Phase 2: Grid Search & Screening#

The second phase builds a focused parameter grid and searches all combinations using single-seed evaluation.

Grid Building#

Parameters are categorized by sensitivity analysis results:

  • INCLUDE (Δ > threshold): Include in grid search with all values

  • FIX (Δ ≤ threshold): Fix at best value from sensitivity analysis

from calibration import build_focused_grid, count_combinations

grid, fixed = build_focused_grid(sensitivity)
print(f"Grid: {count_combinations(grid)} combinations")
print(f"Fixed: {fixed}")

Value pruning (optional): Drops grid values whose OAT score is more than pruning_threshold below the best value for that parameter. Default: auto (2× sensitivity threshold). Use --pruning-threshold none to disable.

Grid from YAML#

# calibration_grid.yml
grid:
  h_rho: [0.05, 0.10, 0.15]
  delta: [0.05, 0.10]
fixed:
  theta: 8

python -m calibration --phase grid --grid calibration_grid.yml

Parameter Grid#

The 14 common parameters cover:

Category

Parameters

Initial conditions

price_init, min_wage_ratio, net_worth_ratio, equity_base_init, savings_init

New firm entry

new_firm_size_factor, new_firm_production_factor, new_firm_wage_factor, new_firm_price_markup

Economy-wide

beta

Search frictions

max_M

Credit

max_loan_to_net_worth, max_leverage

Labor

job_search_method

Extension-specific: Growth+ adds sigma_decay, sigma_max; buffer-stock adds buffer_stock_h.

Checkpointing#

Grid screening supports automatic checkpointing for resumability:

# Start a grid search (auto-checkpoints)
python -m calibration --phase grid --scenario baseline

# Resume after interruption
python -m calibration --phase grid --scenario baseline --resume

Checkpoints save completed results incrementally, so interrupted runs lose only the current evaluation.