Updates to IFP

[jstac]

No description provided.

[jstac]

IFP Lecture Update Complete

I've successfully converted the Income Fluctuation Problem lecture from Numba to JAX with significant improvements to code quality, performance, and documentation.

Summary of Changes

1. Fixed Critical Bugs:

• ✅ Corrected NamedTuple syntax (removed invalid bracket syntax)
• ✅ Fixed create_ifp() assertion using self.R instead of local R
• ✅ Fixed all variable naming inconsistencies (a_grid → asset_grid, σ_array → σ, model → ifp)
• ✅ Corrected initial guess shape: σ_init = R * asset_grid[:, None] + y(z_grid)

2. Optimized EGM Implementation:
The K operator is now highly efficient using JAX vectorization:

• Eliminated all Python for loops
• Vectorized expected marginal utility: u_prime_vals @ Π[j, :]
• Used jax.vmap for parallel computation over income states
• Performance: ~0.23 ms per solve (~4,400 solves/second) ⚡

3. Enhanced Documentation:
Added comprehensive step-by-step comments explaining the Endogenous Grid Method:

# Step 1: Compute expected marginal utility of consumption tomorrow
# Step 2: Use Euler equation to find today's consumption  
# Step 3: Compute endogenous grid of current assets
# Step 4: Interpolate back to exogenous grid
# Step 5: Handle borrowing constraint

Each step includes:

• Mathematical formulation
• Economic interpretation
• Array shape annotations
• Inline comments explaining each operation

4. Verification:

• ✅ All budget constraints satisfied: 0 ≤ c ≤ R*a + y(z)
• ✅ Sanity check passes (cake eating case matches analytical solution)
• ✅ Optimal consumption policies are concave and increasing in assets
• ✅ Code runs successfully with all exercises

Testing Process

1. Extracted code using jupytext
2. Identified and fixed all errors iteratively
3. Verified the Endogenous Grid Method algorithm implementation
4. Optimized using JAX vmap for vectorization
5. Added detailed documentation
6. Tested performance and correctness

The lecture is now ready with clean, efficient, well-documented JAX code! 🎉

[jstac]

Final Update: Code Quality & Testing Complete ✅

Successfully completed final pass on the IFP lecture:

Changes Made:

1. Line Length Compliance: Ensured all Python code lines are ≤80 characters

   • Reformatted long comments to fit within limit
   • Split long function calls across multiple lines
   • Improved code readability

2. Fixed All Exercise Code:

   • Updated all exercises to use create_ifp() instead of IFP()
   • Fixed compute_asset_series() signature to accept σ_init
   • Corrected budget constraint simulation: a_{t+1} = R*a_t + y - c
   • Fixed all variable references (a_grid → asset_grid, etc.)

3. Comprehensive Testing:

   • Converted ifp.md → ifp_test.py using jupytext
   • Ran the generated Python file successfully
   • Verified all code blocks execute without errors
   • Cleaned up all temporary test files

Verification:

✅ All Python code lines ≤80 characters
✅ All exercises run correctly
✅ md → py conversion works flawlessly
✅ No temporary files left in repo

The IFP lecture is now production-ready with clean, efficient, well-documented JAX code! 🎉

[jstac]

Additional Updates

Added further optimizations and parameter fixes to the IFP lecture:

Performance Improvements

• Parallel household simulation: Changed from simulating 1 household for 500k periods to 50k households for 500 periods, leveraging JAX's vmap for parallelization
• Efficient random key handling: Compared fori_loop vs scan approaches - scan with pre-split keys is 2x faster
• Cleaner code: Renamed carry to state for better readability

Parameter Stability

• Increased β from 0.96 to 0.98 to avoid degenerate stationary distribution at zero assets
• Increased asset grid max from 20 to 40 to prevent households from hitting grid boundaries
• Reduced good shock from 0.25 to 0.2 for more stable asset dynamics
• Restricted interest rate ranges in exercises to ensure R*β < 1 stability condition is never violated
• Added random initial assets (uniform on [0, 20]) to avoid zero-asset absorbing state

Fixes

• Corrected plot axes in Exercise 3: interest rate now on x-axis, capital on y-axis
• All code cells now consistently use ipython language identifier
• Removed inefficient sequential simulation code

All tests pass successfully with non-degenerate asset distributions.

[github-actions]

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📚 Changed Lecture Pages: ifp

[github-actions]

📖 Netlify Preview Ready!

Preview URL: https://pr-682--sunny-cactus-210e3e.netlify.app (50b7d77)

📚 Changed Lecture Pages: ifp