{"id":"W2942581885","doi":"","title":"Nowcasting US GDP Growth in `Pseudo\\' Real Time Using Various Econometric Models","year":2019,"lang":"en","type":"article","venue":"","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nowcasting; Econometrics; Real gross domestic product; Gross domestic product; Lasso (programming language); Econometric model; Quarter (Canadian coin); Dynamic factor; Economics; Computer science; Macroeconomics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001690135,0.0001322518,0.0002756156,0.0008149536,0.00008613867,0.0001982347,0.000641895,0.00009074371,0.001113729],"category_scores_gemma":[0.0002883398,0.0001061927,0.00007908279,0.002379671,0.00003455201,0.0004309379,0.000183178,0.0001349496,0.001143369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009940093,"about_ca_system_score_gemma":0.00006145652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001785218,"about_ca_topic_score_gemma":0.00003812248,"domain_scores_codex":[0.998067,0.00004815437,0.0006287748,0.0005308824,0.0004165637,0.0003086475],"domain_scores_gemma":[0.9983092,0.0007449283,0.0002005318,0.0005043409,0.0001583017,0.00008271543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007995448,0.0007543468,0.3599303,0.0000421673,0.00004469219,0.00003619412,0.001440789,0.226133,0.01549542,0.3180252,0.01557787,0.06244013],"study_design_scores_gemma":[0.0001337435,0.00003187889,0.002448968,0.00001120797,0.000002745028,0.00001610594,0.00002985316,0.9148571,0.0002570463,0.08187895,0.0001800347,0.0001523167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8303189,0.000005670365,0.02180681,0.00008378237,0.000048592,0.0002509337,0.000007989348,0.0001085577,0.1473687],"genre_scores_gemma":[0.9179963,0.000006249385,0.07801435,0.00007483058,0.00003117717,0.000009528902,0.000002066927,0.00001525098,0.003850272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6887242,"threshold_uncertainty_score":0.9997994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1488669466035495,"score_gpt":0.3624562357587408,"score_spread":0.2135892891551913,"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."}}