{"id":"W3121922098","doi":"","title":"Identifying the Shocks Driving Inflation in China","year":2007,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Wilfrid Laurier University","funders":"","keywords":"Deflation; Inflation (cosmology); Economics; China; Monetary policy; Aggregate supply; Identification (biology); Aggregate (composite); Macroeconomics; Inflation targeting; Principal (computer security); Monetary economics; Keynesian economics; Aggregate demand; Geography; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001261469,0.000458286,0.0005366277,0.001254235,0.0006380526,0.0008516585,0.0003798738,0.0004979935,0.0008003764],"category_scores_gemma":[0.002214232,0.0003044681,0.0004717291,0.001639597,0.000331466,0.0005105284,0.0008133503,0.0004432582,0.0001214919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001767384,"about_ca_system_score_gemma":0.00264414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09609091,"about_ca_topic_score_gemma":0.06013898,"domain_scores_codex":[0.9997187,0.00005747024,0.00002228845,0.00005790393,0.00005492234,0.00008871302],"domain_scores_gemma":[0.9992552,0.0001784235,0.0002520841,0.00007063238,0.0001398213,0.0001037676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000284227,0.0001052537,0.8137974,0.00009172466,0.0001591694,0.0007932278,0.0008394499,0.1290477,0.003904593,0.01432877,0.001382546,0.03526598],"study_design_scores_gemma":[0.00004593529,0.00008728723,0.6767665,0.00002497985,0.00007537198,0.00006304764,0.00065439,0.3126768,0.001704004,0.00690364,0.0009510395,0.00004702878],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979134,0.00006460751,0.0009265311,0.0001404478,0.000004134803,0.00001060671,0.0001643029,0.00001159395,0.0007643623],"genre_scores_gemma":[0.9990339,0.0001049739,0.0002858956,0.00001321547,0.000004494243,0.000005169439,0.0002713346,0.000002494672,0.000278434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09609091,"threshold_uncertainty_score":0.1910632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0995768509398885,"score_gpt":0.324952148051193,"score_spread":0.2253752971113046,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}