{"id":"W3124854690","doi":"","title":"Misspecification-robust inference in linear asset pricing models with irrelevant risk factors","year":2013,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Econometrics; Capital asset pricing model; Selection (genetic algorithm); Relevance (law); Model selection; Computer science; Asset (computer security); Linear model; Factor analysis; Indirect Inference; Economics; Mathematics; Machine learning; Artificial intelligence; Statistics","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":[0.03543394,0.002012381,0.002895983,0.00189821,0.000819203,0.002547075,0.003552094,0.002494348,0.002310995],"category_scores_gemma":[0.190715,0.00166477,0.002960745,0.00231983,0.003851986,0.004248598,0.003707371,0.004539431,0.0005005675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389134,"about_ca_system_score_gemma":0.002276838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006254618,"about_ca_topic_score_gemma":0.00377154,"domain_scores_codex":[0.9814712,0.01303271,0.0007518443,0.002028009,0.002012603,0.0007036022],"domain_scores_gemma":[0.8212774,0.1556341,0.008318605,0.01166263,0.002475343,0.0006319617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003466292,0.0001156479,0.008234629,0.0002619328,0.0007541705,0.0005863652,0.0004182897,0.7380162,0.001335695,0.1914754,0.001963331,0.05649165],"study_design_scores_gemma":[0.00003482182,0.00003662914,0.0006604694,0.00001770309,0.00007248919,0.00007081735,0.00001260291,0.8739033,0.0006722673,0.1241722,0.0003212931,0.00002554155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01861494,0.0003108064,0.9796444,0.0003479857,0.00003165309,0.00002810762,0.00006539768,0.0003391794,0.0006175697],"genre_scores_gemma":[0.7591933,0.0009056662,0.2353034,0.000548659,0.0003655277,0.0002025338,0.0005170093,0.0003374839,0.002626415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03543394,"threshold_uncertainty_score":0.1873949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1704092932746466,"score_gpt":0.3062614551770968,"score_spread":0.1358521619024502,"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."}}