{"id":"W2145139914","doi":"10.1016/j.ijforecast.2004.12.007","title":"Clustered panel data models: an efficient approach for nowcasting from poor data","year":2005,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"HEC Montréal","keywords":"Nowcasting; Computer science; Inference; Panel data; Realization (probability); Cluster analysis; Econometrics; Data mining; Field (mathematics); Missing data; Time series; Machine learning; Statistics; Artificial intelligence; Mathematics; Geography","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":[0.009294,0.001383944,0.003106485,0.001991875,0.0008877174,0.002063174,0.003690324,0.002363868,0.004689218],"category_scores_gemma":[0.02796861,0.001501543,0.0017727,0.003891607,0.0006777644,0.002739868,0.002080192,0.003882597,0.001106679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009754256,"about_ca_system_score_gemma":0.001996665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01258406,"about_ca_topic_score_gemma":0.0157316,"domain_scores_codex":[0.9973014,0.001790014,0.000120705,0.0003361405,0.0002875405,0.0001641827],"domain_scores_gemma":[0.9859611,0.009710783,0.0007270359,0.002394423,0.0009853618,0.0002212718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001821248,0.00006543776,0.001509343,0.0001180584,0.0003141722,0.0001214001,0.0001059342,0.8605292,0.0004497572,0.03210849,0.005966919,0.09852915],"study_design_scores_gemma":[0.00001364937,0.00001500811,0.0002406144,0.000009384252,0.00003267598,0.00001212252,0.00001638063,0.971225,0.0001983741,0.0273856,0.0008363136,0.00001485393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004094063,0.0001639502,0.9945432,0.0001641096,0.00004916865,0.00003473877,0.0002691874,0.000349535,0.0003321416],"genre_scores_gemma":[0.3119692,0.001187372,0.6771479,0.0002658236,0.000404741,0.0004174248,0.002848454,0.0003948454,0.005364236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01258406,"threshold_uncertainty_score":0.04915196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.698356989627895,"score_gpt":0.472676036577904,"score_spread":0.225680953049991,"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."}}