1# Data audit (P1)2- File: data/model.csv; SHA-256 098123961451e4f9a2e76afdbc83aec78066fc713d0645659c09b77b1636195b (matches bundle.json).3- 10 data rows x 3 columns (parameter, value, description). No missing values, no duplicate parameter names.4- Parameters: alpha=0.36, beta=0.95, y_min=0.05, y_max=5.0, grid_points=400, grid_spacing=log, vfi_tolerance=1e-8,5 eval_y = 0.5, 1.0, 2.0. Units: output (y), utils (V); alpha, beta dimensionless. No dates (theoretical parameters).6- `value` column is mixed type (string "log" for grid_spacing); parsed per-parameter.7- Sanity: 0<alpha<1, 0<beta<1 -> contraction & interior solution conditions hold. Eval points lie inside [y_min, y_max].8- Grid adequacy: closed-form next-period output y'=(alpha*beta*y)^alpha maps [0.05,5] into approx [0.231,1.213] (checked in9 analysis.py), strictly inside the grid, so the grid bounds do not truncate the optimal policy.10- Eval points 0.5, 1.0, 2.0 are not exact grid nodes (checked in analysis.py) -> linear interpolation in ln y.11- No information-availability violations: file contains only model primitives. No rows excluded.12