ECTS - Applied Econometrics
Applied Econometrics (ECON521) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Applied Econometrics | ECON521 | General Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | Turkish |
|---|---|
| Course Type | Elective Courses |
| Course Level | Social Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Discussion, Question and Answer, Drill and Practice, Problem Solving. |
| 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 | Modeling linear regressions, bivariate and multivariate regression techniques and their applications, model specification problems, parameter estimation problems, nonlinear regression models, data handling problems, simultenaous equation models, restricted regression models, time series, nonstationary series and autocorrelation and panel data. |
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: pp. 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: 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. |
|---|---|
| 2. Gujarati, Damodar N. (2003) Temel Ekonometri, Literatür Kitabevi, McGraw-Hill. | |
| Other Sources | 3. Wooldridge, Jeffrey (2008) Introductory Econometrics: A Modern Approach (with Economic Applications), 4th Edition, Cengage Learning. |
| 4. Peter J. Kennedy (1998) A Guide to Econometrics, 4th Edition, MIT Press. | |
| 5. Ramanathan, R. (2002), Introductory Econometrics with Applications, 5th edition, Orlando, FL: Harcourt College Publishers. | |
| 6. Hill, R.C., Griffiths, W.E. and G. G. Judge (2001) Undergraduate Econometrics, 2nd Edition, John Wiley and Sons, Inc. | |
| 7. Hill, R.C., Griffiths, W.E. and G. G. Judge (2000) Using Eviews For Undergraduate Econometrics, 2nd Edition, Wiley. | |
| 8. Asteriou, D. (2006) Applied Econometrics: A Modern Approach using EViews and Microfit, Palgrave-Macmillan. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 5 | 25 |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 30 |
| Final Exam/Final Jury | 1 | 45 |
| Toplam | 7 | 100 |
| Percentage of Semester Work | |
|---|---|
| Percentage of Final Work | 100 |
| 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 | Can compare basic microeconomic theories and approaches and evaluate them with a critical perspective" | X | ||||
| 2 | Can compare basic macroeconomic theories and approaches and evaluate them with a critical perspective | X | ||||
| 3 | Applies mathematical modeling | X | ||||
| 4 | Analyzes economic phenomena using statistical and econometric methods | X | ||||
| 5 | Can analyze and interpret basic economic indicators | X | ||||
| 6 | Can access theoretical knowledge by conducting literature review and formulate an empirically verifiable hypothesis | X | ||||
| 7 | Can design a research project and conduct the research within the specified time frame | X | ||||
| 8 | Can develop new approaches for solving complex problems in the field of applied economics | X | ||||
| 9 | Develops and can recommend appropriate policies based on academic research results | X | ||||
| 10 | Can evaluate by combining economic knowledge with information obtained from other disciplines to solve problems | X | ||||
| 11 | Can use information technology effectively | X | ||||
| 12 | Acquires the ability to conduct independent research and learn | X | ||||
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 | 4 | 64 |
| 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 | 3 | 3 |
| Total Workload | 117 | ||