Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
ECONOMETRICS I IKT4011 7. Semester 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction English
Course Level Bachelor's Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals This subject aims at giving students basic understanding of econometrics theories and applying econometric techniques to specific empirical economic problems. Numerous examples are examined to achieve this goal. Emphasis is placed on the classical linear model, least-squares estimation, hypothesis testing, and model building. Econometric models are then adopted to analyze practical problems and make forecasts. Furthermore, students are trained in this subject to use computer statistics software. (i.e., EVIEWS).
Course Content This subject aims at giving students basic understanding of econometrics theories and applying econometric techniques to specific empirical economic problems. Numerous examples are examined to achieve this goal. Emphasis is placed on the classical linear model, least-squares estimation, hypothesis testing, and model building. Econometric models are then adopted to analyze practical problems and make forecasts. Furthermore, students are trained in this subject to use computer statistics software. (i.e., EVIEWS).
Learning Outcomes 1) Understands the assumptions of Least Squares (OLS) and similar estimation methods
2) To be able to determine whether the models estimated with OLS provide basic assumptions and solve problems.
3) Formulate, test and evaluate simple regression models

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Introduction Lecture
Brainstorming
Project Based Learning
Homework
2. Week Econometric models: Definitions, aims, and estimation problem Lecture
Brainstorming
Project Based Learning
Homework
3. Week Explantions about statistical concepts and methods Lecture
Brainstorming
Project Based Learning
Homework
4. Week The Regression Model and the Ordinary Least Squares Method Lecture
Brainstorming
Project Based Learning
Homework
5. Week The Classical Regression model : Estimation, Properties and Inference Lecture
Brainstorming
Project Based Learning
Homework
6. Week Hypothesis Testing - Simple hypothesis test Lecture
Brainstorming
Project Based Learning
Homework
7. Week Hypothesis Testing - Joint Hypotheses Lecture
Brainstorming
Project Based Learning
Homework
8. Week Extension of The Linear Regression Model Lecture
Brainstorming
Project Based Learning
Homework
9. Week The Restriction Effect, Functional Form and Specification Lecture
Brainstorming
Project Based Learning
Homework
10. Week Dummy Variables Lecture
Brainstorming
Project Based Learning
Homework
11. Week Autocorrelation Lecture
Brainstorming
Project Based Learning
Homework
12. Week Heteroscedasticity Lecture
Brainstorming
Project Based Learning
Homework
13. Week Simultaneous Equations Lecture
Brainstorming
Project Based Learning
Homework
14. Week Simultaneous Equations 2 Lecture
Brainstorming
Project Based Learning
Homework

Sources Used in This Course
Recommended Sources
J. M. Wooldridge, (2013) Introductory Econometrics, Thomson-Southwestern Pub.; Çevirisi Ekonometriye Giriş: Modern Yaklaşım, Nobel Yayınevi, Çeviri Editörü Ebru Çağlayan D.N. Gujarati, (2004) Basic Econometrics, McGraw Hill pub. Önceki baskısının çevirisi Temel Ekonometri, Damodar N. Gujarati, Çeviri: Ümit Şenesen, Gülay Günlük Şenesen, Literatür Yayınları, İstanbul, 2001. Verbeek, M, (2000), A guide to Modern Econometrics, Wiley. C. Heij, Boer, P., Franses, P.H., Kloek, T., ve Dijk H.K. (2004) Econometric Methods with Application in Business and Economics, Oxford Uni. Press. W. Greene, (2000) Econometric Analysis, Prentice Hall. J. Johnston ve J. Dinardo, (1997) Econometric Methods, 4.ed., McGraw Hill.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 10
Midterm Exam 1 1
Time to prepare for Midterm Exam 2 10
Final Exam 1 1
Time to prepare for Final Exam 2 20
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information