{"id":"W4241228865","doi":"10.1093/jjfinec/nbz037","title":"Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures?","year":2019,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Estimator; Consumer confidence index; Discounting; Computer science; Econometrics; Index (typography); Social media; Measure (data warehouse); Sample (material); Sampling (signal processing); Sentiment analysis; Statistics; Data mining; Machine learning; Economics; Finance; Mathematics; Marketing; Business","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":[0.005222417,0.0005945204,0.0004728659,0.0008064616,0.0002848708,0.001680818,0.0006128504,0.000986314,0.001789893],"category_scores_gemma":[0.03374094,0.0002288582,0.0004513635,0.0006999202,0.0002660354,0.002468766,0.0006144034,0.001137564,0.0005494067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003935195,"about_ca_system_score_gemma":0.0004581965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007083572,"about_ca_topic_score_gemma":0.006787093,"domain_scores_codex":[0.9990854,0.0004979292,0.00005475642,0.000147043,0.0001312116,0.00008350679],"domain_scores_gemma":[0.9789937,0.0156378,0.001696389,0.001540849,0.001746907,0.0003843174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002274445,0.0006575686,0.4112561,0.0001771441,0.0006252054,0.0001713791,0.0002591024,0.3585197,0.004983868,0.007666562,0.006408349,0.2070007],"study_design_scores_gemma":[0.00003279256,0.0001293405,0.02496302,0.00003184891,0.00004778744,0.00001654466,0.0001255049,0.9673162,0.002138444,0.004463479,0.0007100944,0.00002490806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9107113,0.0005335387,0.08037005,0.002403858,0.0002424351,0.00003458602,0.0009061694,0.0003784846,0.004419582],"genre_scores_gemma":[0.9930308,0.00005754224,0.006323462,0.00005774613,0.00006830971,0.000004887891,0.0002428768,0.00001079557,0.0002036626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007083572,"threshold_uncertainty_score":0.02761906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1485670598544888,"score_gpt":0.3442444046208788,"score_spread":0.19567734476639,"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."}}