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 | Acquires skills to use the advanced theoretical and applied knowledge obtained at the mathematics bachelors program to do further academic and scientific research in both mathematics-based graduate programs and public or private sectors. | |||||
| 2 | Transplants and applies the theoretical and applicable knowledge gained in their field to the secondary education by using suitable tools and devices. | |||||
| 3 | Acquires the skill of choosing, using and improving problem solving techniques which are needed for modeling and solving current problems in mathematics or related fields by using the obtained knowledge and skills. | |||||
| 4 | Acquires analytical thinking and uses time effectively in the process of deduction. | |||||
| 5 | Acquires basic software knowledge necessary to work in the computer science related fields and together with the skills to use information technologies effectively. | |||||
| 6 | Obtains the ability to collect data, to analyze, interpret and use statistical methods necessary in decision making processes. | |||||
| 7 | Acquires the level of knowledge to be able to work in the mathematics and related fields and keeps professional knowledge and skills up-to-date with awareness in the importance of lifelong learning. | |||||
| 8 | Takes responsibility in mathematics related areas and has the ability to work affectively either individually or as a member of a team. | |||||
| 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 the ability to communicate ideas with peers supported by qualitative and quantitative data. | |||||
| 11 | Has professional and 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 | ||