{"id":"W1611383587","doi":"","title":"Sectoral gross value-added forecasts at the regional level: Is there any information gain?","year":2013,"lang":"en","type":"preprint","venue":"Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)","topic":"German Economic Analysis & Policies","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pooling; Autoregressive model; Econometrics; Gross value added; Distributed lag; Economics; Quarter (Canadian coin); Value (mathematics); Term (time); Geography; Mathematics; Economy; Statistics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008081156,0.0006648839,0.001233807,0.0006677445,0.0008029879,0.0001431418,0.002490183,0.0004628673,0.003488042],"category_scores_gemma":[0.00009060838,0.0007484891,0.00119788,0.0002311561,0.001225712,0.0006822216,0.003335837,0.00113183,0.00142614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008535668,"about_ca_system_score_gemma":0.0002437094,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02206487,"about_ca_topic_score_gemma":0.00355328,"domain_scores_codex":[0.9968451,0.0001995887,0.001052077,0.0008791389,0.0002536009,0.000770541],"domain_scores_gemma":[0.9953639,0.0002582365,0.002010133,0.001898212,0.0001955546,0.0002739526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001349299,0.0008922266,0.1064056,0.001737855,0.008310106,0.00006212499,0.3409779,0.01464026,0.00009005979,0.149688,0.3659774,0.009869058],"study_design_scores_gemma":[0.002639304,0.0002064513,0.3126338,0.0003800289,0.0003173671,0.00004674642,0.01727068,0.1480946,0.00003361552,0.05343757,0.4627987,0.002141085],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9397803,0.001735823,0.001207305,0.004842135,0.0003123006,0.0009091999,0.005660636,0.0000903598,0.04546198],"genre_scores_gemma":[0.9734212,0.002218549,0.002056485,0.001073829,0.000141205,0.000008287329,0.001308624,0.00007401394,0.01969776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3237073,"threshold_uncertainty_score":0.9994966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05759908544312499,"score_gpt":0.2087132395653329,"score_spread":0.1511141541222079,"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."}}