ECTS - Econometrics II
Econometrics II (IKT302) Course Detail
Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
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Econometrics II | IKT302 | 6. Semester | 3 | 0 | 0 | 3 | 6 |
Pre-requisite Course(s) |
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N/A |
Course Language | Turkish |
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Course Type | Compulsory Departmental Courses |
Course Level | Bachelor’s Degree (First Cycle) |
Mode of Delivery | Face To Face |
Learning and Teaching Strategies | Lecture, Drill and Practice. |
Course Lecturer(s) |
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Course Objectives | This course is a continuation of ECON 301, which set out the basic assumptions of the classical linear regression model (CLRM). The assumptions of the CLRM are usually not satisfied in econometric applications. This course will look at: the detection and consequences of violations of the CLRM including multicollinearity, heteroskedasticity, autocorrelation, and model misspecification, as well as a selection of further topics in econometrics including model specification, diagnostic testing. Applications to real world data are emphasized to illustrate the concepts introduced in the course |
Course Learning Outcomes |
The students who succeeded in this course;
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Course Content | Review of regression and hypothesis testing; dummy variable regression models; multicollinearity; heteroskedasticity; autocorrelation; model misspecification; model selection criteria; outlier analysis. |
Weekly Subjects and Releated Preparation Studies
Week | Subjects | Preparation |
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1 | Review of material learned in ECON 301 | Class Handouts |
2 | Dummy Variable Regression Models | Gujarati, Chapter 9: pp. 297-309 |
3 | Special Applications of Dummy Variables | Gujarati, Chapter 9: pp. 310-323 |
4 | Nature and Consequences of Multicollinearity | Gujarati, Chapter 10: pp. 335-358 |
5 | Multicollinearity: Detection and Remedial Measures | Gujarati, Chapter 10: pp. 359-375 |
6 | EViews Applications | Class Handouts |
7 | MIDTERM EXAM | |
8 | Nature and Consequences of Heteroskedasticity | Gujarati, Chapter 11: pp. 387-400 |
9 | Heteroskedasticity: Detection and Remedial Measures | Gujarati, Chapter 11: pp. 400-428 |
10 | Nature and Consequences of Autocorrelation | Gujarati, Chapter 12: pp. 441-461 |
11 | Autocorrelation: Detection and Remedial Measures | Gujarati, Chapter 12: pp. 462-489 |
12 | Econometric Modeling: Model Misspecification, Model Selection Criteria | Gujarati, Chapter 13: pp. 506-529 |
13 | Econometric Modeling: Diagnostic Testing and Outlier Analysis | Gujarati, Chapter 13: pp. 530-547 |
14 | EViews Applications | Class Handouts |
15 | Review | |
16 | Final Exam |
Sources
Course Book | 1. Gujarati, Damodar N. (2003) Temel Ekonometri, Literatür Kitabevi, McGraw-Hill. |
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Other Sources | 2. Wooldridge, Jeffrey (2008) Introductory Econometrics: A Modern Approach (with Economic Applications), 4th Edition, Cengage Learning. |
3. Peter J. Kennedy (1998) A Guide to Econometrics, 4th Edition, MIT Press. | |
4. Ramanathan, R. (2002), Introductory Econometrics with Applications, 5th edition, Orlando, FL: Harcourt College Publishers. | |
5. Hill, R.C., Griffiths, W.E. and G. G. Judge (2001) Undergraduate Econometrics, 2nd Edition, John Wiley and Sons, Inc. | |
6. Hill, R.C., Griffiths, W.E. and G. G. Judge (2000) Using Eviews For Undergraduate Econometrics, 2nd Edition, Wiley. | |
7. Asteriou, D. (2006) Applied Econometrics: A Modern Approach using EViews and Microfit, Palgrave-Macmillan. |
Evaluation System
Requirements | Number | Percentage of Grade |
---|---|---|
Attendance/Participation | 1 | 10 |
Laboratory | - | - |
Application | - | - |
Field Work | - | - |
Special Course Internship | - | - |
Quizzes/Studio Critics | - | - |
Homework Assignments | - | - |
Presentation | - | - |
Project | 1 | 20 |
Report | - | - |
Seminar | 1 | 30 |
Midterms Exams/Midterms Jury | - | - |
Final Exam/Final Jury | 1 | 30 |
Toplam | 4 | 90 |
Percentage of Semester Work | 70 |
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Percentage of Final Work | 30 |
Total | 100 |
Course Category
Core Courses | X |
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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 | ||||
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1 | 2 | 3 | 4 | 5 | ||
1 | They acquire the skills to understand, explain, and use the basic concepts and methods of economics. | X | ||||
2 | Acquires macro-economic analysis skills. | X | ||||
3 | Acquire microeconomic analysis skills. | X | ||||
4 | Understands the formulation and implementation of economic policies at local, national, regional and/or global levels. | X | ||||
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. | X | ||||
11 | Develops communication skills. | X | ||||
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 | X |
ECTS/Workload Table
Activities | Number | Duration (Hours) | Total Workload |
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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 | 6 | 96 |
Presentation/Seminar Prepration | |||
Project | |||
Report | |||
Homework Assignments | |||
Quizzes/Studio Critics | |||
Prepration of Midterm Exams/Midterm Jury | 1 | 2 | 2 |
Prepration of Final Exams/Final Jury | 1 | 2 | 2 |
Total Workload | 148 |