ECON301 Econometrics I
ECON301 Econometrics I
Syllabus | International University of Sarajevo - Last Update on Jan 01, 2026
Syllabus Quick Jump
Search and navigate to any syllabus instantly
Economics
Edo Omerčević
Course Lecturer
Course Objectives
The goal of this course is to provide students with a foundation in econometrics, develop practical skills in regression analysis and diagnostics, and enable critical evaluation of empirical studies.
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
1. Wooldridge, J. M. (2020). Introductory Econometrics: A Modern Approach, (Seventh Edition), Cengage Learning. 2. Gujarati, D. N. & Porter, D. C. (2009). Basic Econometrics. McGraw-Hill / Special Readings. 3. Bruce E. Hansen. (2021). Econometrics. Princeton University Press https://www.ssc.wisc.edu/~bhansen/econometrics/Teaching Methods
Lectures with problem demonstrations
Tutorial sessions for guided practice
Class discussions
Exam-style reviews
And a team project applying learned concepts to real-world data
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introductory Lecture | |
| 2 | The linear regression models | Chapter 1 |
| 3 | Functional forms of regression models | Chapter 2 |
| 4 | Test 1 | |
| 5 | Functional forms of regression models (cont.) | Chapter 2 |
| 6 | Qualitative explanatory variables regression models | Chapter 3 |
| 7 | Qualitative explanatory variables regression models (cont.) | Chapter 3 |
| 8 | Midterm exam | |
| 9 | Site visit: Agency for Statistics of Bosnia and Herzegovina | |
| 10 | Regression diagnostics: Multicollinearity | Chapter 4 |
| 11 | Regression diagnostics: Heteroscedasticity | Chapter 5 |
| 12 | Test 2 | |
| 13 | Regression diagnostics: Autocorrelation | Chapter 6 |
| 14 | Regression diagnostics: Model specification errors | Chapter 7 |
| 15 | Student presentations |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| ECON301.1 | Course | Wednesday 12:00 - 14:50 | B F1.2 - Class/ECON Lab | - | - |
| ECON301.1 | Tutorial | Thursday 13:00 - 14:50 | 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 : 2 3 4 5
Test
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Group Project & Presentation
AI: Not AllowedAlignment with Learning Outcomes : 2 3 4 5
Attendance & Participation
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
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)
Tutorials
30 hours ⏳ (15 week × 2 h)
Tests Study
20 hours ⏳ (2 week × 10 h)
Midterm Exam Study
20 hours ⏳ (1 week × 20 h)
Final Exam Study
20 hours ⏳ (1 week × 20 h)
Group project & Presentation
15 hours ⏳ (1 week × 15 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 [ECON301] 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 Jan 01, 2026 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| ECON301 | Econometrics I | 3 | 2 | 6 | ||||||
| Prerequisite | ECON221 | It is a prerequisite to | ECON302 | |||||||
| 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 | Anes Kadić | Assistant E-mail | akadic@ius.edu.ba | |||||||
| Course Objectives | The goal of this course is to provide students with a foundation in econometrics, develop practical skills in regression analysis and diagnostics, and enable critical evaluation of empirical studies. | |||||||||
| 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 | Lectures with problem demonstrations, tutorial sessions for guided practice, class discussions, exam-style reviews, and a team project applying learned concepts to real-world data | |||||||||
| 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 | Chapter 2 | ||||||||
| Week 4 | Test 1 | |||||||||
| Week 5 | Functional forms of regression models (cont.) | Chapter 2 | ||||||||
| Week 6 | Qualitative explanatory variables regression models | Chapter 3 | ||||||||
| Week 7 | Qualitative explanatory variables regression models (cont.) | Chapter 3 | ||||||||
| Week 8 | Midterm exam | |||||||||
| Week 9 | Site visit: Agency for Statistics of Bosnia and Herzegovina | |||||||||
| Week 10 | Regression diagnostics: Multicollinearity | Chapter 4 | ||||||||
| Week 11 | Regression diagnostics: Heteroscedasticity | Chapter 5 | ||||||||
| Week 12 | Test 2 | |||||||||
| Week 13 | Regression diagnostics: Autocorrelation | Chapter 6 | ||||||||
| Week 14 | Regression diagnostics: Model specification errors | Chapter 7 | ||||||||
| Week 15 | Student presentations | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Test | 2 | 10 | 1,2,3,4,5 | Not Allowed | |
| Midterm Exam | 1 | 30 | 1,2,3 | Not Allowed | |
| Group Project & Presentation | 1 | 20 | 2,3,4,5 | Not Allowed | |
| Attendance & Participation | 1 | 10 | 1,2,3,4,5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Tutorials | 2 | 15 | 30 | |||
| Tests Study | 10 | 2 | 20 | Midterm Exam Study | 20 | 1 | 20 | |||
| Final Exam Study | 20 | 1 | 20 | Group project & Presentation | 15 | 1 | 15 | |||
| 0 | 0 | |||||||||
| 0 | 0 | |||||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 29/01/2026 | |||||||||