{"id":"W2516855501","doi":"10.3390/econometrics4030036","title":"Nonparametric Regression with Common Shocks","year":2016,"lang":"en","type":"article","venue":"Econometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Yale University","keywords":"Estimator; Kernel regression; Mathematics; Kernel (algebra); Nonparametric statistics; Econometrics; Conditional probability distribution; Nonparametric regression; Kernel density estimation; Applied mathematics; Statistics; Conditional expectation; Conditional variance; Discrete mathematics","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":[0.01179325,0.001094065,0.002626213,0.002070238,0.0007433273,0.00307725,0.003340791,0.00326986,0.002239654],"category_scores_gemma":[0.04776046,0.001170299,0.002108459,0.002980467,0.003766854,0.005873023,0.003889585,0.003614057,0.0005044728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001773374,"about_ca_system_score_gemma":0.001370876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005611002,"about_ca_topic_score_gemma":0.003160171,"domain_scores_codex":[0.9917679,0.004368398,0.0003918494,0.001688981,0.001140416,0.0006424458],"domain_scores_gemma":[0.9679829,0.02259726,0.004238191,0.003532711,0.001228158,0.0004207555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008905017,0.00007990692,0.004513687,0.0001253975,0.0002599058,0.0005563173,0.0001332642,0.3491982,0.0006067684,0.6239418,0.0007767498,0.01971902],"study_design_scores_gemma":[0.00001890219,0.00003812056,0.0009227619,0.00002359768,0.00003985054,0.0001080842,0.0000357757,0.8148925,0.0002447989,0.1823755,0.001273849,0.00002621193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02914954,0.0005465206,0.9677933,0.0005901066,0.00006013854,0.00002974228,0.0001362097,0.0001186588,0.001575765],"genre_scores_gemma":[0.8967806,0.001285663,0.09124316,0.0003417548,0.0004007402,0.0002097542,0.0005031089,0.00007442785,0.00916084],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01179325,"threshold_uncertainty_score":0.06236935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.126417340276385,"score_gpt":0.3593253466192968,"score_spread":0.2329080063429117,"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."}}