{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004349496,0.0001346954,0.0002855594,0.0005225241,0.00005679511,0.00003208578,0.0001885536,0.00007305498,0.0009394148],"category_scores_gemma":[0.004211992,0.00006791893,0.00003794615,0.001288166,0.00006972263,0.00008794244,0.00005720376,0.0000926987,0.00009252423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007629921,"about_ca_system_score_gemma":0.0000242452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000519474,"about_ca_topic_score_gemma":0.000002894907,"domain_scores_codex":[0.9990442,0.00005720662,0.0002692378,0.0002388583,0.0001501478,0.0002403227],"domain_scores_gemma":[0.9942316,0.005058503,0.0001510565,0.000367229,0.00006077779,0.0001308168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004739587,0.0001713348,0.08074494,0.00005155534,0.00003339134,0.00001513097,0.00004091142,5.528067e-7,0.00004818833,0.3373771,0.00497784,0.5764917],"study_design_scores_gemma":[0.001801722,0.0009681057,0.04638031,0.0002737963,0.00006608005,0.000030881,0.00005439465,0.0008444312,0.003006381,0.935549,0.01029141,0.0007335394],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2673794,0.0001147602,0.696307,0.000336754,0.0002100513,0.0001900587,0.00004260392,0.00009918577,0.03532018],"genre_scores_gemma":[0.7532449,0.0000469382,0.2453571,0.00007032401,0.00005321773,0.00001341321,7.452848e-7,0.00002129453,0.00119203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5981718,"threshold_uncertainty_score":0.9999738,"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."}}