{"id":"W2018992809","doi":"10.1016/s0304-3932(01)00066-6","title":"Precautionary saving and portfolio allocation: DP by GMM","year":2001,"lang":"en","type":"article","venue":"Journal of Monetary Economics","topic":"Financial Literacy, Pension, Retirement Analysis","field":"Business, Management and Accounting","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; McMaster University","funders":"","keywords":"Economics; Portfolio; Consumption (sociology); Econometrics; Portfolio allocation; Parametric statistics; Portfolio optimization; Generalized method of moments; Constant (computer programming); Replicating portfolio; Microeconomics; Financial economics; Computer science; Panel data; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000418333,0.0001374957,0.0002693793,0.0002620221,0.0001217745,0.0001473043,0.0001661969,0.00005590787,0.0004071043],"category_scores_gemma":[0.00003980466,0.0001374599,0.0001114591,0.0001574766,0.00003575899,0.001914418,0.00007208933,0.0001262983,0.0000428856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004861661,"about_ca_system_score_gemma":0.00002472374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001323181,"about_ca_topic_score_gemma":0.00006353664,"domain_scores_codex":[0.9989632,0.000007472555,0.000606722,0.0001568817,0.0001069668,0.0001587945],"domain_scores_gemma":[0.9989701,0.00002732601,0.0007031648,0.0001362201,0.0001389213,0.00002426193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008866562,0.00007424102,0.9591063,0.00002423313,0.00005174178,0.00002910912,0.00002806841,0.001323966,0.0001725123,0.001012443,0.03254059,0.005548095],"study_design_scores_gemma":[0.0008361156,0.00004041354,0.6770774,0.00007331001,0.0002754996,0.0001148451,0.0001005621,0.0230692,0.00002464975,0.004562885,0.2934609,0.000364197],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950251,0.001406977,0.00005144616,0.001025514,0.0002827642,0.00006553622,0.000001786103,0.00001188559,0.002128975],"genre_scores_gemma":[0.9948543,0.001473635,0.0005630471,0.001333881,0.001207836,9.965364e-7,0.00004058668,0.00001746066,0.0005082876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2820289,"threshold_uncertainty_score":0.5605454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009434180932383778,"score_gpt":0.1881696813137741,"score_spread":0.1787355003813903,"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."}}