{"text": "{\"genre\":\"methodological_econometric\",\"subfield\":\"asset_pricing\",\"subfield_secondary\":\"financial_econometrics\",\"primary_method\":\"other\",\"identification\":{\"estimand\":\"Identification of risk premia (rank of the beta matrix) and confidence sets for risk premia and SDF parameters such as relative risk aversion\",\"strategy\":\"Identification-robust tests: a rank test with F bounding distribution, and a GRS-FAR statistic (an extension of GRS/Anderson-Rubin) inverted to give confidence sets; GMM-AR for nonlinear SDFs; Monte Carlo simulations calibrated to the data\",\"key_assumptions\":[\"Exact F distribution of the GRS-type statistic under normality\",\"Model specification (correct versus misspecified) affects the shape of confidence sets\",\"GMM-AR requires T much larger than N\"],\"threats_addressed\":[\"Weak or no identification from small factor–return correlations\",\"Small T relative to N\",\"Spurious t-test rejections in Fama-MacBeth\",\"Model misspecification\"],\"causal_language_strength\":1,\"uses_causal_claim\":false},\"abstract_moves\":{\"question\":{\"present\":true,\"sentence_index\":0},\"gap\":{\"present\":true,\"sentence_index\":1},\"method\":{\"present\":true,\"sentence_index\":2},\"results\":{\"present\":true,\"sentence_index\":3},\"magnitude\":{\"present\":false,\"sentence_index\":-1},\"contribution\":{\"present\":true,\"sentence_index\":4}},\"introduction\":{\"moves\":[{\"paragraph_index\":0,\"move\":\"motivation\"},{\"paragraph_index\":0,\"move\":\"tension_or_puzzle\"},{\"paragraph_index\":1,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":1,\"move\":\"literature_gap\"},{\"paragraph_index\":2,\"move\":\"preview_of_results\"},{\"paragraph_index\":2,\"move\":\"mechanism_or_theory_sketch\"},{\"paragraph_index\":3,\"move\":\"preview_of_approach\"},{\"paragraph_index\":4,\"move\":\"preview_of_results\"},{\"paragraph_index\":5,\"move\":\"preview_of_approach\"},{\"paragraph_index\":5,\"move\":\"preview_of_results\"},{\"paragraph_index\":6,\"move\":\"contribution_statement\"},{\"paragraph_index\":6,\"move\":\"resolution_of_puzzle\" ,\"move\":\"other\"},{\"paragraph_index\":7,\"move\":\"roadmap\"}],\"contribution_paragraph_index\":6,\"contribution_types\":[\"new_method\",\"new_evidence\",\"resolution_of_puzzle\"],\"roadmap_present\":true,\"literature_positioning_location\":\"inside_introduction\"},\"section_roles\":[{\"idx\":0,\"role\":\"introduction\"},{\"idx\":1,\"role\":\"theory\"},{\"idx\":2,\"role\":\"results\"},{\"idx\":3,\"role\":\"results\"},{\"idx\":4,\"role\":\"results\"},{\"idx\":5,\"role\":\"conclusion\"}],\"results_presentation\":{\"economic_significance_discussed\":false,\"statistical_significance_language\":\"mixed\",\"magnitude_statements_in_intro\":false},\"robustness\":{\"location\":\"within_results\",\"types\":[\"alternative_measure\",\"alternative_sample_period\",\"alternative_specification\"]},\"tables\":{\"convention_notes\":\"Table notes identify the test assets, the sample period and frequency, and the data source (Kroencke 2017; Lettau-Ludvigson 2001). They explain how estimates are produced (FM two-pass, GMM) and define fit measures such as the pseudo-R2 and cross-sectional R2. 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Proposes a rank test and the GRS-FAR test, then applies them to the Kroencke data, previewing unbounded confidence sets and their reconciling role. Section I develops identification conditions and the tests for a single factor, alternating simulations with applications. Section II covers multifactor models, larger T and misspecification. Section III extends the analysis to nonlinear SDFs via GMM-AR. The conclusion restates the findings, gives practitioner guidance and points to extensions.\",\"writing_profile_keywords\":[\"weak identification\",\"robust inference\",\"Monte Carlo calibration\",\"confidence set inversion\",\"econometric method applied to asset pricing\",\"GRS test\"],\"confidence\":0.8,\"notes\":\"The method is an econometric testing procedure, so primary_method is coded as other. A placeholder move was inadvertently entered for paragraph 6; the intended move there is contribution_statement only. Section idx 2 is a parsing artifact: its heading is an equation and it continues Section I.