COURSE UNIT TITLE

: ADVANCED ECONOMETRICS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ECN 6065 ADVANCED ECONOMETRICS COMPULSORY 3 0 0 9

Offered By

Economics (English)

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR ERDOST TORUN

Offered to

Economics (English)

Course Objective

The objective of the course is to provide advanced econometric tools to economic
measures. Emphasis is on the advanced economic modeling, estimation techniques, and
interpretation of empirical findings. The use of computer is an integrated part of the
course. Students are expected to prepare a term project to demonstrate their skills
developed in the course.

Learning Outcomes of the Course Unit

1   Be able to collect raw data related to economic, financial and social topics, and make them ready for statistical and econometric analysis.
2   Demonstrate understanding of building linear and nonlinear econometric models.
3   Identify problems with linear and nonlinear econometric models.
4   Be able to interpret the estimation results so that the learner can draw implications from the results.
5   Demonstrate engaging an independent empirical research in order to prepare a term project.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Math and Statistics Review
2 Estimation methods: OLS, MLE, and GMM
3 Estimation methods: OLS, MLE, and GMM
4 Linear econometric models
5 Linear econometric models
6 Linear econometric models
7 Midterm
8 Nonlinear econometric models
9 Nonlinear econometric models
10 Nonlinear econometric models
11 Nonlinear econometric models
12 Nonlinear econometric models
13 Nonlinear econometric models
14 General Overview

Recomended or Required Reading

1. William H. Greene, Econometric Analysis, 7th Ed. Pearson, 2011
2. Lecture Notes

Planned Learning Activities and Teaching Methods

1. Lectures
2. Class Discussions
3. Term Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FCG FINAL COURSE GRADE
3 FCGR FINAL COURSE GRADE MTE * 0.40 + FCG* 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST* 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

1. The learner will use necessary statistical and econometric tools to engage
independent research and make it ready for submission to the academic journals.
2. The learner will clearly recognize the problems with linear and nonlinear
econometric models.
3. The learner will build econometric models for estimation purposes.
4. The learner will interpret empirical results.
5. The learner will draw some policy implications from estimation results.

Language of Instruction

English

Course Policies and Rules

1. It is obligatory to attend at least 70% of the classes.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Tutorials 13 2 26
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 30 30
Preparation for final exam 1 30 30
Preparing assignments 1 35 35
Preparing presentations 1 15 15
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 220

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.14
LO.2455
LO.3545
LO.443
LO.545