{"id":"W2946161056","doi":"10.1002/jae.2701","title":"Mixed‐frequency models with moving‐average components","year":2019,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; Université du Québec à Montréal","funders":"","keywords":"Econometrics; Nowcasting; Ordinary least squares; Moving average; Context (archaeology); Autoregressive–moving-average model; Autoregressive model; Component (thermodynamics); Statistics; Ranking (information retrieval); Mathematics; Computer science; Artificial intelligence","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001079481,0.0002955383,0.001029673,0.001686676,0.00007629806,0.0001404315,0.0005759818,0.0001582974,0.001178141],"category_scores_gemma":[0.00002721844,0.0002963878,0.0002382441,0.0006152009,0.00005049347,0.0009770392,0.00006547658,0.0004261095,0.001568932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003341894,"about_ca_system_score_gemma":0.00003947826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007824173,"about_ca_topic_score_gemma":0.000003241793,"domain_scores_codex":[0.9974819,0.000008469157,0.00151056,0.0004043602,0.00007569863,0.0005190566],"domain_scores_gemma":[0.9972439,0.0001251594,0.001836829,0.0004759346,0.00003347039,0.0002847483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004270159,0.0004563802,0.1052409,0.0001470063,0.0008472311,0.00003089685,0.0009186736,0.3735771,0.0000861706,0.514353,0.0017517,0.002163914],"study_design_scores_gemma":[0.01482581,0.002428738,0.1144074,0.0001247204,0.0001155689,0.0004850581,0.0006503494,0.1705121,0.0005512348,0.643735,0.04894284,0.003221295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8618122,0.00066457,0.005857541,0.0001485905,0.000654968,0.0002292549,0.00008567824,0.00001616109,0.130531],"genre_scores_gemma":[0.9924929,0.0002940881,0.006052646,0.0004457487,0.0002210555,0.000004100159,0.00001144709,0.00005258192,0.0004254999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2030651,"threshold_uncertainty_score":0.9999488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1260551931471932,"score_gpt":0.1965037436962879,"score_spread":0.07044855054909469,"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."}}