Computed 2026-08-05 against hypothesis.md (commit fa8eef8, pre-registered
before any correlation was computed). Reproducible via
python -m src.domains.domain_c_korea_nowcast.compute.
The pipeline reproduces the well-established relationship. Contemporaneous (lag 0) correlation between Korea's and Taiwan's YoY semiconductor export growth is r = 0.63, already above the pre-registered 0.5 threshold. The best fit across the tested range is at lag −1 (Taiwan leading Korea by one month), r = 0.69.
| Lag (months) | n | Correlation |
|---|---|---|
| −3 (Taiwan leads by 3) | 29 | 0.62 |
| −2 | 30 | 0.62 |
| −1 | 33 | 0.69 (max) |
| 0 (contemporaneous) | 37 | 0.63 |
| +1 (Korea leads by 1) | 33 | 0.57 |
| +2 | 30 | 0.50 |
| +3 | 29 | 0.48 |
Falsification criterion (max correlation ≤ 0.5) was not met — H1 is not falsified, and per the calibration-check framing in the hypothesis, this result is read as validating the pipeline's method, not just as an economic finding. Two independently-constructed aggregations (Korea summed across partners at 6-digit HS level; Taiwan summed across partners and 11-digit national codes) produce the expected strong, positive, roughly-contemporaneous co-movement that trade commentary has long attributed to these two economies. If this hadn't reproduced, the hypothesis explicitly called for treating that as a likely pipeline bug first — it didn't come to that.
The correlation is fairly flat and strong across lags −3 through 0 (0.62 to 0.69) and decays toward the positive-lag end (0.50 to 0.48 at +2/+3) — consistent with a real, roughly-contemporaneous shared demand cycle rather than one series mechanically leading the other by a fixed number of months. The slight edge at lag −1 (Taiwan leading by one month) is suggestive but not sharply distinguished from lag 0 — both exceed the threshold comfortably and the difference (0.69 vs 0.63) is not large relative to the noise in a 30-40 month sample.
m3 dataset is listed but returned
zero populated series; a US HS-8542 import aggregate was a plausible
candidate but the query timed out and wasn't retried). The result
should be read as "Korea and Taiwan chip exports move together," not
as "Korea's exports lead the world economy" — the latter claim is
outside what was actually tested here.COUNT(*) verification query
timed out and was not retried. That both countries are missing the
same months is itself notable — worth checking whether this reflects
a genuine FactIQ ingestion gap common to both schemas, rather than two
independent coincidences, in a future pass.LIKE join, to get the downstream demand leg this domain is
currently missing.Status: PRE-REGISTERED, 2026-08-05. No cross-correlation or lag analysis has been computed at the time this file is committed — only series existence, aggregation structure, and a raw monthly total pull for two series (metadata plus construction, not a computed result). Per project principle, this hypothesis will not be edited after results are computed; if it fails, the failure is recorded, not the hypothesis.
Per the original spec: Korea's export-leads-the-cycle relationship is well-established in real-world trade commentary, which makes it the best test of the pipeline itself, not just of the data. If this pipeline cannot reproduce a relationship this well documented, that is a warning about the pipeline's method, not merely an inconclusive economic finding. This framing is carried through to H1's falsification criterion below.
Korea's 10-day and 20-day provisional export releases remain confirmed
absent from FactIQ (Phase 0 finding, re-checked here via search_series
across export-related terms — every Korea trade series returned is
Monthly frequency; no dekad-release series exists). This means the
"nowcast" in this domain's name can only ever mean early monthly
detection of a turn, not the intra-month nowcast the original spec
envisioned. Stated here plainly, not worked around.
Korea semiconductor exports — FactIQ korea_trade schema:
No single series carries Korea's total semiconductor export value; it
must be aggregated in SQL across every partner country at the CN/HS
6-digit level for HS chapter 8542 (electronic integrated circuits),
flow = X (export), excluding _qty (weight) series. Confirmed
constructible and non-trivial: a test pull for 2022-01 through 2026-06
(54 months) returned real values ranging from ~$5.3B/month (early 2023,
the real-world chip downturn) to ~$33.6B/month (June 2026, consistent
with the real-world AI/HBM memory demand surge) — a plausible, complete
54-of-54-month series, not sparse.
Taiwan semiconductor exports — FactIQ taiwan_trade schema, as the
comparator: Same aggregation logic, but Taiwan's HS detail only exists
at the 11-digit national level (hs_level = '11', no 6-digit rollup), so
the aggregation sums across all 11-digit codes under 8542 and all
partners. Confirmed constructible: the same 2022-01–2026-06 test pull
returned real values from ~$21.6B/month (early 2024) to ~$51.0B/month
(May 2026) — the same broad shape as Korea's series (2022 high, 2023
trough, 2024 recovery, 2025–2026 surge), which is itself a first,
qualitative version of the exact relationship H1 formally tests.
A genuine "global cycle" downstream-demand comparator was attempted and
not completed. No US ISM/PMI New Orders series exists anywhere in
FactIQ (searched directly — absent, consistent with it being a private
survey most government warehouses don't carry). Census's m3
(Manufacturers' Shipments, Inventories, and Orders) dataset is listed in
search_datasets but returned zero series on direct query — listed
but unpopulated. A US Census HS-trade aggregate (us_census_hs, HS 8542,
"Total For All Countries" partner code) does exist and would have been a
reasonable downstream-demand proxy, but the aggregation query timed out
(30s limit) when joining across the full HS 8542 code range. This
domain proceeds without a downstream global-demand comparator — H1
tests co-movement between two upstream exporters, not a lead against
final demand. Stated as a real scope limitation, not silently dropped;
see Caveats.
Korea and Taiwan are the world's two dominant semiconductor exporters, both selling into the same global electronics/AI-hardware demand cycle. Their monthly export value growth rates should be strongly positively correlated at or near zero lag — this is the well-established relationship the original spec calls out.
Method (deterministic, in compute.py):
1. Compute year-over-year % change for both series (same-month-prior-year
lookup, as in Domain A's yoy() — not a naive row-offset).
2. Cross-correlate the two YoY series at lags −3 to +3 months (Taiwan
shifted relative to Korea; negative lag = Taiwan leads, positive lag =
Korea leads).
3. Report the correlation at every tested lag and identify the maximum.
Falsification, framed per this domain's calibration-check purpose: if the maximum correlation across lags −3..+3 does not exceed 0.5, H1 is falsified — and per the framing above, that outcome should be read first as a possible pipeline problem (wrong HS aggregation, wrong date alignment, a real methodological bug) and only second as a genuine economic finding, precisely because the underlying relationship is so well documented elsewhere that its absence here is more likely to indict the method than the world.
m3
listed but empty; the one viable candidate, US HS-8542 import totals,
hit a query timeout) — H1 tests two upstream exporters against each
other, not against final demand. A future pass could retry the US
Census aggregate with a narrower, pre-enumerated list of exact 10-digit
series IDs instead of a broad LIKE join.Password required.