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Add new Job Search V lecture on risk-sensitive preferences
This commit adds a new lecture on risk-sensitive preferences in the job search context, positioned after the fitted VFI lecture. The lecture introduces entropic risk-adjusted expectations and applies them to the McCall search model. Changes: - Add mccall_risk.md: New lecture on risk-sensitive preferences - Update _toc.yml: Insert mccall_risk after mccall_fitted_vfi - Renumber subsequent lectures (V→VI, VI→VII, VII→VIII, VIII→IX, IX→X) - mccall_persist_trans: V → VI - career: VI → VII - jv: VII → VIII - odu: VIII → IX - mccall_q: IX → X Key features of the new lecture: - Introduction to risk-sensitive preferences via entropic risk measure - Examples with Gaussian and Beta distributions - Mean-preserving spread analysis - Application to McCall job search model with risk aversion - Analysis of reservation wages and unemployment rates vs risk aversion - All code verified to run successfully with JAX 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
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lectures/_toc.yml

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- file: mccall_model_with_separation
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- file: mccall_model_with_sep_markov
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- file: mccall_fitted_vfi
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- file: mccall_risk
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- file: mccall_persist_trans
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- file: career
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- file: jv

lectures/career.md

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</div>
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# Job Search VI: Modeling Career Choice
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# Job Search VII: Modeling Career Choice
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```{index} single: Modeling; Career Choice
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```

lectures/jv.md

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</div>
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# {index}`Job Search VII: On-the-Job Search <single: Job Search VII: On-the-Job Search>`
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# {index}`Job Search VIII: On-the-Job Search <single: Job Search VIII: On-the-Job Search>`
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```{index} single: Models; On-the-Job Search
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lectures/mccall_persist_trans.md

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</div>
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# Job Search V: Persistent and Transitory Wage Shocks
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# Job Search VI: Persistent and Transitory Wage Shocks
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```{include} _admonition/gpu.md
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lectures/mccall_q.md

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name: python3
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# Job Search IX: Search with Q-Learning
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# Job Search X: Search with Q-Learning
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## Overview
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