📅 2024-09-10 — Session: Weibull Duration Model Analysis and Implementation
🕒 01:50–03:00
🏷️ Labels: Weibull Model, MLE, Python, Statistical Analysis, Education
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and implement a Weibull duration model for analyzing job tenure, focusing on maximum likelihood estimation (MLE) and interpretation of results.
Key Activities
- Developed a comprehensive problem for estimating a Weibull duration model using a minimal dataset, emphasizing MLE and coefficient interpretation.
- Implemented the model in Python, detailing tasks for coefficient estimation and results interpretation.
- Designed a challenging question for PhD-level economics students, focusing on MLE in Weibull models with unobserved heterogeneity.
- Analyzed vulnerabilities in statistical exercises and provided solutions to enhance complexity and critical thinking.
- Addressed issues in Weibull model fitting, including coefficient magnitude and correlation, and provided updated simulation code.
- Discussed parameter coherence in modeling and debugging log-likelihood estimation issues.
- Outlined PhD-level problems and educational puzzles to enhance critical thinking and problem-solving skills.
Achievements
- Successfully implemented the Weibull duration model in Python, providing a framework for job tenure analysis.
- Developed educational content and complex problems for PhD students, enhancing understanding of statistical modeling.
- Identified and addressed issues in model fitting and parameter coherence, improving the robustness of the analysis.
Pending Tasks
- Further review and interpretation of Weibull model results, focusing on covariates such as education, sector, and gender.
- Continue developing educational problems that introduce uncertainty and require critical thinking.
- Explore additional debugging techniques for log-likelihood estimation.