1{"text": "{\"genre\":\"mixed_theory_empirical\",\"subfield\":\"asset_pricing\",\"subfield_secondary\":\"macro_finance\",\"primary_method\":\"structural_estimation\",\"identification\":{\"estimand\":\"Coefficient of relative risk aversion implied by consumption covariances with stock returns (GMM Euler equations), and cross-sectional consumption risk premia\",\"strategy\":\"A Kalman-filter model of NIPA consumption estimation is used to reverse (unfilter) reported NIPA consumption. Stock returns are time-aggregated to address time-aggregation bias. The resulting series is used in GMM Euler-equation estimation and Fama-MacBeth cross-sectional tests. A simulation experiment validates the procedure.\",\"key_assumptions\":[\"BEA estimation process can be approximated by an optimal (Kalman) filter\",\"Measurement error in observable consumption is uncorrelated with stock returns\",\"Calibrated filter parameters (signal-to-noise, consumption volatility) are appropriate\"],\"threats_addressed\":[\"time-aggregation bias\",\"heterogeneity between nondurables and services\",\"choice of consumption volatility measure in the unfilter\",\"conditioning information and long-horizon returns\"],\"causal_language_strength\":2,\"uses_causal_claim\":false},\"abstract_moves\":{\"question\":{\"present\":true,\"sentence_index\":0},\"gap\":{\"present\":false,\"sentence_index\":-1},\"method\":{\"present\":true,\"sentence_index\":1},\"results\":{\"present\":true,\"sentence_index\":2},\"magnitude\":{\"present\":false,\"sentence_index\":-1},\"contribution\":{\"present\":true,\"sentence_index\":0}},\"introduction\":{\"moves\":[{\"paragraph_index\":0,\"move\":\"motivation\"},{\"paragraph_index\":0,\"move\":\"tension_or_puzzle\"},{\"paragraph_index\":0,\"move\":\"mechanism_or_theory_sketch\"},{\"paragraph_index\":1,\"move\":\"preview_of_results\"},{\"paragraph_index\":2,\"move\":\"preview_of_results\"},{\"paragraph_index\":3,\"move\":\"preview_of_results\"},{\"paragraph_index\":3,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":4,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":5,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":6,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":7,\"move\":\"literature_gap\"},{\"paragraph_index\":7,\"move\":\"contribution_statement\"},{\"paragraph_index\":7,\"move\":\"policy_or_broader_implication\"},{\"paragraph_index\":7,\"move\":\"roadmap\"}],\"contribution_paragraph_index\":0,\"contribution_types\":[\"new_data_or_measure\",\"resolution_of_puzzle\",\"new_evidence\",\"new_method\"],\"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\":\"theory\"},{\"idx\":5,\"role\":\"conclusion\"},{\"idx\":6,\"role\":\"appendix\"},{\"idx\":7,\"role\":\"appendix\"},{\"idx\":8,\"role\":\"appendix\"},{\"idx\":9,\"role\":\"appendix\"},{\"idx\":10,\"role\":\"appendix\"},{\"idx\":11,\"role\":\"appendix\"}],\"results_presentation\":{\"economic_significance_discussed\":true,\"statistical_significance_language\":\"unclear\",\"magnitude_statements_in_intro\":true},\"robustness\":{\"location\":\"within_results\",\"types\":[\"alternative_measure\",\"alternative_specification\",\"subsample\",\"alternative_sample_period\"]},\"tables\":{\"convention_notes\":\"Notes define moment restrictions and estimators and fix the discount factor. They state sample periods (full sample and postwar). Some tables scale moments (×100). Simulation tables report medians of 10,000 simulations.\",\"units_stated_in_notes\":true,\"standard_errors_description\":\"unclear\"},\"figures\":{\"uncertainty_shown\":\"mixed\"},\"conclusion\":{\"bounded_claims\":true,\"policy_implications\":false,\"open_questions\":false,\"restates_contribution\":true},\"hedging\":{\"level\":\"moderate\",\"typical_devices\":[\"'might be attributed, at least in part'\",\"'may underestimate/overstate'\",\"'provides a first attempt'\",\"explicit acknowledgment of failures (profitability premium, return predictability)\"]},\"claim_evidence\":[{\"claim\":\"Unfiltered NIPA consumption explains the equity premium with a reasonable relative risk aversion\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table III\"},{\"claim\":\"Unfiltering recovers properties of observable consumption in a simulated economy\",\"evidence_type\":\"figure\",\"evidence_ref\":\"Table I; Figure 1\"},{\"claim\":\"Unfiltered consumption prices size, value, and investment portfolios with low pricing errors but fails for profitability\",\"evidence_type\":\"table\",\"evidence_ref\":\"Tables VIII, IX\"},{\"claim\":\"The filter model explains differences between NIPA nondurables and services\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table IV\"},{\"claim\":\"Calibrating to unfiltered consumption changes the implications of habit and long-run risk models\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table X; Figure 5\"}],\"structural_summary\":\"The introduction frames the paper around the puzzle of why garbage beats NIPA consumption in the consumption CAPM. It proposes statistical filtering by NIPA as the explanation and enumerates findings, including failures. Literature positioning (real estate unsmoothing, time aggregation) closes the introduction with a roadmap. Section I builds a Kalman-filter model, inverts it, and validates it by simulation. Section II compares consumption measures and estimates risk aversion by GMM, with several robustness variants. Section III runs cross-sectional tests. Section IV recalibrates habit and long-run risk models. A short conclusion restates the contribution and acknowledges the unexplained return predictability.\",\"writing_profile_keywords\":[\"measurement-error explanation\",\"puzzle resolution\",\"Kalman unfiltering\",\"GMM Euler equations\",\"simulation validation\",\"model calibration implications\",\"enumerated findings\"],\"confidence\":0.8,\"notes\":\"Primary method is a hybrid of filter modeling, calibration/simulation, and GMM estimation. Structural_estimation was chosen as the closest label. The excerpts do not show how statistical significance is reported.\"}", "json": {"genre": "mixed_theory_empirical", "subfield": "asset_pricing", "subfield_secondary": "macro_finance", "primary_method": "structural_estimation", "identification": {"estimand": "Coefficient of relative risk aversion implied by consumption covariances with stock returns (GMM Euler equations), and cross-sectional consumption risk premia", "strategy": "A Kalman-filter model of NIPA consumption estimation is used to reverse (unfilter) reported NIPA consumption. Stock returns are time-aggregated to address time-aggregation bias. The resulting series is used in GMM Euler-equation estimation and Fama-MacBeth cross-sectional tests. 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