Information Package / Course Catalogue
Econometrics
Course Code: EFN503
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

To present to the students sufficient theoretical background on basic econometric techniques and to prepare students in doing independent research project with computation intensive methods.

Course Content

A Review of Some Statistical Concepts, Introduction to Classical Regression Model, Least Squares and Projections, Finite Sample Properties of Least Squares Estimators, Normality Assumption and Basic Statistical Inference, Large Sample Properties of Least Squares Estimators, Application: Monte Carlo Simulation, Maximum Likelihood Estimation, Applications of Maximum Likelihood Estimation, Hypothesis Tests for Regression Models: LR, Wald, and LM Tests

Name of Lecturer(s)
Learning Outcomes
1.To be more familiar with guidelines for using econometric techniques
2.To have the confidence and skills to critically evaluate econometric research
3.To have a thorough understanding of why it is necessary to consider extensions tothe classical linear regression model
4.To be able to develop proficiency in use of econometric packages for econometric modelling
5.To be able to establish an econometric model
Recommended or Required Reading
1.Greene W. H., (2003), Econometric Analysis, Prentice-Hall, Fifth Edition
2.Stewart J. and Gill L (1998) Econometrics, Prentice Hall Europe, Second Edition
Weekly Detailed Course Contents
Week 1 - Theoretical
A Review of Some Statistical Concepts Probability and Distribution Theory
Week 2 - Theoretical
A Review of Some Statistical Concepts Estimation and Inference
Week 3 - Theoretical
Introduction to Classical Regression Model The Linear Regression Model Assumptions of the Classical Linear Regression Model
Week 4 - Theoretical
Least Squares and Projections Least Squares Regression The Projection Theorem
Week 5 - Theoretical
Finite Sample Properties of Least Squares Estimators Unbiased Estimation The Gauss Markov Theorem
Week 6 - Theoretical
Finite Sample Properties of Least Squares Estimators Estimating Variance of the Least Squares Estimator
Week 7 - Theoretical
Normality Assumption and Basic Statistical Inference Testing a Hypothesis About a Coefficient Testing Significance of a Regression
Week 8 - Intermediate Exam
Midterm Exam
Week 9 - Theoretical
Large Sample Properties of Least Squares Estimators Consistency of the Least Squares Estimator Asymptotic Normality of the Least Squares Estimator
Week 10 - Theoretical
Application: Monte Carlo Simulation
Week 11 - Theoretical
Maximum Likelihood Estimation The Likelihood Function , The Principle of Maximum Likelihood
Week 12 - Theoretical
Asymptotic Properties of the Maximum Likelihood Estimation Asymptotic Normality Asymptotic Efficiency
Week 13 - Theoretical
Applications of Maximum Likelihood Estimation Non-linear Regression Models
Week 14 - Theoretical
Applications of Maximum Likelihood Estimation Nonnormal Disturbances-The Stochastic Frontier Model
Week 15 - Theoretical
Hypothesis Tests for Regression Models The Likelihood Ratio Test, the Wald Test, and the Lagrange Multiplier Test
Week 16 - Final Exam
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Individual Work72228
Midterm Examination110111
Final Examination115116
TOTAL WORKLOAD (hours)125
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
4
5
OÇ-2
5
5
4
5
OÇ-3
4
4
4
OÇ-4
5
5
OÇ-5
Adnan Menderes University - Information Package / Course Catalogue
2026