Data audit
| file | SHA-256 | rows | cols | range |
|---|---|---|---|---|
| data/cpi_quarterly.csv | c8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea (matches README/bundle) | 127 | 3 (quarter, cpi_avg, infl_annualised) | 1995-01-01 … 2026-07-01 |
| data/cpi_monthly.csv | 855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2 (matches) | 381 | 2 (month, cpi) | 1995-01 … 2026-09 |
- Units: cpi index 2002=100 NSA; infl_annualised in percent at annual rate.
- Missing: only infl_annualised for 1995Q1 (by construction, no prior quarter). No duplicates; no gaps (monthly all consecutive; quarterly consecutive).
- Consistency: quarterly mean of monthly reproduces cpi_avg to 3.3e-7 (rounding); 400·Δln(cpi_avg) reproduces infl_annualised to 3.3e-6 (rounding of cpi_avg to 6 dp). Every quarter has 3 months.
- infl_annualised: n=126, mean 2.13, sd 2.39, min −6.04, max 12.15. Strong seasonality (mean by quarter Q1 2.32, Q2 4.18, Q3 1.66, Q4 0.30).
- Anomalies: none in format; economic outliers in 2020 (COVID) and 2021–2023 (inflation surge) kept as they are genuine data.
- Availability: data extend beyond the last origin (to 2026Q3) — needed for targets; analysis.py truncates the estimation sample at each origin to quarters ≤ origin. No input violates rules otherwise.