{"id":"W2288716366","doi":"","title":"E¢cient Unit Root Tests using GLS Detrended Data and Covariates in Structural Change Models ¤","year":2005,"lang":"en","type":"article","venue":"","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Covariate; Unit root; Statistics; Mathematics; Statistic; Context (archaeology); Monte Carlo method; Econometrics; Detrended fluctuation analysis; Series (stratigraphy); Scaling","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"opus","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03520147,0.001512655,0.002513965,0.002548675,0.0007259246,0.002005281,0.002357097,0.002233658,0.008117768],"category_scores_gemma":[0.2491036,0.001213397,0.00159751,0.00298369,0.003237554,0.004640453,0.002575168,0.003805017,0.0009417995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008522158,"about_ca_system_score_gemma":0.001716867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00254856,"about_ca_topic_score_gemma":0.002572944,"domain_scores_codex":[0.9683599,0.02605731,0.0007455547,0.002433158,0.00185525,0.0005487801],"domain_scores_gemma":[0.665047,0.3166739,0.006779103,0.008396075,0.002291755,0.000812164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002834572,0.0006566727,0.04474586,0.001107148,0.001875703,0.002027276,0.001644884,0.2249732,0.00475207,0.2464016,0.00803734,0.4609437],"study_design_scores_gemma":[0.0005066096,0.0007006893,0.01494695,0.0001068246,0.0001690792,0.0001903456,0.0002841085,0.6540554,0.002307379,0.3233915,0.003223708,0.0001174053],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08950756,0.0007596353,0.9040246,0.001438909,0.0001574872,0.0002492481,0.0005434554,0.0006395078,0.002679488],"genre_scores_gemma":[0.7458082,0.0005675856,0.2490837,0.000317785,0.0002310587,0.0005784438,0.001051339,0.0002212198,0.002140671],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03520147,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3532346481585663,"score_gpt":0.3074809760900428,"score_spread":0.04575367206852343,"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."}}