{"id":"W4390382672","doi":"10.1016/j.jeconom.2023.105642","title":"Semiparametric Bayesian estimation of dynamic discrete choice models","year":2023,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mixed logit; Bayesian probability; Inference; Discrete choice; Computer science; Bayesian inference; Extreme value theory; Generalized extreme value distribution; Mathematical optimization; Semiparametric model; Monte Carlo method; Econometrics; Mathematics; Nonparametric statistics; Statistics; Logistic regression; Machine learning; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001051968,0.0001149702,0.0003113734,0.004865054,0.00006118957,0.0001372329,0.0002678146,0.00004684892,0.0001116548],"category_scores_gemma":[0.000891129,0.0001080513,0.0001461876,0.006029545,0.00002304687,0.001667032,0.00009242931,0.0001673149,0.00003318281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000517433,"about_ca_system_score_gemma":0.00002937336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008224171,"about_ca_topic_score_gemma":0.0000131847,"domain_scores_codex":[0.9987845,0.000009495058,0.0006711364,0.0001147857,0.0002303038,0.0001898109],"domain_scores_gemma":[0.998295,0.0003880627,0.0009378452,0.00015284,0.0002044692,0.00002175682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000101074,0.000197816,0.2863936,0.000622246,0.0001650805,0.00003659623,0.00007818207,0.0953017,0.0001236344,0.001889548,0.00232995,0.6127605],"study_design_scores_gemma":[0.0005875849,0.00003905391,0.2075713,0.0000656372,0.0001717694,0.000007383987,0.00007783455,0.7849644,0.000009814071,0.005252296,0.001072661,0.0001802042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772636,0.0003488724,0.0166633,0.0002375359,0.000641843,0.0001051902,0.000004409038,0.00003352249,0.004701698],"genre_scores_gemma":[0.9988055,0.0001205227,0.0007247801,0.00005683297,0.0001403889,0.000001345831,0.000008388311,0.00001900916,0.0001232012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6896627,"threshold_uncertainty_score":0.4406204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07988496823799046,"score_gpt":0.2721479072582231,"score_spread":0.1922629390202326,"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."}}