ECTS - Advance Data Modeling
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) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | N/A |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| 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;
|
| 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 | ||||
|---|---|---|---|---|---|---|
| 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 | ||