{"id":"W2113243636","doi":"10.7202/1036914ar","title":"A Shrinkage Instrumental Variable Estimator for Large Datasets","year":2016,"lang":"en","type":"article","venue":"L Actualité économique","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Instrumental variable; Shrinkage estimator; Shrinkage; Consistency (knowledge bases); Inference; Monte Carlo method; Computer science; Variable (mathematics); Statistics; Stein's unbiased risk estimate; Bias of an estimator; Mathematics; Econometrics; Algorithm; Applied mathematics; Minimum-variance unbiased estimator; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0199743,0.0009210382,0.00174211,0.002377907,0.0006885109,0.001581931,0.002611302,0.001934441,0.003954596],"category_scores_gemma":[0.0932058,0.0007749519,0.001199458,0.002700218,0.001752226,0.003398484,0.003397673,0.003360718,0.001305028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006017112,"about_ca_system_score_gemma":0.001677991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008806915,"about_ca_topic_score_gemma":0.0009778403,"domain_scores_codex":[0.9888325,0.007509554,0.0004878044,0.001233658,0.001597598,0.0003389922],"domain_scores_gemma":[0.9541786,0.03492874,0.00342103,0.004937492,0.002262826,0.0002711951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001722065,0.0001466516,0.0114777,0.0004269396,0.0004798768,0.0003396355,0.000426102,0.1080125,0.001887187,0.645877,0.009351858,0.2214023],"study_design_scores_gemma":[0.0001601392,0.0001293842,0.002439546,0.0002443781,0.0001249228,0.0002203552,0.0001028604,0.4682934,0.001983986,0.5072228,0.01900046,0.00007772703],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003556371,0.0002200121,0.9942142,0.0004789893,0.00005178892,0.0000619043,0.0001544172,0.000171999,0.001090345],"genre_scores_gemma":[0.2208736,0.001125923,0.7690665,0.0009026456,0.0006176048,0.001107341,0.001256859,0.0002556579,0.004793835],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0199743,"threshold_uncertainty_score":0.1056355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0710083964430922,"score_gpt":0.2444613700085574,"score_spread":0.1734529735654652,"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."}}