Advance Data Modeling (ECON552) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advance Data Modeling ECON552 3 0 0 3 5
Pre-requisite Course(s)
Course Language Turkish
Course Type N/A
Course Level Social Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
  • Prof. Dr. Tolga Omay
Course Assistants
Course Objectives This course provides an understanding of the application of software technologies that enables users to make better and faster decisions based on various data. This course covers the statistical tools needed to understand empirical research and to plan and execute independent research projects.
Course Learning Outcomes The students who succeeded in this course;
  • Upon the completion of this course, the student will be able to: Define the advance econometric techniques,
  • Equilibrium solution by using the advance mathematical techniques. By using this solutions constructing econometric models,
  • have the ability to predict the effects of changes in any kind of policy related to investigated field.
Course Content Statistical inference, regression, generalized least squares, instrumental variables, simultaneous equations models, and evaluation of policies and programs.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Single-Equation Regression Models Two-Variable Regression Model: The Problem of Estimation DG and DCP Chp 1.
2 Classical Normal Linear Regression Model (CNLRM) DG and DCP Chp 2-3.
3 Multiple Regression Analysis: The Problem of Inference DG and DCP Chp 3-8.
4 The Matrix Approach to Linear Regression Model JJ and JD Chp 3.
5 Relaxing the Assumptions of the Classical Model MIDTERM EXAM I DG and DCP Chp 10-13. JJ and JD Chp 6.
6 Nonlinear Regression Models DG and DCP Chp 14.
7 Qualitative Response Regression Models DG and DCP Chp 15. JJ and JD Chp 13.
8 Panel Data Regression Models DG and DCP Chp 16. JJ and JD Chp 12.
9 Dynamic Econometric Models: Autoregressive and Distributed-Lag Models DG and DCP Chp 17. JJ and JD Chp 8.
10 Simultaneous-Equation Models DG and DCP Chp 18-20.
11 Time Series Analysis DG and DCP Chp 21-22. JJ and JD Chp 8-9.
12 Panel Time Series Models SRP and AM
13 Nonlinear Modelling in Time and Panel data analysis TT and GCEJ


Course Book 1. Domador Gujarati, Dawn C. Porter (2015) Introduction to Econometrics McGraw Hill Higher Education; 5th edition
2. Jack Johnston and John Dinardo Econometric Methods. McGraw Hill Higher Education; 4th edition
3. Terasvirta T. and Granger C.E.J Modelling Nonlinear Economic Time Series
4. Smith R.P. and Fuertes A.M. Panel Time Series (2012)

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation 14 10
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation 2 20
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 20
Final Exam/Final Jury 1 50
Toplam 18 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 To compare main microeconomic theories, approaches and make a critical evaluation of each X
2 To compare main macroeconomic theories, approaches and make a critical evaluation of each X
3 To apply mathematical modeling X
4 To employ statistical and econometric tools in analyzing an economic phenomena X
5 To analyze the main economic indicators and comment on them X
6 To acquire theoretical knowledge through literature survey and derive empirically confirmable hypothesis X
7 To make a research design and carry it out within predetermined time frames X
8 To be able to develop new approaches for complex problems in applied economics and/or apply statistical/econometric tools to new areas/problems X
9 To formulate and present policy recommendations based on academic research X
10 To combine economic knowledge with other disciplines in order to solve problems requiring scientific expertise
11 To use information technology effectively X
12 To continue learning and undertake advanced research independently X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 14 3 42
Special Course Internship
Field Work
Study Hours Out of Class 14 3 42
Presentation/Seminar Prepration 1 21 21
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 20 20
Prepration of Final Exams/Final Jury 1 25 25
Total Workload 150