{"id":"W2753033282","doi":"10.1017/s1365100517001092","title":"TREND–CYCLE–SEASONAL INTERACTIONS: IDENTIFICATION AND ESTIMATION","year":2018,"lang":"en","type":"article","venue":"Macroeconomic Dynamics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Econometrics; Economics; Identification (biology); Recession; Estimation; Business cycle; Uncorrelated; Consumption (sociology); Seasonal adjustment; Seasonality; Great recession; Statistics; Macroeconomics; Mathematics; Keynesian economics; Variable (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003954625,0.0001727508,0.0002806211,0.0002525969,0.0002019342,0.0002200503,0.0001861449,0.00007621828,0.0009249526],"category_scores_gemma":[0.00004407769,0.0002299991,0.00007572732,0.00007138685,0.000169755,0.0007047776,0.00006552287,0.000115919,0.002174528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003082282,"about_ca_system_score_gemma":0.000008168361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003950713,"about_ca_topic_score_gemma":0.0006007518,"domain_scores_codex":[0.9985521,0.000008472271,0.0006819862,0.000452463,0.00001414817,0.0002908548],"domain_scores_gemma":[0.9991198,0.00004540466,0.0003898698,0.0003277358,0.000005944307,0.0001113024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001596658,0.0002205535,0.1097374,0.00009252365,0.0003977175,0.000003173997,0.001910445,0.01360094,0.00004645653,0.7524109,0.007672032,0.1137481],"study_design_scores_gemma":[0.0003193634,0.00003713656,0.04860619,0.000006343503,0.000007393331,0.00002184539,0.00004944601,0.8885082,0.00001683345,0.0539307,0.008270422,0.0002261435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698781,0.0001738663,0.01223155,0.001317586,0.0009600478,0.0001629907,0.0005749138,0.00007017779,0.01463075],"genre_scores_gemma":[0.9955316,0.00007151419,0.001140803,0.0003338252,0.0002760622,0.0000203828,0.0002038335,0.0000283673,0.002393667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8749073,"threshold_uncertainty_score":0.9999883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02764375108590085,"score_gpt":0.24775909944221,"score_spread":0.2201153483563091,"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."}}