{"paper":{"title":"Quasi-Bayesian Local Projection Instrumental-Variables Method: Application to Renewable Energy and Electricity Prices","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A roughness-penalty prior smooths LP-IV impulse responses without changing their first-order asymptotics.","cross_cats":["stat.ME"],"primary_cat":"econ.EM","authors_text":"Masahiro Tanaka","submitted_at":"2026-05-15T13:56:37Z","abstract_excerpt":"This paper introduces a quasi-Bayesian approach for local projection instrumental-variables (LP-IV) estimation. It builds a moment-based quasi-posterior using the generalized method of moments (GMM) objective and applies a roughness-penalty prior to smooth impulse responses over different horizons. The approach maintains the key first-order features of traditional LP-IV methods, while enhancing stability in finite samples and allowing for joint inference through simultaneous bands. Simulations indicate that this regularization decreases root mean squared error compared to standard GMM, especia"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Simulations indicate that this regularization decreases root mean squared error compared to standard GMM, especially at medium and longer horizons. The approach maintains the key first-order features of traditional LP-IV methods, while enhancing stability in finite samples and allowing for joint inference through simultaneous bands.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The roughness-penalty prior can be introduced without changing the first-order asymptotic distribution of the LP-IV estimator, so that the quasi-Bayesian procedure remains consistent for the same parameters as ordinary GMM-based LP-IV.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A quasi-Bayesian LP-IV estimator is proposed that regularizes impulse responses via a roughness-penalty prior on the GMM objective, reducing finite-sample RMSE while preserving first-order asymptotics.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A roughness-penalty prior smooths LP-IV impulse responses without changing their first-order asymptotics.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8efa827ffbd74416859cd37e54a896df94c685392efbe4159c2195766933a076"},"source":{"id":"2605.15966","kind":"arxiv","version":1},"verdict":{"id":"eba1b975-8f4d-4440-b5e7-65d35bf7d1f8","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T18:07:07.789261Z","strongest_claim":"Simulations indicate that this regularization decreases root mean squared error compared to standard GMM, especially at medium and longer horizons. The approach maintains the key first-order features of traditional LP-IV methods, while enhancing stability in finite samples and allowing for joint inference through simultaneous bands.","one_line_summary":"A quasi-Bayesian LP-IV estimator is proposed that regularizes impulse responses via a roughness-penalty prior on the GMM objective, reducing finite-sample RMSE while preserving first-order asymptotics.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The roughness-penalty prior can be introduced without changing the first-order asymptotic distribution of the LP-IV estimator, so that the quasi-Bayesian procedure remains consistent for the same parameters as ordinary GMM-based LP-IV.","pith_extraction_headline":"A roughness-penalty prior smooths LP-IV impulse responses without changing their first-order asymptotics."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15966/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T18:31:18.755062Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T18:21:10.762624Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:44.871671Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.694954Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"cfb7e81c84420d1c1bc8267b604c97d3bb7898aa8a75296558bc8e9ddab5b235"},"references":{"count":44,"sample":[{"doi":"","year":2019,"title":"Barnichon, R. & Brownlees, C. 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