
1) Recognize the econometrical research method.
Uses the method of regression analysis.
Apply test the estimates.
Use the kind of functional forms.
Identification and correction multicollinearity, heteroscedasticity and autocorrelation.
1) Recognize the econometrical research method. Uses the method of regression analysis. Apply test the estimates. Use the kind of functional forms. Identification and correction multicollinearity, heteroscedasticity and autocorrelation.
1) Recognize the econometrical research method. Uses the method of regression analysis. Apply test the estimates. Use the kind of functional forms. Identification and correction multicollinearity, heteroscedasticity and autocorrelation.
1) Recognize the econometrical research method. Uses the method of regression analysis. Apply test the estimates. Use the kind of functional forms. Identification and correction multicollinearity, heteroscedasticity and autocorrelation.
1) Recognize the econometrical research method. Uses the method of regression analysis. Apply test the estimates. Use the kind of functional forms. Identification and correction multicollinearity, heteroscedasticity and autocorrelation.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||
| 1 | 2 | 3 | ||
| Learning Outcomes | ||||
| 1 | Middle | Middle | Middle | |
| 1 | High | High | High | |
| 1 | High | High | High | |
| 1 | Middle | Middle | Middle | |
| 1 | Middle | Middle | Middle | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Econometric I
Course Contents
This course equips candidates with in-depth knowledge on econometrics and its scope, simple regression model, properties of estimation techniques, multiple regression model, solving by matrices, types of functional forms, linear form, multiple form, semi-logarithmic form, logarithmic form, inverse form, multicollinearity, heteroscedasticity, autocorrelation.
Weekly Schedule
1) Least Square Method2) Least Square Method
3) Generalized Moment Method
4) Maximum Likelyhood Method
5) Proxy Method
6) T and F Tests
7) T and F Tests
8) Midterm Examination/Assessment
9) Stockastic Process and Stationary
10) AR, MA ve ARMA Process
11) Partial Auto-corellation Function
12) Partial Auto-corellation Function
13) Estimation, Validity and Spesification in ARMA Models
14) Non-Stationary Time Series Models, Trend Stationary and Unit Root
15) ARIMA and Seasonal ARIMA Models
16) Final Examination
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Discussion
4) Demonstration
5) Group Study
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
40% |
|---|---|
Contribution of Final Examination to Course Grade |
60% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required