# Data audit (P1)

• File: data/model.csv; SHA-256 098123961451e4f9a2e76afdbc83aec78066fc713d0645659c09b77b1636195b (matches bundle.json).
• 10 data rows x 3 columns (parameter, value, description). No missing values, no duplicate parameter names.
• 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 = 0.5, 1.0, 2.0. Units: output (y), utils (V); alpha, beta dimensionless. No dates (theoretical parameters).
• value column is mixed type (string "log" for grid_spacing); parsed per-parameter.
• Sanity: 0<alpha<1, 0<beta<1 -> contraction & interior solution conditions hold. Eval points lie inside [y_min, y_max].
• Grid adequacy: closed-form next-period output y'=(alphabetay)^alpha maps [0.05,5] into approx [0.231,1.213] (checked in analysis.py), strictly inside the grid, so the grid bounds do not truncate the optimal policy.
• Eval points 0.5, 1.0, 2.0 are not exact grid nodes (checked in analysis.py) -> linear interpolation in ln y.
• No information-availability violations: file contains only model primitives. No rows excluded.