{"id":"W4378840090","doi":"10.1016/j.jeconom.2022.10.011","title":"Semi-nonparametric estimation of random coefficients logit model for aggregate demand","year":2023,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Estimator; Sieve (category theory); Nonparametric statistics; Logit; Mathematics; Monte Carlo method; Econometrics; Applied mathematics; Mathematical optimization; 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":[0.00493134,0.000416473,0.001148999,0.0009161046,0.0004075952,0.001377193,0.001749065,0.001041175,0.005619328],"category_scores_gemma":[0.01955659,0.0007110762,0.001175917,0.001072196,0.0006379359,0.001937264,0.001093157,0.001368169,0.0008491146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006817598,"about_ca_system_score_gemma":0.001206516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01597088,"about_ca_topic_score_gemma":0.01625648,"domain_scores_codex":[0.9978039,0.001580429,0.00007590253,0.0002162975,0.000166928,0.0001566691],"domain_scores_gemma":[0.9783462,0.01877265,0.0007646515,0.001216231,0.0007409821,0.0001594456],"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":[0.0003032953,0.0002784482,0.01099934,0.0001592264,0.0002066553,0.0001940118,0.0001655939,0.9048446,0.0009442355,0.03559724,0.001627419,0.04467997],"study_design_scores_gemma":[0.000008519685,0.00001651274,0.001238076,0.000004228053,0.000008244548,0.00001945646,0.00001898717,0.9939178,0.0001040013,0.004527739,0.0001280148,0.000008487425],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1843022,0.0001672062,0.8119812,0.0002402445,0.00002880265,0.00008988188,0.0009001305,0.0006167093,0.001673595],"genre_scores_gemma":[0.9182084,0.0001766775,0.07585423,0.00004933787,0.00003987109,0.0001621927,0.001384146,0.00008947832,0.004035692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01597088,"threshold_uncertainty_score":0.03175586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1068756634493719,"score_gpt":0.2751392369343563,"score_spread":0.1682635734849844,"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."}}