Information Package / Course Catalogue
Econometrics
Course Code: EK171
Course Type: Non Departmental Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

The preliminary aim of this course is to provide students general information about econometrics. Secondly, it aims to provide them basic skills to enpower their academic background. Moreover, teaching econometric methods that would help students to interpret on economical issues using data is another aim of the course.

Course Content

The aim of the course is to teach Notion of Econometrics and Econometric Models, Introduction to Estimation Theory, Properties of the Estimator, Estimation Methods (Least Squares Method, Method of Moments, Maximum Likelihood Method, Best Linear Unbiased Estimator), Simple Linear Regression, Basic Assumptions, Estimation of Parameters, t Test, F Test, Correlation Coefficient, Coefficient of Determination and Interval Estimation subjects are the context of the course.

Name of Lecturer(s)
Learning Outcomes
1. To be able to understand the difference between time series, cross-sectional and panel data
2.To be able to define the purposes of econometrics model
3.To be able to analyse the model with appropriate econometrics method
4.To understand the most important econometric problems and their solutions
5.To learn how to test and estimate using computer programmes
Recommended or Required Reading
1.Gujarati, D. N. & Porter, D. C. 2009. Basic econometrics, 5th edn. New York: The McGraw-Hill Companies Inc.
2.Wooldridge, J. M. (2000), Introductory Econometrics: A Modern Approach. Cincinnati, OH: SouthWestern
Weekly Detailed Course Contents
Week 1 - Theoretical
The purpose of econometrics, the topic of econometrics and the preceding steps in econometrics research
Week 2 - Theoretical
Simple Linear Regression Model(Bivariate Regression Model), Least Square Regression Model and its assumptions.
Week 3 - Theoretical
Multiple Regression Model
Week 4 - Practice
STATA applications using simple and multiple regression models
Week 5 - Theoretical
Hypothesis Tests, Regression and Analysis of Variance Analysis
Week 6 - Theoretical
Qualitative Information and Dummy Variables
Week 7 - Theoretical
Heteroscedasticity Problem
Week 8 - Intermediate Exam
Midterm Exam
Week 9 - Theoretical
Multicollineartity, meaning of Multicollineartity, Estimations of Least Square Regression in case Multicollineartity, consequences of Multicollineartity, detection and solving Multicollineartity
Week 10 - Theoretical
Autocorrelation, meaning of Autocorrelation, Estimations of Least Square Regression in case Autocorrelation, consequences after Autocorrelation
Week 11 - Theoretical
Time-Series Analysis
Week 12 - Theoretical
Serial Correlation in Time Series , detection and solving of the problem
Week 13 - Theoretical
Basic Panel Data Method
Week 14 - Theoretical
Instrumental Variables Estimation and Two Stage Least Squares
Week 15 - Practice
Computer Applications using STATA
Week 16 - Final Exam
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Individual Work73235
Midterm Examination1819
Final Examination1819
TOTAL WORKLOAD (hours)123
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
OÇ-1
3
5
3
5
3
5
3
5
3
4
OÇ-2
3
5
3
5
3
5
3
5
3
5
OÇ-3
3
5
5
5
5
5
5
3
3
3
OÇ-4
3
5
3
3
3
3
3
3
3
4
OÇ-5
3
5
3
3
3
5
3
5
5
5
Adnan Menderes University - Information Package / Course Catalogue
2026