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Search, Evaluate, and Read Evidence","\u002Fen\u002Facademic-writing\u002F02-reading-and-literature-matrix","en\u002Facademic-writing\u002F02-reading-and-literature-matrix",{"title":27,"path":28,"stem":29},"3. From Sources to Synthesis and Argument","\u002Fen\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs","en\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs",{"title":31,"path":32,"stem":33},"4. Literature Review and a Defensible Gap","\u002Fen\u002Facademic-writing\u002F04-writing-the-literature-review","en\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":35,"path":36,"stem":37},"5. Research Design, Evidence, and Core Sections","\u002Fen\u002Facademic-writing\u002F05-core-sections-and-evidence","en\u002Facademic-writing\u002F05-core-sections-and-evidence",{"title":39,"path":40,"stem":41},"6. Citation, Paraphrasing, Integrity, and AI","\u002Fen\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity","en\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity",{"title":43,"path":44,"stem":45},"7. Revision, Review, and Submission","\u002Fen\u002Facademic-writing\u002F07-revision-style-and-submission","en\u002Facademic-writing\u002F07-revision-style-and-submission",{"title":47,"path":48,"stem":49},"8. Research Workbook","\u002Fen\u002Facademic-writing\u002F08-literature-review-checklist-and-template","en\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":51,"path":52,"stem":53},"9. Research Workflow and Tools","\u002Fen\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management","en\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management",{"title":55,"path":56,"stem":57},"10. Worked Project — From Question to Defensible Conclusion","\u002Fen\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain","en\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain",{"title":59,"path":60,"stem":61,"children":62,"page":249},"Accounting","\u002Fen\u002Faccounting","en\u002Faccounting",[63,69,87,93,193],{"title":64,"path":65,"stem":66,"children":67},"Accounting — From Evidence to Decisions","\u002Fen\u002Faccounting\u002F00-index","en\u002Faccounting\u002F00-index",[68],{"title":64,"path":65,"stem":66},{"title":70,"path":71,"stem":72,"children":73},"Accounting Appendix","\u002Fen\u002Faccounting\u002Fappendix","en\u002Faccounting\u002Fappendix\u002Findex",[74,75,79,83],{"title":70,"path":71,"stem":72},{"title":76,"path":77,"stem":78},"Worked Examples and Error Diagnosis","\u002Fen\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls","en\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls",{"title":80,"path":81,"stem":82},"Accounting Glossary","\u002Fen\u002Faccounting\u002Fappendix\u002F26-glossary","en\u002Faccounting\u002Fappendix\u002F26-glossary",{"title":84,"path":85,"stem":86},"Reading and Evidence Map","\u002Fen\u002Faccounting\u002Fappendix\u002F27-reading-map","en\u002Faccounting\u002Fappendix\u002F27-reading-map",{"title":88,"path":89,"stem":90,"children":91},"Northstar Record-to-Decision Capstone","\u002Fen\u002Faccounting\u002Fcapstone","en\u002Faccounting\u002Fcapstone\u002Findex",[92],{"title":88,"path":89,"stem":90},{"title":94,"path":95,"stem":96,"children":97},"Financial Accounting","\u002Fen\u002Faccounting\u002Ffinancial-accounting","en\u002Faccounting\u002Ffinancial-accounting\u002Findex",[98,99,117,131,165,179],{"title":94,"path":95,"stem":96},{"title":100,"path":101,"stem":102,"children":103},"1. Foundations","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002Findex",[104,105,109,113],{"title":100,"path":101,"stem":102},{"title":106,"path":107,"stem":108},"Objectives and Qualitative Characteristics","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics",{"title":110,"path":111,"stem":112},"Equation and Elements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements",{"title":114,"path":115,"stem":116},"Accrual Basis, Estimates and Periods","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles",{"title":118,"path":119,"stem":120,"children":121},"2. Recording Transactions","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002Findex",[122,123,127],{"title":118,"path":119,"stem":120},{"title":124,"path":125,"stem":126},"Double-Entry and Debit\u002FCredit","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr",{"title":128,"path":129,"stem":130},"Accounting Cycle","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle",{"title":132,"path":133,"stem":134,"children":135},"3. Measurement and Adjustments","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002Findex",[136,137,141,145,149,153,157,161],{"title":132,"path":133,"stem":134},{"title":138,"path":139,"stem":140},"Revenue Recognition","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition",{"title":142,"path":143,"stem":144},"Inventory","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory",{"title":146,"path":147,"stem":148},"Receivables and Expected Credit Losses","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables",{"title":150,"path":151,"stem":152},"Property, Plant and Equipment","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe",{"title":154,"path":155,"stem":156},"Intangible Assets","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles",{"title":158,"path":159,"stem":160},"Leases","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases",{"title":162,"path":163,"stem":164},"Current and Deferred Income Tax","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax",{"title":166,"path":167,"stem":168,"children":169},"4. Reporting and Cash","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002Findex",[170,171,175],{"title":166,"path":167,"stem":168},{"title":172,"path":173,"stem":174},"Linked Financial Statements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements",{"title":176,"path":177,"stem":178},"Cash, Reconciliation and Internal Control","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control",{"title":180,"path":181,"stem":182,"children":183},"5. Analysis and Comparison","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002Findex",[184,185,189],{"title":180,"path":181,"stem":182},{"title":186,"path":187,"stem":188},"Ratio Analysis","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis",{"title":190,"path":191,"stem":192},"IFRS versus US GAAP","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap",{"title":194,"path":195,"stem":196,"children":197},"Management Accounting","\u002Fen\u002Faccounting\u002Fmanagement-accounting","en\u002Faccounting\u002Fmanagement-accounting\u002Findex",[198,199,217,235],{"title":194,"path":195,"stem":196},{"title":200,"path":201,"stem":202,"children":203},"1. Cost Foundations","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002Findex",[204,205,209,213],{"title":200,"path":201,"stem":202},{"title":206,"path":207,"stem":208},"Cost Concepts and Behaviour","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts",{"title":210,"path":211,"stem":212},"Costing Systems","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems",{"title":214,"path":215,"stem":216},"Variable and Absorption Costing","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption",{"title":218,"path":219,"stem":220,"children":221},"2. Planning and Control","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002Findex",[222,223,227,231],{"title":218,"path":219,"stem":220},{"title":224,"path":225,"stem":226},"Cost–Volume–Profit Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis",{"title":228,"path":229,"stem":230},"Budgeting and Variance Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances",{"title":232,"path":233,"stem":234},"Performance Measurement","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement",{"title":236,"path":237,"stem":238,"children":239},"3. Decisions and Investment","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002Findex",[240,241,245],{"title":236,"path":237,"stem":238},{"title":242,"path":243,"stem":244},"Short-Term Decisions","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions",{"title":246,"path":247,"stem":248},"Capital Budgeting","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting",false,{"title":251,"path":252,"stem":253,"children":254,"page":249},"Business Analytics","\u002Fen\u002Fbusiness-analytics","en\u002Fbusiness-analytics",[255,261,279,305,335,365,383,400],{"title":256,"path":257,"stem":258,"children":259},"Business Analytics — From Data to Defensible Action","\u002Fen\u002Fbusiness-analytics\u002F00-index","en\u002Fbusiness-analytics\u002F00-index",[260],{"title":256,"path":257,"stem":258},{"title":262,"path":263,"stem":264,"children":265},"0. Decision and Data Foundations","\u002Fen\u002Fbusiness-analytics\u002F00-intro","en\u002Fbusiness-analytics\u002F00-intro\u002Findex",[266,267,271,275],{"title":262,"path":263,"stem":264},{"title":268,"path":269,"stem":270},"Decision Framing","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing","en\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing",{"title":272,"path":273,"stem":274},"Data Contracts","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts","en\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts",{"title":276,"path":277,"stem":278},"Reproducible Analytics Workflow","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow","en\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow",{"title":280,"path":281,"stem":282,"children":283},"1. Descriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive","en\u002Fbusiness-analytics\u002F01-descriptive\u002Findex",[284,285,289,293,297,301],{"title":280,"path":281,"stem":282},{"title":286,"path":287,"stem":288},"Distributions and Exploratory Data Analysis","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda","en\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda",{"title":290,"path":291,"stem":292},"KPIs and Denominators","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators","en\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators",{"title":294,"path":295,"stem":296},"Segments, Cohorts and Funnels","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels","en\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels",{"title":298,"path":299,"stem":300},"Visual