pith:CTYE6UVE
A Kernel Score Perspective on Forecast Disagreement and the Linear Pool
Forecast disagreement among component distributions affects linear pool performance under any kernel scoring rule, with a new condition for optimal equal weights.
arxiv:2412.09430 v4 · 2024-12-12 · econ.EM · stat.AP
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Record completeness
Claims
Our results indicate that forecast disagreement (measured as the average pairwise divergence of all component distributions) has important implications for the linear pool's performance. [...] yield a novel condition under which equal combination weights are optimal under a given kernel scoring rule.
The mathematical properties that allow results on linear pooling under squared error loss extend without additional restrictions to the entire family of kernel scores across univariate, multivariate, discrete, and continuous settings.
Generalizes linear pooling theory from squared error to kernel scores and identifies a condition for optimal equal weights based on forecast disagreement.
Receipt and verification
| First computed | 2026-06-01T01:03:39.361773Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
14f04f52a4e199410fe3612bbe16c0f2bcf85d100c701df2daf3301f8c389b04
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/CTYE6UVE4GMUCD7DMEV34FWA6K \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 14f04f52a4e199410fe3612bbe16c0f2bcf85d100c701df2daf3301f8c389b04
Canonical record JSON
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