ECTS - Econometrics II
Econometrics II (ECON302) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Econometrics II | ECON302 | General Elective | 3 | 0 | 0 | 3 | 6 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Demonstration. |
| Course Lecturer(s) |
|
| 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;
|
| 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 |
|---|---|---|
| 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) Basic Econometrics, 4th Edition, New York and Boston: McGraw-Hill |
|---|---|
| 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 | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 40 |
| Final Exam/Final Jury | 1 | 50 |
| Toplam | 3 | 100 |
| Percentage of Semester Work | 70 |
|---|---|
| Percentage of Final Work | 30 |
| 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 | Can apply gained knowledge and problem solving abilities in inter-disciplinary research | |||||
| 3 | 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 | |||||
| 4 | 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 | |||||
| 5 | Can develop strategies, implement plans and principles on the area of study and can evaluate obtained results within the framework | |||||
| 6 | Can develop and extend the knowledge in the area and to use them with scientific, social and ethical responsibility | |||||
| 7 | 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 | |||||
| 8 | To have an oral and written communication ability in at least one of the common foreign languages ("European Language Portfolio Global Scale", Level B2) | |||||
| 9 | Has software and hardware knowledge in the area of expertise, and has proficient information and communication technology knowledge | |||||
| 10 | Follows scientific, cultural, and ethical criteria in collecting, interpreting and announcing data in the research area and has the ability to teach. | |||||
| 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) | 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 | ||