{"id":"W3122215856","doi":"10.34989/swp-2017-2","title":"A Dynamic Factor Model for Nowcasting Canadian GDP Growth","year":2021,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Nowcasting; Dynamic factor; Univariate; Design for manufacturability; Gross domestic product; Econometrics; Computer science; Economics; Multivariate statistics; Geography; Engineering; Macroeconomics; Meteorology; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001886358,0.0009114658,0.0007089798,0.001111483,0.0007208257,0.001811958,0.001817849,0.001203757,0.003055162],"category_scores_gemma":[0.006226064,0.0005378567,0.001056156,0.001597544,0.0008750307,0.001366154,0.000580618,0.001843294,0.0005199074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006706318,"about_ca_system_score_gemma":0.005796003,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7842189,"about_ca_topic_score_gemma":0.5991769,"domain_scores_codex":[0.9993413,0.0001609948,0.00002328414,0.0002080086,0.0001525365,0.0001139875],"domain_scores_gemma":[0.9988747,0.0004914246,0.0001305591,0.0000943663,0.0003329912,0.0000760311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006162989,0.00001350665,0.003561528,0.00003857575,0.00005482965,0.00006780096,0.00009031627,0.9201702,0.0003209648,0.05467413,0.004501,0.01644556],"study_design_scores_gemma":[0.0000174921,0.000009337648,0.001815763,0.00001463953,0.00002438364,0.00001803921,0.00002585138,0.981874,0.0001606661,0.01045884,0.005549836,0.0000311431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1918639,0.00306316,0.7668046,0.004920598,0.0006431424,0.0001557734,0.01103387,0.001528372,0.01998653],"genre_scores_gemma":[0.909431,0.001895025,0.06443931,0.0002792266,0.0001826969,0.0001252652,0.006842342,0.0002249386,0.01658024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2157811,"threshold_uncertainty_score":0.4341037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1129402366875171,"score_gpt":0.2911997724676211,"score_spread":0.178259535780104,"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."}}