|
Code
|
Semester
|
Theory
(hour/week)
|
Application/Lab
(hour/week)
|
Local Credits
|
ECTS
|
|
ECON 301
|
Fall/Spring
|
3
|
0
|
3
|
6
|
|
Prerequisites
|
| |
ECON 101 To succeed (To get a grade of at least DD) |
| and |
ECON 102 To succeed (To get a grade of at least DD) |
|
|
Course Language
|
English
|
|
Course Type
|
Elective
|
|
Course Level
|
First Cycle
|
|
Mode of Delivery
|
- |
|
Teaching Methods and Techniques of the Course
|
- |
|
National Occupation Classification
|
-
|
|
Course Coordinator
|
|
|
Course Lecturer(s)
|
|
|
Assistant(s)
|
|
|
Course Objectives
|
The main objective of the course is to improve the students’ basic statistical knowledge to conduct advanced level econometric analysis. More specifically, it aims to give extensive background on econometric techniques, their implementation and usage in a high level statistical package (R-studio). Each student is required to prepare a project to show their skills developed in this course. |
|
Learning Outcomes
|
The students who succeeded in this course;
- Will be able to collect data related to social and economic topics.
- Will be able to get raw data ready for statistical and econometric analysis.
- Will be able to build econometric models that describe the data generating process behind data.
- Will be able to interpret the results that are obtained through econometric analysis.
- Will be able to conduct an independent empirical research project from start to finish.
- Will be able to use an econometrics software (Eviews) to make statistical and econometric analysis.
|
|
Course Description
|
Econometrics can be defined as the “application of statistics to the analysis of economic phenomena”. The knowledge of econometrics is essential to test economic theories and to understand empirical work being done in Economics. The course will teach how to do empirical work by using examples drawn from various fields in economics. It will also focus on various types of economic data, how one can obtain them, and how they may be used. Topics include regression analysis, ordinary least squares, hypothesis testing, choosing independent variables and functional form, multicollinearity, serial correlation and heteroskedasticity. To aid in empirical work Eviews will be used. |
|
Related Sustainable Development Goals
|
|
|
|
Core Courses |
|
| Major Area Courses |
|
| Supportive Courses |
X
|
| Media and Management Skills Courses |
|
| Transferable Skill Courses |
|
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
|
Week |
Subjects |
Related Preparation |
| 1 |
Mathematical and Statistical Foundations |
HGL, Ch 1 & Appendix B, C |
| 2 |
Introduction to Eviews 1 |
GHL, Ch 1 |
| 3 |
Introduction to Eviews 2 |
GHL, Ch 1 |
| 4 |
Simple Regression Model |
HGL, Ch 2 & GHL Ch 2 |
| 5 |
Interval Estimation and Hypothesis Testing |
HGL, Ch 3 & GHL Ch 3 |
| 6 |
Estimation and Goodness-of-Fit |
HGL, Ch 4 & GHL Ch 4 |
| 7 |
Multivariate Regression Model 1 |
HGL, Ch 5 & GHL Ch 5 |
| 8 |
Midterm Exam |
|
| 9 |
Multivariate Regression Model 2 |
HGL, Ch 6 & GHL Ch 6 |
| 10 |
Dummy Variable Model |
HGL, Ch 7 & GHL Ch 7 |
| 11 |
Heteroskedasticity |
HGL, Ch 8 & GHL Ch 8 |
| 12 |
Multicollinearity |
Stock and Watson, Ch 6-7 |
| 13 |
Time Series: Stationarity |
HGL, Ch 9 & GHL Ch 9 |
| 14 |
Autocorrelation |
HGL, Ch 9 & GHL Ch 9 |
| 15 |
Qualitative Variables |
HGL, Ch 16 & GHL Ch 16 |
| 16 |
Additional Topic(s) (Optional and Time Permitting) |
|
|
Course Notes/Textbooks
|
C.Hill, W. Griffith and G. Lim: (HGL). (2018). Principles of Econometrics. 5th Edition. Wiley.
W.Griffith, C.Hill and G.Lim: (GHL). (2018). Using EViews For Principles of
Econometrics. 5th Edition. Wiley
Stock, J. H., & Watson, M. W. (2020). Introduction to Econometrics. 4th
Edition, Global Edition: Pearson.
|
|
Suggested Readings/Materials
|
Peter E. Kennedy, A Guide to Econometrics (5th Edition)
|
EVALUATION SYSTEM
|
Semester Activities
|
Number |
Weigthing |
| Participation |
16
|
10
|
| Laboratory / Application |
-
|
-
|
| Field Work |
-
|
-
|
| Quizzes / Studio Critiques |
-
|
-
|
| Portfolio |
-
|
-
|
| Homework / Assignments |
-
|
-
|
| Presentation / Jury |
-
|
-
|
| Project |
1
|
20
|
| Seminar / Workshop |
-
|
-
|
| Oral Exams |
-
|
-
|
| Midterm |
1
|
30
|
| Final Exam |
1
|
40
|
| Total |
19
|
100
|
| Weighting of Semester Activities on the Final Grade |
18
|
60
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
| Total |
19 |
100 |
ECTS / WORKLOAD TABLE
|
Semester Activities
|
Number |
Duration (Hours) |
Workload |
Theoretical Course Hours
(Including exam week: 16 x total hours) |
16
|
3
|
48
|
Laboratory / Application Hours
(Including exam week: '.16.' x total hours) |
-
|
-
|
-
|
| Study Hours Out of Class |
16
|
3
|
48
|
| Field Work |
-
|
-
|
-
|
| Quizzes / Studio Critiques |
-
|
-
|
-
|
| Portfolio |
-
|
-
|
-
|
| Homework / Assignments |
-
|
-
|
-
|
| Presentation / Jury |
-
|
-
|
-
|
| Project |
1
|
24
|
24
|
| Seminar / Workshop |
-
|
-
|
-
|
| Oral Exam |
-
|
-
|
-
|
| Midterms |
1
|
30
|
30
|
| Final Exam |
1
|
30
|
30
|
| |
|
Total |
180
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes
|
* Contribution Level
|
|
1
|
2
|
3
|
4
|
5
|
|
1 |
To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.
|
-
|
-
|
-
|
-
|
-
|
|
2 |
To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.
|
-
|
-
|
-
|
-
|
-
|
|
3 |
To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.
|
-
|
-
|
-
|
-
|
-
|
|
4 |
To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.
|
-
|
-
|
-
|
-
|
X
|
|
5 |
To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.
|
-
|
-
|
-
|
-
|
X
|
|
6 |
To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.
|
-
|
-
|
-
|
X
|
-
|
|
7 |
To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.
|
-
|
-
|
-
|
-
|
-
|
|
8 |
To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.
|
-
|
-
|
-
|
-
|
-
|
|
9 |
To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.
|
-
|
-
|
X
|
-
|
-
|
|
10 |
To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.
|
-
|
-
|
-
|
-
|
-
|
|
11 |
To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.
|
-
|
-
|
-
|
-
|
-
|
|
12 |
To be able to speak a second foreign language at a medium level of fluency efficiently.
|
-
|
-
|
-
|
-
|
-
|
|
13 |
To be able to relate the knowledge accumulated throughout the human history to their field of expertise.
|
-
|
-
|
-
|
-
|
-
|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
