ECON302 Econometrics II
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Economics
Course Objectives
This aim of this course is to extend the methods and techniques employed in Econometrics I and to allow for causal inference and prediction in a larger variety of data sets. The overall objective is for the student to learn how to critically examine economic and financial data as well as empirical studies.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Wooldridge, J.M. (2016). Introductory Econometrics: A Modern Approach (6ed) Cengage Learning. / Special Readings.
Additional Literature
Teaching Methods
The methods include lectures (which may involve power point presentation
Video and audio aids)
Student presentations
Projects and class discussions.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introductory Lecture | |
| 2 | Review of Regression | Ch. 01 & 02 |
| 3 | Multiple Regression Analysis: Estimation & Inference | Ch. 03 & 04 |
| 4 | Multiple Regression Analysis: Further Issues | Ch. 06 |
| 5 | Dummy Variable Regression Models / Quiz 2 | Ch. 07 |
| 6 | Heteroskedasticity | Ch. 08 |
| 7 | Midterm Exam | |
| 8 | Time Series Data: Basic Regression Analysis | Ch. 10 |
| 9 | Time Series Data: Some Issues in Using OLS | Ch. 11 |
| 10 | Time Series Data: Serial Correlation & Heteroskedasticity | Ch. 12 |
| 11 | Pooled Cross-Sectional Data | Ch. 13 |
| 12 | Panel Data Methods: Fixed Effects and Random Effects / Quiz 4 | Ch. 14 |
| 13 | Instrumental Variables Estimation and Two Stage Least Squares | Ch. 15 |
| 14 | Presentations | |
| 15 | Presentations |
Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Project
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Quiz
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Class Participation
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
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)
Home Study
30 hours ⏳ (15 week × 2 h)
Quiz
10 hours ⏳ (2 week × 5 h)
Projects
20 hours ⏳ (5 week × 4 h)
Midterm Exam Study
20 hours ⏳ (1 week × 20 h)
Final Exam Study
25 hours ⏳ (1 week × 25 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 [ECON302] 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 Mar 03, 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 | |||||||||
| ECON302 | Econometrics II | 3 | 2 | 6 | ||||||
| Prerequisite | ECON301 | It is a prerequisite to | - | |||||||
| Lecturer | Office Hours / Room / Phone | Currently not available |
||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | This aim of this course is to extend the methods and techniques employed in Econometrics I and to allow for causal inference and prediction in a larger variety of data sets. The overall objective is for the student to learn how to critically examine economic and financial data as well as empirical studies. | |||||||||
| Textbook | Wooldridge, J.M. (2016). Introductory Econometrics: A Modern Approach (6ed) Cengage Learning. / Special Readings. | |||||||||
| Additional Literature | ||||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | The methods include lectures (which may involve power point presentation, video and audio aids), student presentations, projects and class discussions. | |||||||||
| Teaching Method Delivery | Teaching Method Delivery Notes | |||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introductory Lecture | |||||||||
| Week 2 | Review of Regression | Ch. 01 & 02 | ||||||||
| Week 3 | Multiple Regression Analysis: Estimation & Inference | Ch. 03 & 04 | ||||||||
| Week 4 | Multiple Regression Analysis: Further Issues | Ch. 06 | ||||||||
| Week 5 | Dummy Variable Regression Models / Quiz 2 | Ch. 07 | ||||||||
| Week 6 | Heteroskedasticity | Ch. 08 | ||||||||
| Week 7 | Midterm Exam | |||||||||
| Week 8 | Time Series Data: Basic Regression Analysis | Ch. 10 | ||||||||
| Week 9 | Time Series Data: Some Issues in Using OLS | Ch. 11 | ||||||||
| Week 10 | Time Series Data: Serial Correlation & Heteroskedasticity | Ch. 12 | ||||||||
| Week 11 | Pooled Cross-Sectional Data | Ch. 13 | ||||||||
| Week 12 | Panel Data Methods: Fixed Effects and Random Effects / Quiz 4 | Ch. 14 | ||||||||
| Week 13 | Instrumental Variables Estimation and Two Stage Least Squares | Ch. 15 | ||||||||
| Week 14 | Presentations | |||||||||
| Week 15 | Presentations | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 1-4 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 30 | 1-4 | Not Allowed | |
| Project | 1 | 20 | 1-4 | Not Allowed | |
| Quiz | 2 | 10 | 1-4 | Not Allowed | |
| Class Participation | 10 | 1-4 | Not Allowed | ||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Home Study | 2 | 15 | 30 | |||
| Quiz | 5 | 2 | 10 | Projects | 4 | 5 | 20 | |||
| Midterm Exam Study | 20 | 1 | 20 | Final Exam Study | 25 | 1 | 25 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||