ECTS - Econometrics I
Econometrics I (ECON301) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Econometrics I | ECON301 | 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 | The aim of this course is to introduce students to the study of econometrics, which deals with the application of statistical methods to test economic theory. Econometrics uses observational data to estimate economic relationships, test hypotheses about economic behaviour, and predict future values of economic variables. Software applications are introduced during the course in order to provide hands-on experience with data collection, analysis and interpretation. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Review of basic statistics; simple regression, tests of hypothesis; prediction; assessing goodness of fit; assumptions of the classical linear regression model; transformation of variables; estimation and inference in the multiple regression model. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Review of Basic Statistics - Descriptive Statistics, Probability and Random variables; Introduction – The Methodology of Economics | Gujarati, Introduction: pp. 1-13 |
| 2 | The Nature of Regression Analysis – Causation, Correlation and Types of Data | Gujarati, Chapter 1: pp. 15-32 |
| 3 | Two Variable Regression Model: Some Basic Ideas | Gujarati, Chapter 2: ss. 37-52 |
| 4 | Two Variable Regression Model: The Problem of Estimation | Gujarati, Chapter 3: pp. 58-105 |
| 5 | Two Variable Regression Model: The Problem of Estimation | Gujarati, Chapter 3: pp. 58-105 |
| 6 | The Normality Assumption: Classical Normal Linear Regression Model | Gujarati, Chapter 4: pp. 107-113 |
| 7 | Two-Variable Regression Model: Interval Estimation and Hypothesis Testing | Gujarati, Chapter 5: pp. 119-133 |
| 8 | Two-Variable Regression Model: Interval Estimation and Hypothesis Testing | Gujarati, Chapter 5: pp. 134-150 |
| 9 | MIDTERM EXAM | |
| 10 | Introduction to Eviews | Class Handouts |
| 11 | Extensions of the Two-Variable Regression Model: Scaling, Functional Forms | Gujarati, Chapter 6: pp. 164-193 |
| 12 | Multiple Regression Model: The Problem of Estimation | Gujarati, Chapter 7: pp. 202-232 |
| 13 | Multiple Regression Model: The Problem of Inference | Gujarati, Chapter 8: pp. 248-263 |
| 14 | Multiple Regression Model: The Problem of Inference | Gujarati, Chapter 8: pp. 264-280 |
| 15 | General 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 | 30 |
| Final Exam/Final Jury | 1 | 45 |
| Toplam | 3 | 85 |
| Percentage of Semester Work | 55 |
|---|---|
| Percentage of Final Work | 45 |
| 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 | ||