{"id":"W3123609253","doi":"10.2139/ssrn.1021953","title":"Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination Across Models and Observation Windows","year":2007,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trinity College","funders":"Rheinische Friedrich-Wilhelms-Universität Bonn","keywords":"Weighting; Complement (music); Forecast error; Econometrics; Forecast skill; Estimation; Computer science; Statistics; Mathematics; Economics","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":[0.005948748,0.001098587,0.0009983444,0.00102032,0.0006301819,0.001763333,0.0005693164,0.001605286,0.00121649],"category_scores_gemma":[0.01788222,0.0006290879,0.001365079,0.0008311103,0.0004187482,0.002453875,0.001027158,0.001425591,0.0001478007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000801346,"about_ca_system_score_gemma":0.001011097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03098162,"about_ca_topic_score_gemma":0.01813205,"domain_scores_codex":[0.9987815,0.0007352155,0.00006832123,0.0001978655,0.0001143285,0.0001027659],"domain_scores_gemma":[0.9830217,0.01490052,0.0007303002,0.0004475914,0.0006620664,0.0002378122],"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":[0.0003802719,0.00003899134,0.01051673,0.00003615776,0.0001760831,0.0001081529,0.00007588825,0.9817169,0.0005316923,0.001015695,0.00028262,0.005120921],"study_design_scores_gemma":[0.00001969673,0.00007879075,0.003979873,0.000007066378,0.00006268891,0.00001611932,0.00004047415,0.9943211,0.0005735687,0.0008091755,0.00007213619,0.00001939282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714145,0.0002606822,0.0262966,0.0003499093,0.00002648172,0.00001708396,0.0004746533,0.0001658296,0.0009942327],"genre_scores_gemma":[0.9951752,0.00006283102,0.003957172,0.00001261254,0.00001452629,0.000008603357,0.0004773752,0.0000264311,0.0002652454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03098162,"threshold_uncertainty_score":0.06160259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1185251944764588,"score_gpt":0.2992897100281448,"score_spread":0.180764515551686,"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."}}