Week 8 Homework Feedback: Kamila Murzabulatova

Assignment: Dynamic Programming & Value Function Iteration with Population Growth
Week: 8
Date: Week 8 Assessment

⚠️ Overall Assessment

Result: ⚠️ Partial <50% Correct

Kamila’s code is neat, modular, and numerically robust, but it solves the wrong model. Both the main script and the helper solveVFI function omit the ((1+n)) term in the resource constraint, so the entire computation corresponds to the (n=0) case from Week 7. As a result, the value/policy functions, simulations, and parameter experiments do not satisfy the Week 8 instructions. 5/15 tasks correct (≈33%).

Key Issues

  • Missing population growth in consumption mapping (Task 1):
    cons = bsxfun(@minus, fvec + (1-delta).*kgrid, kgrid'); should be
    cons = fvec + (1-delta).*kgrid - (1+n)*kgrid'.
  • Simulation uses wrong resource constraint:
    cpath(t) = f(k_t) + (1-δ)k_t - k_{t+1} omits the ((1+n)) factor.
  • Parameter experiments only change (\alpha) and (\beta) in the (n=0) model; no experiment varies (n).
  • Transition discussion and comments therefore refer to the wrong dynamics.

What Worked

  • Clean separation of concerns (solveVFI helper, saved (V) snapshots, etc.).
  • Comprehensive plotting (value convergence, policy, simulations).
  • Clear commentary on the economic meaning of (\beta) and (\alpha).

Suggestions to Fix

  1. Include population growth everywhere: 
    cons = fvec + (1-delta).*kgrid - (1+n)*kgrid';
cpath(t) = kpath(t)^alpha + (1-delta)*kpath(t) - (1+n)*kpath(t+1);

    Pass n as an argument to solveVFI so experiments can vary it.

  2. Re-run VFI with the corrected constraint and regenerate all figures.
  3. Add an (n) experiment (e.g., (n = 0, 0.01, 0.02)) to show the dilution effect emphasized in the brief.

Summary

Great structure, but the Week 8 modification—the ((1+n)) term—was deliberately the heart of this assignment. Please patch the resource constraint, re-run the analysis, and resubmit so we can reassess under the correct model.