Future improvement suggestions: calibration and performance

[vahid-ahmadi]

Tracking suggestions from PR #66 review for follow-up work.

Calibration

1. Open-economy parameters (zeta_D, zeta_K)

Currently at defaults (zeta_D=0.4, zeta_K=0.1). The UK runs a persistent current account deficit (~3–4% of GDP) and foreign investors hold ~30% of gilts. These should be calibrated from:

• ONS Balance of Payments (Pink Book)
• DMO gilt holder data

2. Government spending composition (alpha_G)

Currently auto-calibrated as a fiscal residual in SS. OBR publishes detailed departmental spending breakdowns that could anchor this, especially in multi-sector mode where government demand varies across sectors:

• OBR Public finances databank
• HM Treasury PESA

3. Immigration age profile

We use UN World Population Prospects data, which is smoothed. ONS publishes UK-specific immigration data by single year of age that captures the sharp peak at ages 18–25:

• ONS Long-term international migration

4. UK wealth distribution (lambdas and e)

The current J=7 ability types use US-derived earnings-by-age profiles. Re-estimating from UK data would improve distributional results:

• HMRC Survey of Personal Incomes (SPI)
• ONS Family Resources Survey (FRS)
• ONS Wealth and Assets Survey

5. Sector-specific effective corporate tax rates

Currently a flat 27% CIT across all 8 sectors. Effective rates vary in the UK (e.g. North Sea oil has a supplementary charge, R&D-intensive sectors get enhanced capital allowances):

• HMRC Corporation Tax statistics
• HMRC effective tax rates by sector
• OBR policy costings for capital allowances impact

Performance

6. Parallel calibration

The 3 budget-window years in calibrate() are estimated sequentially but are independent. Could run in parallel with concurrent.futures.ProcessPoolExecutor to cut calibration time by ~60%.