Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İST002ECONOMETRICSElective243
Level of Course Unit
First Cycle
Objectives of the Course
To give students the ability applying mathematics and statistics to economic data
Name of Lecturer(s)
Yrd.Doç.Dr. Özge ELMASTAŞ GÜLTEKİN
Learning Outcomes
1To have a knowledge about some basic mathematical and statistical rules
2To learn micro, macro, and sector-specific models
3To be able to construct linear, parabolic, semi-parabolic-double logarithmic econometric models
4To be able to construct econometric models consisting of single explanatory variable
5To be able to construct multiple regression model, to make inference and hypothesis test, to learn solution techniques with matrices
6To have a knowledge about multicollinearity, varying variance and autocorrelation, to be able to determine spesification errors
7To be able to construct models including dummy variables
Mode of Delivery
Face to Face
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Course Contents
Topic of Econometrics - Some basic mathematical rules - Basic statistical rules - Micro, macro, and sector-specific models - Econometric model patterns (Linear, parabolic, semi-parabolic-double logarithmic)- Econometric models consisting of single variable- Multiple regression estimation and hypothesis tests, solution techniques with matrices- Deviations from classical regression model- Multicollinearity, varying variance, autocorrelation, specification errors, models including dummy variables.
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Topic of Econometrics - Some basic mathematical rules - Basic statistical rules
2Topic of Econometrics - Some basic mathematical rules - Basic statistical rules
3Micro, macro, and sector-specific models
4Econometric model patterns (Linear, parabolic, semi-parabolic-double logarithmic)
5Econometric models consisting of single variable (Single explanatory variable)
6Multiple regression estimation and hypothesis tests
7Solution techniques of multiple regression model with matrices
8Deviations from classical regression model
9Midterm exam
10Multicollinearity, varying variance
11Autocorrelation, specification errors
12Models including dummy variables (One quantitative and one qualitative variables)
13Models including dummy variables (One quantitative and one qualitative variables)
14Models including dummy variables (One quantitative and two qualitative variables)
15Models including dummy variables (One quantitative and two qualitative variables)
16Final exam
Recommended or Required Reading
Ertek, T., Ekonometriye giriş Akkaya, Ş., Pazarlıoğlu, V., Ekonometri I Gujarati, D.N., Basic Econometrics
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
SUM0
End Of Term (or Year) Learning ActivitiesQuantityWeight
SUM0
SUM0
Language of Instruction
Turkish
Work Placement(s)
None
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination122
Final Examination122
Quiz122
Practice14342
Problem Solving14342
Individual Study for Homework Problems11414
Individual Study for Mid term Examination7214
Individual Study for Final Examination7214
Individual Study for Quiz11414
TOTAL WORKLOAD (hours)146
Contribution of Learning Outcomes to Programme Outcomes
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* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr