Advance Data Modeling (ECON552) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Advance Data Modeling	ECON552	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Elective Courses
Course Level	Natural & Applied 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
14	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
	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
#	Program Qualifications / Competencies	1	2	3	4	5
1	Has the ability to apply scientific knowledge gained in the undergraduate education and to expand and extend knowledge in the same or in a different area.
2	Has the ability to obtain, to evaluate, to interpret and to apply information by doing scientific research.
3	Can apply gained knowledge and problem solving abilities in inter-disciplinary research.
4	Has the ability to work independently within research area, to state the problem, to develop solution techniques, to solve the problem, to evaluate the obtained results and to apply them when necessary.
5	Takes responsibility individually and as a team member to improve systematic approaches to produce solutions in unexpected complicated situations related to the area of study.
6	Can develop strategies, implement plans and principles on the area of study and can evaluate obtained results within the framework.
7	Can develop and extend the knowledge in the area and to use them with scientific, social and ethical responsibility.
8	Has the ability to follow recent developments within the area of research, to support research with scientific arguments and data, to communicate the information on the area of expertise in a systematically by means of written report and oral/visual presentation.
9	Has proficiency in English language and has the ability to communicate with colleagues and to follow the innovations in mathematics and related fields.
10	Has software and hardware knowledge in the area of expertise, and has proficient information and communication technology knowledge.
11	Has professional ethical consciousness and responsibility which takes into account the universal and social dimensions in the process of data collection, interpretation, implementation and declaration of results in mathematics and its applications.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	14	3	42
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	14	3	42
Presentation/Seminar Prepration	1	21	21
Project
Report
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