1{2 "validation_errors": [],3 "doc_id": "jof_12854",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:11:41+00:00",9 "source_sha256": "1946517567efa438940674ef310d5fb59b27ddbfd02bcdfe6b9654b4f1b2841e",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 18279,12 "usage": {13 "input": 7108,14 "output": 1959,15 "cache_read": 3968,16 "cache_write": 017 },18 "cost_usd": 0.068406,19 "request_id": "req_011CggqgTbaG6ecUo7n2ka3q",20 "annotation": {21 "genre": "replication_or_correction",22 "subfield": "financial_econometrics",23 "subfield_secondary": "asset_pricing",24 "primary_method": "calibration_simulation",25 "identification": {26 "estimand": "not applicable (Bayesian model comparison: posterior model probabilities / marginal likelihoods for factor models)",27 "strategy": "Analytical derivation that priors on nuisance parameters must satisfy an across-model change-of-variable property, followed by Monte Carlo simulations measuring how often each method selects the true DGP",28 "key_assumptions": [29 "Nuisance parameters across models are connected by invertible mappings",30 "Simulated DGPs calibrated to real factor data are representative"31 ],32 "threats_addressed": [33 "Results being specific to a single DGP (33 DGPs used)",34 "Dependence on sample size (multiple T values)"35 ],36 "causal_language_strength": 1,37 "uses_causal_claim": false38 },39 "abstract_moves": {40 "question": {41 "present": true,42 "sentence_index": 043 },44 "gap": {45 "present": true,46 "sentence_index": 147 },48 "method": {49 "present": true,50 "sentence_index": 351 },52 "results": {53 "present": true,54 "sentence_index": 255 },56 "magnitude": {57 "present": false,58 "sentence_index": -159 },60 "contribution": {61 "present": true,62 "sentence_index": 463 }64 },65 "introduction": {66 "moves": [67 {68 "paragraph_index": 0,69 "move": "contribution_statement"70 },71 {72 "paragraph_index": 0,73 "move": "tension_or_puzzle"74 },75 {76 "paragraph_index": 0,77 "move": "preview_of_approach"78 }79 ],80 "contribution_paragraph_index": 0,81 "contribution_types": [82 "replication_or_correction",83 "new_method"84 ],85 "roadmap_present": false,86 "literature_positioning_location": "inside_introduction"87 },88 "section_roles": [89 {90 "idx": 0,91 "role": "introduction"92 },93 {94 "idx": 1,95 "role": "theory"96 },97 {98 "idx": 2,99 "role": "theory"100 },101 {102 "idx": 3,103 "role": "theory"104 },105 {106 "idx": 4,107 "role": "theory"108 },109 {110 "idx": 5,111 "role": "theory"112 },113 {114 "idx": 6,115 "role": "theory"116 },117 {118 "idx": 7,119 "role": "theory"120 },121 {122 "idx": 8,123 "role": "theory"124 },125 {126 "idx": 9,127 "role": "theory"128 },129 {130 "idx": 10,131 "role": "theory"132 },133 {134 "idx": 11,135 "role": "results"136 },137 {138 "idx": 12,139 "role": "results"140 },141 {142 "idx": 13,143 "role": "conclusion"144 },145 {146 "idx": 14,147 "role": "appendix"148 },149 {150 "idx": 15,151 "role": "appendix"152 },153 {154 "idx": 16,155 "role": "appendix"156 },157 {158 "idx": 17,159 "role": "appendix"160 },161 {162 "idx": 18,163 "role": "appendix"164 },165 {166 "idx": 19,167 "role": "appendix"168 },169 {170 "idx": 20,171 "role": "appendix"172 },173 {174 "idx": 21,175 "role": "appendix"176 },177 {178 "idx": 22,179 "role": "appendix"180 }181 ],182 "results_presentation": {183 "economic_significance_discussed": false,184 "statistical_significance_language": "unclear",185 "magnitude_statements_in_intro": false186 },187 "robustness": {188 "location": "within_results",189 "types": [190 "sensitivity_to_parameters",191 "other"192 ]193 },194 "tables": {195 "convention_notes": "Simulation tables, each row a true DGP labelled by its risk factors, columns are sample sizes T, and entries are the percentage of replications in which the true model is selected. BS and CZZ results are reported in parallel tables for the 8- and 12-factor experiments.",196 "units_stated_in_notes": true,197 "standard_errors_description": "none"198 },199 "figures": {200 "uncertainty_shown": "no_figures"201 },202 "conclusion": {203 "bounded_claims": false,204 "policy_implications": false,205 "open_questions": false,206 "restates_contribution": true207 },208 "hedging": {209 "level": "low",210 "typical_devices": [211 "categorical verdicts such as 'unsound' and 'invalidates'",212 "self-description of the method as a notable advance"213 ]214 },215 "claim_evidence": [216 {217 "claim": "The Jeffreys priors used in the BS method violate a required across-model change-of-variable property, so the resulting marginal likelihoods depend on arbitrary constants",218 "evidence_type": "equation_or_proof",219 "evidence_ref": "Propositions 2-3, Sections II-IV, Appendix B"220 },221 {222 "claim": "The BS permutation method from their online appendix is also unsound",223 "evidence_type": "equation_or_proof",224 "evidence_ref": "Section IV.B"225 },226 {227 "claim": "In simulations, the BS method rarely selects the true DGP, even with very large samples",228 "evidence_type": "table",229 "evidence_ref": "Tables I-II"230 },231 {232 "claim": "The CZZ priors yield valid marginal likelihoods that select the true model far more often, even at small T",233 "evidence_type": "table",234 "evidence_ref": "Tables III-IV"235 },236 {237 "claim": "A model-specific prior on alpha gives performance gains of up to 20% in small samples (results not reported)",238 "evidence_type": "none_stated",239 "evidence_ref": "Section III.C"240 }241 ],242 "structural_summary": "A critique of a prior JF paper. 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