Advance Data Modeling (ECON481) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advance Data Modeling ECON481 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type N/A
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery
Learning and Teaching Strategies .
Course Coordinator
Course Lecturer(s)
  • Specialist Bora Güngören
Course Assistants
Course Objectives The main contents are; statistical inference, regression, generalized least squares, instrumental variables, simultaneous equations models, and evaluation of policies and programs. Students will learn the principles and best practices for how to use data in order to support fact-based decision-making. Emphasis will be given to applications in various data which has big data facilities.
Course Learning Outcomes The students who succeeded in this course;
  • Upon the completion of this course, the student will be able to: 1.Define the advance econometric techniques, 2.Equilibrium solution by using the advance mathematical techniques. By using this solutions constructing econometric models. 3.analyze and critically evaluate from oral written, and visual materials.
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) JJ ve JD Bölüm 3.
3 Multiple Regression Analysis: The Problem of Inference DG and DCP Chp 3-8.
4 The Matrix Approach to Linear Regression Model DG and DCP Chapter 14.
5 Relaxing the Assumptions of the Classical Model DG and DCP Chp 10-13. JJ and JD Chp 6.
6 MIDTERM EXAM I
7 Nonlinear Regression Models DG and DCP Chapter 16. JJ and JD Chapter 12.
8 Niteliksel Tepki Regresyon Modelleri DG and DCP Chp. 15. JJ and JD Chp 13.
9 Panel Data Regression Models DG and DCP Chp. 18-20.
10 Dynamic Econometric Models: Autoregressive and Distributed-Lag Models DG and DCP Chp. 21-22. JJ and JD Chp 8-9
11 Eşanlı Denklemler DG and DCP Chp 18-20.
12 Time Series Analysis DG and DCP Chp 21-22. JJ and JD Chp 8-9.
13 Panel Time Series Models DG and DCP Chp. 3-8
14 Nonlinear Modelling in Time and Panel data analysis JJ and JD Chp. 3.
15 Nonlinear Modelling in Time and Panel data analysis JJ and JD Chp. 3.
16 Final Exam

Sources

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

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation 1 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 5 100
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 Acquiring the skills of understanding, explaining, and using the fundamental concepts and methods of economics
2 Acquiring the skills of macro level economic analysis
3 Acquiring the skills of micro level economic analysis
4 Understanding the formulation and implementation of economic policies at the local, national, regional, and/or global level
5 Learning different approaches on economic and related issues X
6 Acquiring the quantitative and/or qualitative techniques in economic analysis X
7 Improving the ability to use the modern software, hardware and/or technological devices X
8 Developing intra-disciplinary and inter-disciplinary team work skills X
9 Acquiring an open-minded behavior through encouraging critical analysis, discussions, and/or life-long learning X
10 Adopting work ethic and social responsibility
11 Developing the skills of communication.
12 Improving the ability to effectively implement the knowledge and skills in at least one of the following areas: economic policy, public policy, international economic relations, industrial relations, monetary and financial affairs.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration 1 21 21
Project
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 20 20
Total Workload 147