📅 2024-09-06 — Session: Developed Advanced Econometric Questions for Teaching
🕒 15:35–16:20
🏷️ Labels: Econometrics, Nested Logit, Education, Binary Models, Python
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The goal of the session was to develop advanced econometric questions suitable for graduate and post-graduate level studies, with a focus on binary outcome models and nested logit models.
Key Activities
- Question Development: Created a series of question ideas inspired by econometric exercises, focusing on binary outcome models such as probit and logit. These questions are designed to challenge graduate-level students and encourage a deeper understanding of econometric concepts.
- Advanced Question Outline: Outlined advanced questions aimed at post-graduate or PhD students, focusing on complex calculations and theoretical proofs, including likelihood ratio tests and maximum likelihood estimation.
- Nested Logit Model Analysis: Reviewed and corrected probability computations and elasticity calculations for transportation alternatives using a nested logit model. Updated Python code to derive cross-price elasticities and choice probabilities.
Achievements
- Developed a comprehensive set of advanced econometric questions for educational purposes.
- Successfully reviewed and improved the nested logit model analysis, ensuring accurate probability and elasticity calculations.
Pending Tasks
- Further refinement of questions to ensure clarity and alignment with educational objectives.
- Validation of Python code implementations for accuracy and efficiency.