{"id":"W1625383456","doi":"","title":"Estimating Intertemporal Allocation Parameters using Simulated Expectation Errors","year":2006,"lang":"en","type":"preprint","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Core Research for Evolutional Science and Technology; Social Sciences and Humanities Research Council of Canada; Danmarks Grundforskningsfond; National Research Foundation","keywords":"Estimator; Structural estimation; Econometrics; Estimation; Residual; Sample (material); Generalized method of moments; Euler equations; Consumption (sociology); Computer science; Mathematics; Economics; Statistics; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007094417,0.0006328461,0.0009669943,0.0009527935,0.000247877,0.001418671,0.001624647,0.001613133,0.003344442],"category_scores_gemma":[0.04399095,0.0007323822,0.001000467,0.001482693,0.0009227062,0.002355009,0.001275791,0.001803064,0.0005940826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162357,"about_ca_system_score_gemma":0.001085759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007700185,"about_ca_topic_score_gemma":0.005517235,"domain_scores_codex":[0.9966938,0.002380245,0.0001536691,0.0003764325,0.00023534,0.0001605043],"domain_scores_gemma":[0.9743935,0.01986181,0.002084227,0.002774403,0.0007056654,0.0001804699],"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":[0.0001638543,0.00005949294,0.006417333,0.000058998,0.0001150049,0.00005128852,0.0002090012,0.8544703,0.0004290348,0.1153168,0.0005631983,0.02214575],"study_design_scores_gemma":[0.00003085872,0.00002254862,0.0008792994,0.00002352359,0.00001629913,0.00001479418,0.00003222787,0.9285288,0.0006235532,0.06902707,0.000784778,0.00001624454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08726732,0.0001040399,0.9102131,0.0002820567,0.00002388099,0.00005442256,0.0002936113,0.0002530885,0.001508556],"genre_scores_gemma":[0.8005521,0.0002241423,0.1956545,0.00009742525,0.00003595369,0.0002057012,0.0009726061,0.00009846656,0.002159059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007700185,"threshold_uncertainty_score":0.03751928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1130268918275881,"score_gpt":0.2801327390657359,"score_spread":0.1671058472381479,"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."}}