{"text": "{\"genre\":\"methodological_econometric\",\"subfield\":\"financial_econometrics\",\"subfield_secondary\":\"asset_pricing\",\"primary_method\":\"machine_learning_textual\",\"identification\":{\"estimand\":\"SDF loading of a candidate factor, conditional on a selected subset of a high-dimensional set of existing control factors\",\"strategy\":\"Double-selection LASSO (two selection stages over control factors) followed by a two-pass cross-sectional regression and asymptotic inference from a derived central limit result\",\"key_assumptions\":[\"true asset pricing model is approximately low-dimensional (sparsity)\",\"linear SDF specification\",\"large cross-section and time-series asymptotics\"],\"threats_addressed\":[\"omitted variable bias from model selection mistakes\",\"regularization bias from LASSO\",\"tuning parameter sensitivity\",\"choice of test assets\"],\"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\":0},\"results\":{\"present\":true,\"sentence_index\":3},\"magnitude\":{\"present\":false,\"sentence_index\":-1},\"contribution\":{\"present\":true,\"sentence_index\":1}},\"introduction\":{\"moves\":[{\"paragraph_index\":0,\"move\":\"motivation\"},{\"paragraph_index\":0,\"move\":\"tension_or_puzzle\"},{\"paragraph_index\":1,\"move\":\"preview_of_approach\"},{\"paragraph_index\":2,\"move\":\"preview_of_results\"},{\"paragraph_index\":3,\"move\":\"preview_of_results\"},{\"paragraph_index\":4,\"move\":\"preview_of_approach\"},{\"paragraph_index\":5,\"move\":\"robustness_mention\"},{\"paragraph_index\":5,\"move\":\"preview_of_approach\"},{\"paragraph_index\":6,\"move\":\"contribution_statement\"},{\"paragraph_index\":6,\"move\":\"mechanism_or_theory_sketch\"},{\"paragraph_index\":7,\"move\":\"preview_of_approach\"},{\"paragraph_index\":8,\"move\":\"other\"},{\"paragraph_index\":8,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":9,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":10,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":10,\"move\":\"contribution_statement\"},{\"paragraph_index\":11,\"move\":\"roadmap\"}],\"contribution_paragraph_index\":6,\"contribution_types\":[\"new_method\",\"new_evidence\"],\"roadmap_present\":true,\"literature_positioning_location\":\"inside_introduction\"},\"section_roles\":[{\"idx\":0,\"role\":\"introduction\"},{\"idx\":1,\"role\":\"empirical_design\"},{\"idx\":2,\"role\":\"results\"},{\"idx\":3,\"role\":\"conclusion\"}],\"results_presentation\":{\"economic_significance_discussed\":false,\"statistical_significance_language\":\"stars_and_tstats\",\"magnitude_statements_in_intro\":false},\"robustness\":{\"location\":\"within_results\",\"types\":[\"sensitivity_to_parameters\",\"alternative_measure\",\"alternative_specification\",\"subsample\"]},\"tables\":{\"convention_notes\":\"Table notes spell out the control set (135 factors through 2011), the test assets (750 bivariate-sorted portfolios), and the sample period. 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It then introduces a double-selection LASSO two-pass regression method that targets SDF loadings. The introduction previews four empirical exercises and emphasizes robustness to tuning parameters. The method is positioned against alpha-based tests and the LASSO post-selection literature. A methodology section derives inference, and an empirical section applies it to recent and recursively tested factors, with robustness subsections. A short conclusion restates the contribution and findings.\",\"writing_profile_keywords\":[\"methodological\",\"high-dimensional\",\"model selection\",\"LASSO\",\"SDF loadings\",\"factor zoo\",\"conservative test\",\"robustness to tuning\"],\"confidence\":0.8,\"notes\":\"Primary method has no exact category; machine_learning_textual was chosen to reflect LASSO regularization, though the paper is econometric and involves no text analysis. Figures 2-3 report t-statistic heat maps, so uncertainty is shown only partially. Monte Carlo evidence and proofs are in the Internet Appendix.\"}", "json": {"genre": "methodological_econometric", "subfield": "financial_econometrics", "subfield_secondary": "asset_pricing", "primary_method": "machine_learning_textual", "identification": {"estimand": "SDF loading of a candidate factor, conditional on a selected subset of a high-dimensional set of existing control factors", "strategy": "Double-selection LASSO (two selection stages over control factors) followed by a two-pass cross-sectional regression and asymptotic inference from a derived central limit result", "key_assumptions": ["true asset pricing model is approximately low-dimensional (sparsity)", "linear SDF specification", "large cross-section and time-series asymptotics"], "threats_addressed": ["omitted variable bias from model selection mistakes", "regularization bias from LASSO", "tuning parameter sensitivity", "choice of test assets"], "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": 0}, "results": {"present": true, "sentence_index": 3}, "magnitude": {"present": false, "sentence_index": -1}, "contribution": {"present": true, "sentence_index": 1}}, "introduction": {"moves": [{"paragraph_index": 0, "move": "motivation"}, {"paragraph_index": 0, "move": "tension_or_puzzle"}, {"paragraph_index": 1, "move": "preview_of_approach"}, {"paragraph_index": 2, "move": "preview_of_results"}, {"paragraph_index": 3, "move": "preview_of_results"}, {"paragraph_index": 4, "move": "preview_of_approach"}, {"paragraph_index": 5, "move": "robustness_mention"}, {"paragraph_index": 5, "move": "preview_of_approach"}, {"paragraph_index": 6, "move": "contribution_statement"}, {"paragraph_index": 6, "move": "mechanism_or_theory_sketch"}, {"paragraph_index": 7, "move": "preview_of_approach"}, {"paragraph_index": 8, "move": "other"}, {"paragraph_index": 8, "move": "related_literature_positioning"}, {"paragraph_index": 9, "move": "related_literature_positioning"}, {"paragraph_index": 10, "move": "related_literature_positioning"}, {"paragraph_index": 10, "move": "contribution_statement"}, {"paragraph_index": 11, "move": "roadmap"}], "contribution_paragraph_index": 6, "contribution_types": ["new_method", "new_evidence"], "roadmap_present": true, "literature_positioning_location": "inside_introduction"}, "section_roles": [{"idx": 0, "role": "introduction"}, {"idx": 1, "role": "empirical_design"}, {"idx": 2, "role": "results"}, {"idx": 3, "role": "conclusion"}], "results_presentation": {"economic_significance_discussed": false, "statistical_significance_language": "stars_and_tstats", "magnitude_statements_in_intro": false}, "robustness": {"location": "within_results", "types": ["sensitivity_to_parameters", "alternative_measure", "alternative_specification", "subsample"]}, "tables": {"convention_notes": "Table notes spell out the control set (135 factors through 2011), the test assets (750 bivariate-sorted portfolios), and the sample period. 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It then introduces a double-selection LASSO two-pass regression method that targets SDF loadings. The introduction previews four empirical exercises and emphasizes robustness to tuning parameters. The method is positioned against alpha-based tests and the LASSO post-selection literature. A methodology section derives inference, and an empirical section applies it to recent and recursively tested factors, with robustness subsections. A short conclusion restates the contribution and findings.", "writing_profile_keywords": ["methodological", "high-dimensional", "model selection", "LASSO", "SDF loadings", "factor zoo", "conservative test", "robustness to tuning"], "confidence": 0.8, "notes": "Primary method has no exact category; machine_learning_textual was chosen to reflect LASSO regularization, though the paper is econometric and involves no text analysis. Figures 2-3 report t-statistic heat maps, so uncertainty is shown only partially. 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