pith:DQ52YKTY
Estimator Averaging of Local Projection and VAR Impulse Responses
Estimator averaging optimally combines local projections and VARs to minimize mean squared error of impulse responses at each horizon.
arxiv:2605.05456 v2 · 2026-05-06 · econ.EM
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\pithnumber{DQ52YKTYGUWSLJF2SEJGXIJSCE}
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Record completeness
Claims
We propose an easy-to-implement estimator-averaging approach that combines LP and VAR at each horizon by minimizing the mean squared error of the impulse response itself, rather than in-sample fit. We derive closed-form oracle weights for this finite-sample risk problem, develop feasible AR-sieve-bootstrap procedures...
For a benchmark class of short-memory linear data generating processes in which LP and VAR are both consistent, we establish the consistency and limiting distribution of the feasible averaged estimator.
A new estimator-averaging method combines LP and VAR impulse responses by minimizing MSE at each horizon, with closed-form weights and bootstrap procedures, showing risk reduction in Monte Carlo and empirical tests.
Receipt and verification
| First computed | 2026-06-09T02:07:28.459973Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1c3bac2a78352d25a4ba91126ba1321127b8b11332b0ec37909a9453415404a3
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DQ52YKTYGUWSLJF2SEJGXIJSCE \
| 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: 1c3bac2a78352d25a4ba91126ba1321127b8b11332b0ec37909a9453415404a3
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "econ.EM",
"submitted_at": "2026-05-06T21:24:39Z",
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