feat: add calibration/reweighting system#24
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nikhilwoodruff merged 7 commits intomainfrom Apr 8, 2026
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Python scripts pull calibration targets from OBR EFO (tax/benefit aggregates), HMRC SPI (income distributions by band), DWP stat-xplore (benefit caseloads), and ONS (demographics). Rust module reweights household data using Adam optimiser in log-space to minimise mean squared relative error, with holdout validation for HMRC count targets. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
UC_Monthly measure is V_F_UC_CASELOAD_FULL (people), not V_F_UC_HOUSEHOLD. PIP uses PIP_Monthly_new database (post-2019). Simplified queries to use no dimensions — stat-xplore auto-selects the latest month. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds pension credit, carer's allowance, attendance allowance, state pension, ESA, and DLA caseloads from stat-xplore, plus UC household breakdowns by family type, child/carer/LCWRA/housing entitlement. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Run a baseline simulation before calibrating so targets can reference simulated variables (income_tax, national_insurance, vat, etc.) rather than raw input proxies. Income tax RMSRE drops from 79% to 1%. Also adds benunit entity support to the calibration matrix builder, and updates OBR targets to use simulated tax/benefit variables (income_tax, national_insurance, vat, fuel_duty, capital_gains_tax, stamp_duty) and benunit-level benefit variables (universal_credit, housing_benefit, etc.). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add BenunitFilter struct for UC subgroup targets (is_couple, has_children, has_carer, has_lcwra, has_lcw, has_housing) - Add total_ni variable (employee + employer NI) for OBR NI receipts - ONS targets now emitted for years 2024-2030 so they bind regardless of calibration year (fixes weight sum blowup from 34m to ~29m) - DWP UC subgroup targets now carry benunit_filter conditions - Add historical FRS years 1994-2021 to rebuild_all manifest Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Parse sheet 1.6 for employment count, wages & salaries, self-employment income, and self-employed count. Brings total target count to 385 (283 training, 102 holdout). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
HMRC SPI 2022-23 income bands are now scaled to 2024-2030 using OBR growth rates (sheet 3.5 for self-employment/dividends/property/savings, sheet 1.6 for wages & salaries, sheet 1.7 CPI for pensions). DWP stat-xplore caseloads are scaled to 2024-2029 using DWP's own caseload forecasts from the Spring Statement 2025 benefit expenditure and caseload tables. All targets now participate in training (holdout flag only affects reporting, not gradient). This ensures consistent ~15-20% RMSRE across all calibration years, so trends are accurate. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Adds a calibration/reweighting system that adjusts EFRS household survey weights to match administrative totals from four sources.
The system works in two layers: Python scripts parse official statistics into a standardised JSON target format, then a Rust Adam optimiser adjusts log-space weights to minimise mean squared relative error against those targets.
Target sources (1,406 targets total):
Targets are consistent across calibration years (2024-2029 each have ~201 targets), ensuring trends are accurate. Training RMSRE is 15-20% across all years. Calibrated EFRS for 2024-2029 uploaded to
gs://policyengine-uk-microdata/efrs/.