{"id":"W3154062084","doi":"10.5539/ijef.v13n5p9","title":"Forecasting the Market Equity Premium: Does Nonlinearity Matter?","year":2021,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Smoothing; Estimator; Overfitting; Nonlinear system; Equity premium puzzle; Economics; Equity (law); Smoothing spline; Spline (mechanical); Computer science; Risk premium; Mathematics; Statistics; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003447172,0.000619057,0.0006586905,0.0007057169,0.0002657294,0.001417443,0.0007881303,0.0008566693,0.001481566],"category_scores_gemma":[0.01488327,0.000265675,0.0005055081,0.0006774112,0.0006395003,0.002272673,0.0009399462,0.001375461,0.0004841508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002925815,"about_ca_system_score_gemma":0.000889812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003520227,"about_ca_topic_score_gemma":0.003595645,"domain_scores_codex":[0.9994007,0.0002327957,0.00003307569,0.0001329824,0.0001410023,0.00005935166],"domain_scores_gemma":[0.9945666,0.003764318,0.0005917888,0.0003574296,0.0005913911,0.0001285421],"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":[0.000498922,0.0002637453,0.2023559,0.0001628167,0.000407303,0.0003703999,0.0003527922,0.4444889,0.007920957,0.02938733,0.002302387,0.3114885],"study_design_scores_gemma":[0.00001717922,0.00009765036,0.01224452,0.00001790472,0.00005996315,0.00004684207,0.00005583074,0.9636796,0.001361066,0.02182224,0.0005676813,0.00002957343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.406102,0.0006770916,0.5848613,0.002910258,0.0001191098,0.00005791245,0.0002408806,0.0003745613,0.004656921],"genre_scores_gemma":[0.9713134,0.0003011372,0.02673548,0.0001194605,0.0001069043,0.00001995153,0.0001675514,0.00002618051,0.001209943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003520227,"threshold_uncertainty_score":0.01823062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04173744339713462,"score_gpt":0.2454775531285872,"score_spread":0.2037401097314525,"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."}}