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Small-sample inference uses IVX and bootstrap. Results are compared with calibrated Campbell–Cochrane habit model simulations.",28 "key_assumptions": [29 "Cyclical consumption measured via Hamilton (2018) projection captures deviations from trend",30 "Inference robust to persistent predictor (IVX/bootstrap validity)",31 "External habit model as interpretive framework"32 ],33 "threats_addressed": [34 "Persistent regressor bias (Stambaugh)",35 "Look-ahead bias via recursive estimation out-of-sample",36 "Detrending method choice",37 "Temporal instability/subsample periods",38 "Subsumption by alternative predictors"39 ],40 "causal_language_strength": 2,41 "uses_causal_claim": false42 },43 "abstract_moves": {44 "question": {45 "present": true,46 "sentence_index": 047 },48 "gap": {49 "present": false,50 "sentence_index": -151 },52 "method": {53 "present": true,54 "sentence_index": 055 },56 "results": {57 "present": true,58 "sentence_index": 159 },60 "magnitude": {61 "present": false,62 "sentence_index": -163 },64 "contribution": {65 "present": true,66 "sentence_index": 367 }68 },69 "introduction": {70 "moves": [71 {72 "paragraph_index": 0,73 "move": "mechanism_or_theory_sketch"74 },75 {76 "paragraph_index": 0,77 "move": "preview_of_results"78 },79 {80 "paragraph_index": 1,81 "move": "preview_of_approach"82 },83 {84 "paragraph_index": 1,85 "move": "preview_of_results"86 },87 {88 "paragraph_index": 2,89 "move": "robustness_mention"90 },91 {92 "paragraph_index": 2,93 "move": "identification_defense"94 },95 {96 "paragraph_index": 3,97 "move": "preview_of_results"98 },99 {100 "paragraph_index": 4,101 "move": "literature_gap"102 },103 {104 "paragraph_index": 4,105 "move": "related_literature_positioning"106 },107 {108 "paragraph_index": 4,109 "move": "contribution_statement"110 }111 ],112 "contribution_paragraph_index": 4,113 "contribution_types": [114 "new_data_or_measure",115 "new_evidence"116 ],117 "roadmap_present": false,118 "literature_positioning_location": "inside_introduction"119 },120 "section_roles": [121 {122 "idx": 0,123 "role": "introduction"124 },125 {126 "idx": 1,127 "role": "data"128 },129 {130 "idx": 2,131 "role": "results"132 },133 {134 "idx": 3,135 "role": "robustness"136 },137 {138 "idx": 4,139 "role": "results"140 },141 {142 "idx": 5,143 "role": "theory"144 },145 {146 "idx": 6,147 "role": "conclusion"148 }149 ],150 "results_presentation": {151 "economic_significance_discussed": true,152 "statistical_significance_language": "stars_and_tstats",153 "magnitude_statements_in_intro": false154 },155 "robustness": {156 "location": "mixed",157 "types": [158 "alternative_specification",159 "alternative_measure",160 "subsample",161 "alternative_sample_period",162 "other"163 ]164 },165 "tables": {166 "convention_notes": "Table notes open by writing out the regression equation and defining the horizon, the return measure, and the predictor. Significance is marked with stars, and test statistics such as IVX-Wald and out-of-sample R2 are reported. Simulated-data tables state that values are in annualized percentages.",167 "units_stated_in_notes": true,168 "standard_errors_description": "unclear"169 },170 "figures": {171 "uncertainty_shown": "no"172 },173 "conclusion": {174 "bounded_claims": true,175 "policy_implications": false,176 "open_questions": false,177 "restates_contribution": true178 },179 "hedging": {180 "level": "moderate",181 "typical_devices": [182 "'consistent with' framing",183 "'we conjecture'",184 "'suggest' for model-data link",185 "acknowledging fit with alternative model classes"186 ]187 },188 "claim_evidence": [189 {190 "claim": "Low cyclical consumption predicts high future excess market returns across horizons",191 "evidence_type": "table",192 "evidence_ref": "Table I"193 },194 {195 "claim": "Predictability survives persistence-robust IVX inference",196 "evidence_type": "table",197 "evidence_ref": "Table II"198 },199 {200 "claim": "Predictive power is present in both good and bad times",201 "evidence_type": "table",202 "evidence_ref": "Table III"203 },204 {205 "claim": "Out-of-sample forecasts beat constant expected return benchmark",206 "evidence_type": "table",207 "evidence_ref": "Table VI"208 },209 {210 "claim": "Cyclical consumption outperforms 19 alternative predictors out of sample",211 "evidence_type": "table",212 "evidence_ref": "Table X"213 },214 {215 "claim": "Predictability extends to industry portfolios and international markets",216 "evidence_type": "table",217 "evidence_ref": "Tables VII, IX"218 },219 {220 "claim": "Simulated habit model generates an inverse cc-return relation similar to the data",221 "evidence_type": "table",222 "evidence_ref": "Table XII"223 }224 ],225 "structural_summary": "The introduction opens with a habit-formation mechanism and conjectures an inverse relation between detrended consumption and future returns. It then previews the empirical findings, the model simulation, the robustness and international extensions, and the horse race against existing predictors. The body proceeds in order: construction of the measure, in-sample and out-of-sample predictive regressions with state, detrending, stability and robustness splits, comparison with alternative predictors, and calibration of the external habit model. The conclusion restates the measure and its interpretation.",226 "writing_profile_keywords": [227 "return predictability",228 "consumption-based asset pricing",229 "habit formation",230 "out-of-sample tests",231 "horse race against predictors",232 "model calibration check"233 ],234 "confidence": 0.85,235 "notes": "No roadmap paragraph appears in the visible introduction. Section IV was auto-labeled data but functions as results comparing alternative predictors. Section I constructs the measure and is mapped to the data role. No specific magnitudes are visible in the intro excerpts."236 },237 "deterministic_checks": {238 "roadmap_regex": false,239 "contribution_regex_in_intro": false,240 "magnitude_in_abstract": false,241 "abstract_n_sentences": 4,242 "intro_n_paragraphs": 5,243 "intro_n_words": 779,244 "causal_words_in_abstract": 0,245 "hedge_words_in_abstract": 1,246 "stars_mentioned": true,247 "clustered_se_mentioned": false,248 "internet_appendix_mentioned": false,249 "section_roles_rule": {250 "0": "introduction",251 "1": "other",252 "2": "other",253 "3": "robustness",254 "4": "data",255 "5": "theory",256 "6": "conclusion"257 }258 },259 "schema_valid": true,260 "code_version": "87c73cd-dirty"261}