📅 2024-09-10 — Session: Weibull Duration Model Analysis and Educational Problem Design
🕒 01:50–03:00
🏷️ Labels: Weibull Model, Maximum Likelihood Estimation, Job Tenure, Phd Education, Statistical Modeling
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and implement Weibull duration models for job tenure analysis, focusing on maximum likelihood estimation, parameter interpretation, and educational problem design for PhD-level students.
Key Activities
- Developed a comprehensive exercise for estimating a Weibull duration model using maximum likelihood estimation.
- Implemented Python code for Weibull model estimation and interpretation of job tenure coefficients.
- Designed challenging questions for PhD students on duration models with unobserved heterogeneity.
- Analyzed vulnerabilities in statistical exercises and proposed solutions to enhance complexity.
- Addressed issues in Weibull model fitting, including parameter coherence and debugging log-likelihood estimation.
- Created a PhD-level problem involving multivariate data generation and econometrics assessment.
- Transformed statistical problems into engaging educational puzzles to promote critical thinking.
Achievements
- Successfully implemented and debugged Weibull model fitting processes.
- Developed educational content and exercises for advanced statistical modeling.
Pending Tasks
- Further review and interpretation of Weibull model results based on covariates such as education, sector, and gender.
- Continue refining educational problems to enhance student engagement and understanding.