\"}", "json": {"genre": "methodological_econometric", "subfield": "asset_pricing", "subfield_secondary": "financial_econometrics", "primary_method": "other", "identification": {"estimand": "Identification of risk premia (rank of the beta matrix) and confidence sets for risk premia and SDF parameters such as relative risk aversion", "strategy": "Identification-robust tests: a rank test with F bounding distribution, and a GRS-FAR statistic (an extension of GRS/Anderson-Rubin) inverted to give confidence sets; GMM-AR for nonlinear SDFs; Monte Carlo simulations calibrated to the data", "key_assumptions": ["Exact F distribution of the GRS-type statistic under normality", "Model specification (correct versus misspecified) affects the shape of confidence sets", "GMM-AR requires T much larger than N"], "threats_addressed": ["Weak or no identification from small factor–return correlations", "Small T relative to N", "Spurious t-test rejections in Fama-MacBeth", "Model misspecification"], "causal_language_strength": 1, "uses_causal_claim": false}, "abstract_moves": {"question": {"present": true, "sentence_index": 0}, "gap": {"present": true, "sentence_index": 1}, "method": {"present": true, "sentence_index": 2}, "results": {"present": true, "sentence_index": 3}, "magnitude": {"present": false, "sentence_index": -1}, "contribution": {"present": true, "sentence_index": 4}}, "introduction": {"moves": [{"paragraph_index": 0, "move": "motivation"}, {"paragraph_index": 0, "move": "tension_or_puzzle"}, {"paragraph_index": 1, "move": "related_literature_positioning"}, {"paragraph_index": 1, "move": "literature_gap"}, {"paragraph_index": 2, "move": "preview_of_results"}, {"paragraph_index": 2, "move": "mechanism_or_theory_sketch"}, {"paragraph_index": 3, "move": "preview_of_approach"}, {"paragraph_index": 4, "move": "preview_of_results"}, {"paragraph_index": 5, "move": "preview_of_approach"}, {"paragraph_index": 5, "move": "preview_of_results"}, {"paragraph_index": 6, "move": "contribution_statement"}, {"paragraph_index": 6, "move": "other"}, {"paragraph_index": 7, "move": "roadmap"}], "contribution_paragraph_index": 6, "contribution_types": ["new_method", "new_evidence", "resolution_of_puzzle"], "roadmap_present": true, "literature_positioning_location": "inside_introduction"}, "section_roles": [{"idx": 0, "role": "introduction"}, {"idx": 1, "role": "theory"}, {"idx": 2, "role": "results"}, {"idx": 3, "role": "results"}, {"idx": 4, "role": "results"}, {"idx": 5, "role": "conclusion"}], "results_presentation": {"economic_significance_discussed": false, "statistical_significance_language": "mixed", "magnitude_statements_in_intro": false}, "robustness": {"location": "within_results", "types": ["alternative_measure", "alternative_sample_period", "alternative_specification"]}, "tables": {"convention_notes": "Table notes identify the test assets, the sample period and frequency, and the data source (Kroencke 2017; Lettau-Ludvigson 2001). 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Proposes a rank test and the GRS-FAR test, then applies them to the Kroencke data, previewing unbounded confidence sets and their reconciling role. Section I develops identification conditions and the tests for a single factor, alternating simulations with applications. Section II covers multifactor models, larger T and misspecification. Section III extends the analysis to nonlinear SDFs via GMM-AR. The conclusion restates the findings, gives practitioner guidance and points to extensions.", "writing_profile_keywords": ["weak identification", "robust inference", "Monte Carlo calibration", "confidence set inversion", "econometric method applied to asset pricing", "GRS test"], "confidence": 0.8, "notes": "The method is an econometric testing procedure, so primary_method is coded as other. A placeholder move was inadvertently entered for paragraph 6; the intended move there is contribution_statement only. 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