{"id":"W3123335265","doi":"","title":"Measuring consumption smoothing in CEX data","year":2009,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Estimator; Proxy (statistics); Smoothing; Econometrics; Consumption smoothing; Consumer Expenditure Survey; Consumption (sociology); Economics; Survey data collection; Estimation; Statistics; Mathematics; Public economics; Macroeconomics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00956917,0.0004693636,0.0007709651,0.002955977,0.0004519782,0.001661494,0.001109597,0.000897091,0.001206281],"category_scores_gemma":[0.06783998,0.0003317799,0.0003966695,0.005069831,0.0008378852,0.001764382,0.001717175,0.001383131,0.0003966669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000910282,"about_ca_system_score_gemma":0.0007166776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0057835,"about_ca_topic_score_gemma":0.002807145,"domain_scores_codex":[0.9923529,0.004005756,0.0005192089,0.000944411,0.001900439,0.0002772679],"domain_scores_gemma":[0.9354064,0.02800443,0.01398377,0.01676831,0.005271193,0.0005658985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004199858,0.0002350871,0.7482486,0.0002737351,0.0005731139,0.0001588632,0.002175632,0.06293486,0.002610485,0.06279047,0.00409031,0.1154888],"study_design_scores_gemma":[0.00008047657,0.0002529319,0.662084,0.0002238524,0.0001526449,0.0004389606,0.001443473,0.2252245,0.01265692,0.0682977,0.02891208,0.0002323908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7425824,0.0007486888,0.2447867,0.001033666,0.0001108372,0.0001349023,0.003565063,0.0006305704,0.00640699],"genre_scores_gemma":[0.9598047,0.0002723847,0.03588521,0.0001079852,0.00007903558,0.00008847666,0.002727174,0.00005185307,0.0009831627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00956917,"threshold_uncertainty_score":0.0506072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1379855536970807,"score_gpt":0.3217008084094762,"score_spread":0.1837152547123955,"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."}}