ECON506 Applied Econometrics
ECON506 Applied Econometrics
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
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Economics
Edo Omerčević
Course Lecturer
Course Objectives
This course aims to equip students with a practical foundation in econometrics, focusing on the implementation of econometric techniques and the critical evaluation of empirical research in economics.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Gujarati, D. (2014), Econometrics by Example, 2 Edition, Palgrave Macmillan
Additional Literature
Basic Econometrics, Gujarati and Porter (2013). 5th edition, McGraw-HillTeaching Methods
The methods include lectures (with PowerPoint
Video
And audio aids)
Student presentations
Projects
And hands-on econometric software applications in class.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introductory Lecture | |
| 2 | The linear regression models | Chapter 1 |
| 3 | Functional forms of regression models; Qualitative explanatory variables regression models | Chapters 2 & 3 |
| 4 | Holiday | |
| 5 | Test 1 | |
| 6 | Regression diagnostics: Model specification errors | Chapter 7 |
| 7 | Regression diagnostics: Multicollinearity | Chapters 4 |
| 8 | Midterm Exam | |
| 9 | Regression diagnostics: Heteroscedasticity & Autocorrelation | Chapter 5 & 6 |
| 10 | Holiday | |
| 11 | The logit and probit models | Chapter 8 |
| 12 | Test 2 | |
| 13 | Ordinal regression models | Chapter 10 |
| 14 | Panel data regression models | Chapter 17 |
| 15 | Presentations |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| ECON506.1 | Course | Thursday 17:00 - 19:50 | B F1.2 - Class/ECON Lab | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Monday | 15:00 - 16:00 | B F1.12 | |
| Wednesday | 12:00 - 13:00 | B F1.12 | |
| Thursday | 12:00 - 13:00 | B F1.12 | |
| Friday | 16:00 - 17:00 | B F1.12 | For Postgraduate students only |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Tests
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Project
AI: Consult InstructorAlignment with Learning Outcomes : 1 2 3
Participation
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture Hours
45 hours ⏳ (15 week × 3 h)
Tests
16 hours ⏳ (2 week × 8 h)
Project
15 hours ⏳ (3 week × 5 h)
Final Exam Study
30 hours ⏳ (1 week × 30 h)
Home Study
30 hours ⏳ (15 week × 2 h)
Midterm Exam Study
14 hours ⏳ (1 week × 14 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [ECON506] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Feb 02, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| ECON506 | Applied Econometrics | 3 | 0 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Edo Omerčević | Office Hours / Room / Phone | Monday: 11:00-12:00 , 15:00-16:00 Wednesday: 12:00-13:00 Thursday: 12:00-13:00 Friday: 16:00-17:00 For Postgraduate students only |
|||||||
| eomercevic@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | This course aims to equip students with a practical foundation in econometrics, focusing on the implementation of econometric techniques and the critical evaluation of empirical research in economics. | |||||||||
| Textbook | Gujarati, D. (2014), Econometrics by Example, 2 Edition, Palgrave Macmillan | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | The methods include lectures (with PowerPoint, video, and audio aids), student presentations, projects, and hands-on econometric software applications in class. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introductory Lecture | |||||||||
| Week 2 | The linear regression models | Chapter 1 | ||||||||
| Week 3 | Functional forms of regression models; Qualitative explanatory variables regression models | Chapters 2 & 3 | ||||||||
| Week 4 | Holiday | |||||||||
| Week 5 | Test 1 | |||||||||
| Week 6 | Regression diagnostics: Model specification errors | Chapter 7 | ||||||||
| Week 7 | Regression diagnostics: Multicollinearity | Chapters 4 | ||||||||
| Week 8 | Midterm Exam | |||||||||
| Week 9 | Regression diagnostics: Heteroscedasticity & Autocorrelation | Chapter 5 & 6 | ||||||||
| Week 10 | Holiday | |||||||||
| Week 11 | The logit and probit models | Chapter 8 | ||||||||
| Week 12 | Test 2 | |||||||||
| Week 13 | Ordinal regression models | Chapter 10 | ||||||||
| Week 14 | Panel data regression models | Chapter 17 | ||||||||
| Week 15 | Presentations | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 1,2,3 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 30 | 1,2,3 | Not Allowed | |
| Tests | 2 | 20 | 1,2,3 | Not Allowed | |
| Project | 1 | 15 | 1,2,3 | Consult Instructor | |
| Participation | 1 | 5 | 1,2,3 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Tests | 8 | 2 | 16 | |||
| Project | 5 | 3 | 15 | Final Exam Study | 30 | 1 | 30 | |||
| Home Study | 2 | 15 | 30 | Midterm Exam Study | 14 | 1 | 14 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 20/02/2026 | |||||||||