{"id":"W115668296","doi":"","title":"Demand for Money in Hungary: An ARDL Approach","year":2011,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Distributed lag; Cointegration; Demand for money; CUSUM; Exchange rate; Income elasticity of demand; Demand curve; Monetary economics; Fisher hypothesis; Econometrics; Aggregate demand; Inflation (cosmology); Broad money; Monetary policy; Real interest rate; Microeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.000486891,0.0002662069,0.0006778263,0.001022352,0.0002150443,0.001544408,0.0005412109,0.0006247402,0.002609732],"category_scores_gemma":[0.001423895,0.0003077516,0.0005314625,0.001238131,0.000305484,0.0007864446,0.0005231096,0.0006229253,0.000385198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001227501,"about_ca_system_score_gemma":0.000668818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01397205,"about_ca_topic_score_gemma":0.006140501,"domain_scores_codex":[0.9996182,0.0001106652,0.00002611213,0.00007372112,0.00007609292,0.00009521308],"domain_scores_gemma":[0.9991096,0.0005565149,0.0001543613,0.00003180077,0.0001012085,0.000046554],"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":[0.0008754096,0.0004453723,0.2944697,0.0009243378,0.0006051377,0.008955211,0.002035787,0.4705385,0.009434731,0.137389,0.009830879,0.06449602],"study_design_scores_gemma":[0.00008970279,0.0002609009,0.1885905,0.00007414754,0.0001918258,0.0004911702,0.001938848,0.7749432,0.001879495,0.02389971,0.007550264,0.00009017195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835502,0.001244647,0.007990649,0.0008694002,0.00003266031,0.00001740709,0.001359535,0.00004687556,0.0048885],"genre_scores_gemma":[0.9976979,0.0004432916,0.0004880085,0.00002499171,0.00002902762,0.000007339936,0.0004413512,0.0000053203,0.0008627954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01397205,"threshold_uncertainty_score":0.02778143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1300199180555299,"score_gpt":0.2407825625864531,"score_spread":0.1107626445309232,"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."}}