{"id":"W3135695536","doi":"10.2139/ssrn.3503560","title":"Semi-Nonparametric Estimation of Random Coefficient Logit Model for Aggregate Demand","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada; Government of Canada","funders":"","keywords":"Nonparametric statistics; Aggregate (composite); Econometrics; Logit; Estimation; Statistics; Mixed logit; Mathematics; Logistic regression; Economics; Materials science","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":[0.004611925,0.0004166931,0.001047809,0.0009776577,0.0003909651,0.001360281,0.001860972,0.001121422,0.006517916],"category_scores_gemma":[0.01803517,0.0006323354,0.001208044,0.00112176,0.0005935336,0.001836108,0.001103634,0.001331343,0.001144453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006348079,"about_ca_system_score_gemma":0.001093387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01332334,"about_ca_topic_score_gemma":0.01410841,"domain_scores_codex":[0.9976311,0.0016535,0.00007676725,0.0002671699,0.0001959087,0.0001756077],"domain_scores_gemma":[0.9820861,0.01500212,0.0007801365,0.001273628,0.0007138435,0.0001441825],"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":[0.0003846693,0.0003065646,0.01356834,0.0002273303,0.0002434474,0.0002593521,0.0002258996,0.883314,0.001352024,0.03990663,0.002214333,0.05799756],"study_design_scores_gemma":[0.000009139418,0.00002315409,0.00185323,0.000006163044,0.00001002909,0.0000318255,0.00002704975,0.9929213,0.0001634118,0.00476538,0.0001776273,0.00001168923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1715212,0.0001312828,0.8243721,0.0001860293,0.00002557441,0.00009594031,0.001086298,0.0007667518,0.001814777],"genre_scores_gemma":[0.9107589,0.0001493883,0.08252696,0.00004945498,0.00003586529,0.000193946,0.001762598,0.0001030622,0.004419901],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01332334,"threshold_uncertainty_score":0.02649158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224560137769042,"score_gpt":0.2405017712575803,"score_spread":0.2282561698798899,"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."}}