# Data audit (P1)

File: data/model.csv — SHA-256 098123961451e4f9a2e76afdbc83aec78066fc713d0645659c09b77b1636195b (matches bundle.json). 10 rows x 3 columns (parameter, value, description). No missing values, no duplicate parameter names. No dates (theoretical parameters). Values: alpha=0.36, beta=0.95 (both in (0,1)); y_min=0.05, y_max=5.0 (units of output), grid_points=400, grid_spacing=log, vfi_tolerance=1e-08 (utils, sup norm); eval_y = 0.5, 1.0, 2.0. Anomalies: column value is mixed type (string 'log' among numbers) -> parsed per parameter. Evaluation points are not grid nodes (log step = ln(100)/399 ≈ 0.01154) -> interpolation, as allowed by TASK.md. Grid adequacy: closed-form steady state y* = (alpha*beta)^(alpha/(1-alpha)) ≈ 0.547 lies inside [0.05, 5]; the optimal next-period output from every grid state stays strictly inside the grid (checked in analysis.py: policy never hits grid bounds). Information-availability: no input violates the rules; only model.csv is used.