{"id":"W4389046557","doi":"10.3390/forecast5040036","title":"Macroeconomic Predictions Using Payments Data and Machine Learning","year":2023,"lang":"en","type":"article","venue":"Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Overfitting; Nowcasting; Interpretability; Payment; Computer science; Econometrics; Value (mathematics); Machine learning; Economics; Artificial neural network","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":[0.001971162,0.0007551954,0.000480816,0.002180949,0.0006695199,0.001961452,0.000619899,0.0006714819,0.002140129],"category_scores_gemma":[0.01401753,0.0002335388,0.0004384099,0.003502557,0.0005038153,0.001200416,0.0007546879,0.000861306,0.0005481121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004642441,"about_ca_system_score_gemma":0.005972702,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6983125,"about_ca_topic_score_gemma":0.698608,"domain_scores_codex":[0.9990024,0.0002901541,0.00006143848,0.0001393025,0.0003533104,0.000153382],"domain_scores_gemma":[0.9940122,0.002244882,0.00077923,0.0003987706,0.00222681,0.0003381464],"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":[0.0003650886,0.0001697511,0.4559418,0.0001560598,0.0002204701,0.0003068533,0.0001895423,0.4474734,0.0005402545,0.007467802,0.01363623,0.07353282],"study_design_scores_gemma":[0.00004517865,0.00008038298,0.2498451,0.0001567763,0.00006714327,0.00005397697,0.000508782,0.7270646,0.001477103,0.008304418,0.01231486,0.00008169882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9325697,0.001106812,0.01652684,0.003725218,0.0001488992,0.0001079281,0.02659375,0.0006983161,0.01852259],"genre_scores_gemma":[0.9815607,0.0003460138,0.004201695,0.00008752688,0.00004615396,0.00001463314,0.01200192,0.00001815022,0.001723378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6983125,"threshold_uncertainty_score":0.6069283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3317320262824558,"score_gpt":0.2771811568458795,"score_spread":0.05455086943657628,"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."}}