ECTS - Advance Data Modeling
Advance Data Modeling (ECON481) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Advance Data Modeling | ECON481 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | |
| Learning and Teaching Strategies | . |
| Course Lecturer(s) |
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| 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;
|
| 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 | They acquire the skills to understand, explain, and use the basic concepts and methods of economics. | |||||
| 2 | Acquires macro-economic analysis skills. | |||||
| 3 | Acquire microeconomic analysis skills. | |||||
| 4 | Understands the formulation and implementation of economic policies at local, national, regional and/or global levels. | |||||
| 5 | Learn different approaches to the economy and economic issues. | X | ||||
| 6 | Learn qualitative and quantitative research techniques in economic analysis. | X | ||||
| 7 | Improving the ability to use modern software, hardware and/or other technological tools. | X | ||||
| 8 | Develops intra-disciplinary and inter-disciplinary team work skills. | X | ||||
| 9 | Contributes to open-mindedness by encouraging critical analysis, discussion, and/or lifelong learning. | X | ||||
| 10 | Develops a sense of work ethics and social responsibility. | |||||
| 11 | Develops communication skills. | |||||
| 12 | Improving the ability to effectively apply knowledge and skills in at least one of the following areas: Economic policy, public policy, international economic relations, industrial relations, monetary and financial relations | |||||
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 | ||