Information Package / Course Catalogue
Econometrics I
Course Code: EK201
Course Type: Required
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

To gain the ability to use tools such as correlation and regression.

Course Content

Correlation analysis, simple and multiple regression analysis, individual and group significance test, determination coefficient , specification and functional structure and dummy variables.

Name of Lecturer(s)
Assoc. Prof. Tuğba AKIN
Learning Outcomes
1.To be able to understand the difference between time series and cross-sectional data
2.To be able to define the purposes of econometrics model
3.To be able to choose model as reasonable econometrics method
4.To be able to analysis assumptions of econometrics model
5.To be able to make inferences from econometrics models s evidences
Recommended or Required Reading
1.EKONOMETRİ I ( 2000), Şahin AKKAYA-M.Vedat PAZARLIOĞLU, Anadolu Matbaacılık, İzmir.
2.Ekonometri (2006), Recep TARI, Avcı Ofset, İstanbul.
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 - Theoretical
Hypothesis Tests, Regression and Analysis of Variance
Week 5 - Theoretical
Hypothesis Tests, Regression and Analysis of Variance
Week 6 - Theoretical
Topics with Bivariate Regression Models
Week 7 - Theoretical
The Other Tests for econometrics models with one equation, selection of models criteria
Week 8 - Theoretical
The Other Tests for econometrics models with one equation, selection of models criteria
Week 9 - Theoretical
Distribtions for Normality and Normality tests, Multicollineartity, meaning of Multicollineartity, Estimations of Least Square Regression in case Multicollineartity, consequences after Multicollineartity, detected and remove Multicollineartity
Week 10 - Theoretical
Heteroscedasticity, meaning of Heteroscedasticity, Estimations of Least Square Regression in case Heteroscedasticity, consequences after Heteroscedasticity
Week 11 - Theoretical
Detected and remove Heteroscedasticity
Week 12 - Theoretical
Autocorrelation, meaning of Autocorrelation, Estimations of Least Square Regression in case Autocorrelation, consequences after Autocorrelation
Week 13 - Theoretical
Detection and removal of Autocorrelation
Week 14 - Practice
Applications
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142256
Assignment92236
Individual Work71114
Midterm Examination1819
Final Examination1819
TOTAL WORKLOAD (hours)124
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
OÇ-1
4
5
4
5
5
4
5
4
4
OÇ-2
5
4
5
5
5
5
4
4
4
OÇ-3
4
5
5
4
4
5
4
5
4
OÇ-4
3
4
2
3
4
3
5
2
3
OÇ-5
4
5
4
5
4
5
4
5
4
Adnan Menderes University - Information Package / Course Catalogue
2026