{"id":"W2921479879","doi":"10.1093/ajae/aaz004","title":"On the Treatment of Heteroscedasticity in Crop Yield Data","year":2019,"lang":"en","type":"article","venue":"American Journal of Agricultural Economics","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Heteroscedasticity; Econometrics; Volatility (finance); Economics; Crop insurance; Yield (engineering); Generalization; Autoregressive conditional heteroskedasticity; Asymmetry; Statistics; Mathematics; Agriculture; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000151122,0.0001586717,0.0004006295,0.00001288088,0.00004421943,0.0000322806,0.0006975926,0.00003601618,0.0001038814],"category_scores_gemma":[0.00004419438,0.00003528827,0.0001421195,0.0002342611,0.0001274069,0.0001983768,0.00005412774,0.0001269495,0.00002239794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007934597,"about_ca_system_score_gemma":0.00001157035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003720252,"about_ca_topic_score_gemma":0.0004975477,"domain_scores_codex":[0.9989533,0.00006257565,0.0004678469,0.0002016502,0.0001099014,0.0002047639],"domain_scores_gemma":[0.9984094,0.0007062141,0.000625768,0.0001166338,0.00006649618,0.00007548957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001129156,0.002828727,0.3009466,0.00001608886,0.0003881758,0.00001996744,0.001880902,0.02004193,0.3232688,0.003083125,0.002658577,0.343738],"study_design_scores_gemma":[0.0001946569,0.004481852,0.9870208,0.00004833721,0.00001700604,0.00004010924,0.00287719,0.0000619998,0.003639163,0.00006724799,0.001385246,0.0001664167],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977393,0.00008710152,3.048062e-7,0.001567209,0.0001621289,0.000159365,0.00003990046,0.000003731245,0.0002409759],"genre_scores_gemma":[0.9987992,0.0008047263,0.00003673705,0.0001163339,0.0001444099,0.000001265423,0.00001075686,6.490127e-7,0.00008589496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6860741,"threshold_uncertainty_score":0.1439014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475742506622244,"score_gpt":0.2175779797030045,"score_spread":0.1928205546367821,"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."}}