[likelihood_ratio_processes_2] Update new exercises with learning agents #566
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Pull Request Overview
This PR adds comprehensive exercises for the Blume-Easley model with Bayesian learning agents to the likelihood ratio processes lecture. The changes introduce three new exercises that explore how agents with different priors learn from data and how their beliefs affect consumption allocations in complete markets settings.
Key Changes
- Added exercises covering two-agent learning with different priors (Exercise 4)
- Extended the analysis to three-model scenarios with varying belief structures (Exercises 5-7)
- Integrated citation support for related research on incomplete markets
Reviewed Changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| lectures/likelihood_ratio_process_2.md | Added three new exercises with detailed solutions exploring Bayesian learning in Blume-Easley models, updated imports, and enhanced related lectures section |
| lectures/_static/quant-econ.bib | Added bibliography entry for Blume et al. (2018) research on incomplete markets |
|
Hi @mmcky, I will merge this soon! |
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This PR adds new exercises for B-E model with Bayes learning.