Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
ECONOMETRICS IKT4007 7. Semester 3 + 0 3.0 6.0

Prerequisites None

Language of Instruction Turkish
Course Level Bachelor's Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors Emrah ER
Assistants
Goals Modelling of econometric data , testing of model appropriateness by using various statistical methods and parameters estimation
Course Content Type of data and source of data, properties of LS estimators and Gauss Markov Theorem, normality assumption, classical linear regression model, mathematical structures in economic theory, linear structures, semi logarithmic structures, parabolic structure and concept of elesticity, restricted LS, generalized LS, multicollinearity, heteroskedasticity, autocorrelation, simultaneous equations models, econometric time series models
Learning Outcomes 1) Explanation of the Structure of the Econometrics
2) Description of the Variable Types and the Relation Between the Variables
3) Explanation of the Economic Relation and Modelling
4) Description of the General Linear Models and their Extantions
5) Description of the Assumptions of the General Linear Models
6) Description of the Least Square Method and Parameter Estimation
7) Explanation of the Indexs Numbers
8) Explanation of the Panel Data Analyze
9) Explanation of the Regression and Correlaton of Tabulated Data

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Introduction to Econometrics Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
2. Week Econometric Models Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
3. Week Fitting of the Economic Theory into a Mathematical Form Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
4. Week Semi Logaritmic Form, Double Logaritmic Form Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
5. Week Endex Numbers Theory Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
6. Week Classical Linear Regression Model Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
7. Week Point and Interval Estimation Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
8. Week Midterm exam

9. Week Variable Selection In Multiple Linear Regression Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
10. Week Diversify from Classical Regression Model Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
11. Week Heteroshedasticity, Autocorrelation Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
12. Week Regression and Correlation for Grouping Data Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
13. Week Panel Data Analysis Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
14. Week Application Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
15. Week General Evaluation and Application Lecture; Question Answer; Problem Solving

 Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
16. Week Final exam


Sources Used in This Course
Recommended Sources
Ashenfelter, A, Levine P.B. and Zimmerman, D.J.:Statistics and Econometrics: Methods and applications,John Wiley and Sons. Inc.Danvers,2003.
Ferber, R. And Verdoorn, P.J. : Research Methods In Economics & Business, The Macmillan Company, New York, 1970.
Johnston,J.: Econometric Methods, Mc Graw-Hill, New York, 1972.
Kennedy, P.A.: Guide to Econometrics, Blackwell, London, 1998.

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5DK6DK7DK8DK9
PY15055555555
PY25555555555
PY35555555555
PY45555555555

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 4
Work Hour outside Classroom (Preparation, strengthening) 14 4
Homework 5 5
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 20
Final Exam 1 1
Time to prepare for Final Exam 1 20
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload