EPOKA UNIVERSITY
FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES
DEPARTMENT OF ECONOMICS
COURSE SYLLABUS
COURSE INFORMATIONCourse Title: ECONOMETRICS I |
| Code | Course Type | Regular Semester | Theory | Practice | Lab | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| ECO 311 | B | 5 | 4 | 0 | 0 | 4 | 5 |
| Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature) | NA |
| Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours: | Uğur Ergün , on Tuesdays 09:00 to 11:00 |
| Second Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours: | Fatbardha Morina |
| Teaching Assistant(s) and Office Hours: | NA |
| Language: | English |
| Compulsory/Elective: | Compulsory |
| Classroom and Meeting Time: | E-212, Every Monday between 11:30 and 13:15, Tuesday between 10:30 and 12:15 |
| Course Description: | Econometrics I: Techniques of Econometrics, estimating the basic linear model and hypothesis testing, empirical illustrations to contemporary economic issues |
| Course Objectives: | The primary objective of this course is to teach students fundamental econometric techniques in a highly empirical but theoretically rigorous context. Course presents an applied introduction to econometric techniques with some derivations of their properties, but leaves more theoretical treatment to future courses. For all groups, the course provides practical experience in the use of econometric software EVIEWS. |
|
COURSE OUTLINE
|
| Week | Topics |
| 1 | Introduction, Basic Data Handling |
| 2 | The Nature of Econometrics and Economic Data |
| 3 | The Simple Regression Model |
| 4 | The Simple Regression Model |
| 5 | Multiple Regression: Estimation |
| 6 | Multiple Regression Analysis: Estimation |
| 7 | Multiple Regression Analysis: Inference |
| 8 | Midterm Exam |
| 9 | Multiple Regression Analysis: Inference |
| 10 | Multiple Regression Analysis: Further Issues |
| 11 | Multiple Regression Analysis: Further Issues |
| 12 | Multiple Regression Analysis with Qualitative Information: Binary Variables |
| 13 | Multiple Regression Analysis with Qualitative Information: Binary Variables |
| 14 | Review |
| Prerequisite(s): | NA |
| Textbook: | Introduction to Econometrics, J.M. Wooldridge, Cengage Learning Analysis of Economic Data. Gary Koop, Second Edition. John Wiley & Sons. Applied Econometrics with Eviews Applications. Ergun, Ugur and Goksu, Ali. IBU Publication |
| Other References: | NA |
| Laboratory Work: | 2 hours per week |
| Computer Usage: | Eviews software program |
| Others: | No |
|
COURSE LEARNING OUTCOMES
|
| 1 | provide skills to apply the basic econometric techniques. |
| 2 | provide skills to set up econometric model. |
| 3 | Provide ability for testing specification of the model. |
| 4 | Provide ability to apply fundamental econometric principles to real life and scientific problems |
|
COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES
(Blank : no contribution, 1: least contribution ... 5: highest contribution) |
| No | Program Competencies | Cont. |
| Bachelor in Business Informatics (3 years) Program | ||
| 1 | Identify activities, tasks, and skills in management, marketing, accounting, finance, and economics. | 3 |
| 2 | Apply key theories to practical problems within the global business context. | 5 |
| 3 | Demonstrate ethical, social, and legal responsibilities in organizations. | 4 |
| 4 | Develop an open minded-attitude through continuous learning and team-work. | 4 |
| 5 | Integrate different skills and approaches to be used in decision making and data management. | 5 |
| 6 | Combine computer skills with managerial skills, in the analysis of large amounts of data. | 5 |
| 7 | Provide solutions to complex information technology problems. | 4 |
| 8 | Recognize, analyze, and suggest various types of information-communication systems/services that are encountered in everyday life and in the business world. | 4 |
|
COURSE EVALUATION METHOD
|
| Method | Quantity | Percentage |
| Midterm Exam(s) |
1
|
20
|
| Lab/Practical Exams(s) |
2
|
20
|
| Final Exam |
1
|
30
|
| Other |
1
|
10
|
| Total Percent: | 100% |
|
ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)
|
| Activities | Quantity | Duration(Hours) | Total Workload(Hours) |
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 4 | 64 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 2 | 32 |
| Mid-terms | 1 | 6 | 6 |
| Assignments | 0 | ||
| Final examination | 1 | 10 | 10 |
| Other | 1 | 13 | 13 |
|
Total Work Load:
|
125 | ||
|
Total Work Load/25(h):
|
5 | ||
|
ECTS Credit of the Course:
|
5 | ||