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 | 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 | ||