# Data audit (P1)

File: data/model.csv

• SHA-256: 098123961451e4f9a2e76afdbc83aec78066fc713d0645659c09b77b1636195b (matches bundle.json)
• 10 data rows (+header), 3 columns: parameter (str), value (str; numeric except grid_spacing='log'), description (str)
• Parameters: alpha=0.36, beta=0.95, y_min=0.05, y_max=5.0, grid_points=400, grid_spacing=log, vfi_tolerance=1e-8, eval_y_1=0.5, eval_y_2=1.0, eval_y_3=2.0. Units: y in units of output; tolerance in utils.
• No missing values, no duplicate parameter names; no dates (theoretical parameters).
• Sanity: 0<alpha<1, 0<beta<1, y_min<eval points<y_max. Steady state y*=(alpha*beta)^(alpha/(1-alpha))≈0.547 inside grid.
• Grid adequacy (pre-check, analytic): optimal y'=(alphabetay)^alpha ranges from ≈0.231 (y=0.05) to ≈1.21 (y=5) – strictly inside [0.05,5], so the grid bounds do not truncate the optimum. Min choice y'=0.05 needs k=0.05^(1/alpha)≈2.4e-4 < y_min, so every grid state has feasible choices.
• Evaluation points 0.5, 1.0, 2.0 are not exactly grid nodes (log step ≈0.01154) -> interpolation required (as allowed by TASK.md).
• No information-availability violations: file contains only design parameters.