{"id":"W4401467573","doi":"10.1002/jae.3085","title":"Heterogeneous autoregressions in short T panel data models","year":2024,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Estimator; Autoregressive model; Moment (physics); Mathematics; Monte Carlo method; Variance (accounting); Context (archaeology); Method of moments (probability theory); Panel data; Statistics; Econometrics; Sample (material); Applied mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001432499,0.0001859206,0.0006898316,0.002880112,0.00004811163,0.0002357048,0.001021264,0.0001393631,0.0005103164],"category_scores_gemma":[0.00008860509,0.000182114,0.0001776098,0.001923353,0.00003692232,0.0007676807,0.0002483701,0.0003962484,0.0002581357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001827907,"about_ca_system_score_gemma":0.00006417231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007545527,"about_ca_topic_score_gemma":0.00003454709,"domain_scores_codex":[0.9976586,0.000007633307,0.001469512,0.0004868043,0.00007867255,0.0002988237],"domain_scores_gemma":[0.9987105,0.0001766217,0.0002458753,0.0006750216,0.00002686845,0.0001651011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002891438,0.001313609,0.04270533,0.0005289657,0.002741509,0.001224935,0.002603101,0.2916771,0.0001203535,0.4213309,0.02765929,0.2078058],"study_design_scores_gemma":[0.0006646217,0.0001318225,0.004586829,0.00009798836,0.0001148201,0.0001412974,0.0001902651,0.722927,0.00008509967,0.1309697,0.1393106,0.0007798539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6780161,0.0717162,0.135551,0.001503925,0.005171565,0.0006391012,0.005111066,0.0001460126,0.102145],"genre_scores_gemma":[0.9947627,0.002616297,0.001962303,0.0001195564,0.0003161105,0.000004386489,0.0001089663,0.00003232351,0.00007740201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4312499,"threshold_uncertainty_score":0.7426392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2229368298105021,"score_gpt":0.2756896937953359,"score_spread":0.05275286398483375,"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."}}