{"id":"W2792499961","doi":"10.1017/s1365100517001006","title":"THE DEMAND FOR LIQUID ASSETS: EVIDENCE FROM THE MINFLEX LAURENT DEMAND SYSTEM WITH CONDITIONALLY HETEROSKEDASTIC ERRORS","year":2018,"lang":"en","type":"article","venue":"Macroeconomic Dynamics","topic":"Economics of Agriculture and Food Markets","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Homoscedasticity; Economics; Heteroscedasticity; Curvature; Econometrics; Merge (version control); Speculative demand; Covariance matrix; Covariance; Inference; Mathematical economics; Demand for money; Mathematics; Computer science; Monetary policy; Monetary economics","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":[0.004609378,0.0004547169,0.0009965351,0.0009325841,0.0006535349,0.002000236,0.001319223,0.00146859,0.005109156],"category_scores_gemma":[0.0198103,0.0005742922,0.001007912,0.001743548,0.001405105,0.00200606,0.001330613,0.001719819,0.001118863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054903,"about_ca_system_score_gemma":0.0006800992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02564367,"about_ca_topic_score_gemma":0.01756135,"domain_scores_codex":[0.9986421,0.0005804501,0.0001098983,0.0002934494,0.0002183949,0.000155625],"domain_scores_gemma":[0.9601686,0.02258942,0.01124215,0.003298159,0.001662687,0.001039025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00135037,0.0006467798,0.8820934,0.0001379336,0.0004436922,0.0009087825,0.001553651,0.04829677,0.001513141,0.03701339,0.002890002,0.02315199],"study_design_scores_gemma":[0.0003412018,0.0005887191,0.5457814,0.0000924895,0.0003431565,0.0005773906,0.002296465,0.3856884,0.00237221,0.0573041,0.004380961,0.0002336632],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962912,0.0001429816,0.001928224,0.0004599268,0.000006026319,0.00000668383,0.0003742114,0.00002172345,0.000768993],"genre_scores_gemma":[0.9977843,0.0001694375,0.0004146634,0.00006056901,0.00001918691,0.000005977228,0.0009875012,0.000009285672,0.000549202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02564367,"threshold_uncertainty_score":0.05098885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01582326035803053,"score_gpt":0.2160707600670321,"score_spread":0.2002474997090015,"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."}}