{"id":"W1521882791","doi":"10.34989/swp-2010-37","title":"‘Lean' versus ‘Rich' Data Sets: Forecasting during the Great Moderation and the Great Recession","year":2021,"lang":"en","type":"preprint","venue":"Econstor (Econstor)","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Dynamic factor; Recession; Econometrics; Factor (programming language); Computer science; Purchasing; Economics; Moderation; Economy; Macroeconomics; Operations management; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01311181,0.0006890469,0.0008705324,0.001291491,0.0003043961,0.001966842,0.0007470927,0.001162153,0.001010978],"category_scores_gemma":[0.02677426,0.0003813746,0.0009962094,0.001434907,0.0006787408,0.002878162,0.001708274,0.00151746,0.0003106656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006038647,"about_ca_system_score_gemma":0.0004729119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01564637,"about_ca_topic_score_gemma":0.01105777,"domain_scores_codex":[0.9969199,0.001949478,0.0002125452,0.0004247648,0.000297745,0.0001957571],"domain_scores_gemma":[0.9755226,0.0173745,0.002589921,0.002138287,0.00164562,0.0007290004],"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":[0.001616582,0.0002522403,0.3775702,0.000345458,0.001075688,0.0005170257,0.001078798,0.5592608,0.001217974,0.00869856,0.01055721,0.03780948],"study_design_scores_gemma":[0.0003971522,0.0004373164,0.2779899,0.0002000024,0.0003357707,0.0001901396,0.002091729,0.6946399,0.001654719,0.0136053,0.008257127,0.0002008459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872666,0.0005426041,0.006413292,0.001207367,0.00007967368,0.00002586327,0.002646461,0.0001571688,0.001660955],"genre_scores_gemma":[0.9897418,0.0001826946,0.003034833,0.0001772019,0.00007843012,0.00001992335,0.006436264,0.00003644057,0.0002923374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01564637,"threshold_uncertainty_score":0.06934273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1258758011479613,"score_gpt":0.2587826881107284,"score_spread":0.132906886962767,"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."}}