Evidence and Data Stories","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story","en\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story",{"title":302,"path":303,"stem":304},"Experiments and Causal Boundaries","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary","en\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary",{"title":306,"path":307,"stem":308,"children":309},"2. Predictive Analytics","\u002Fen\u002Fbusiness-analytics\u002F02-predictive","en\u002Fbusiness-analytics\u002F02-predictive\u002Findex",[310,311,315,319,323,327,331],{"title":306,"path":307,"stem":308},{"title":312,"path":313,"stem":314},"Validation, Baselines and Leakage","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage","en\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage",{"title":316,"path":317,"stem":318},"Regression and Forecasting","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting","en\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting",{"title":320,"path":321,"stem":322},"Classification and Calibration","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration","en\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration",{"title":324,"path":325,"stem":326},"Trees and Ensembles","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles","en\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles",{"title":328,"path":329,"stem":330},"Decision Metrics and Thresholds","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds","en\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds",{"title":332,"path":333,"stem":334},"Explainability, Monitoring and Drift","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift","en\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift",{"title":336,"path":337,"stem":338,"children":339},"3. Prescriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive","en\u002Fbusiness-analytics\u002F03-prescriptive\u002Findex",[340,341,345,349,353,357,361],{"title":336,"path":337,"stem":338},{"title":342,"path":343,"stem":344},"Decision-Making under Uncertainty","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty",{"title":346,"path":347,"stem":348},"Linear Optimisation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization",{"title":350,"path":351,"stem":352},"Inventory and Allocation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation",{"title":354,"path":355,"stem":356},"Queueing and Simulation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation",{"title":358,"path":359,"stem":360},"Pricing and Revenue Management","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation",{"title":362,"path":363,"stem":364},"Causal Targeting and Policy Learning","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting",{"title":366,"path":367,"stem":368,"children":369},"4. Deployment and Governance","\u002Fen\u002Fbusiness-analytics\u002F04-deployment","en\u002Fbusiness-analytics\u002F04-deployment\u002Findex",[370,371,375,379],{"title":366,"path":367,"stem":368},{"title":372,"path":373,"stem":374},"Data Products and Monitoring","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring","en\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring",{"title":376,"path":377,"stem":378},"Governance, Fairness and Privacy","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy","en\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy",{"title":380,"path":381,"stem":382},"Adoption and Business Value","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value","en\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value",{"title":384,"path":385,"stem":386,"children":387},"Appendix and Revision Tools","\u002Fen\u002Fbusiness-analytics\u002Fappendix","en\u002Fbusiness-analytics\u002Fappendix\u002Findex",[388,389,393,397],{"title":384,"path":385,"stem":386},{"title":390,"path":391,"stem":392},"Formula and Decision Map","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map","en\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map",{"title":394,"path":395,"stem":396},"Worked Examples and Pitfalls","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls","en\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls",{"title":84,"path":398,"stem":399},"\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map","en\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map",{"title":401,"path":402,"stem":403,"children":404},"Capstone — The Evening Delivery Promise","\u002Fen\u002Fbusiness-analytics\u002Fcapstone","en\u002Fbusiness-analytics\u002Fcapstone\u002Findex",[405],{"title":401,"path":402,"stem":403},{"title":407,"path":408,"stem":409,"children":410,"page":249},"Financial Economic Time Series","\u002Fen\u002Ffinancial-economic-time-series","en\u002Ffinancial-economic-time-series",[411,417,423,429,435,441,447,453,459,465,471],{"title":412,"path":413,"stem":414,"children":415},"Financial and Economic Time Series — Course Guide","\u002Fen\u002Ffinancial-economic-time-series\u002F00-intro","en\u002Ffinancial-economic-time-series\u002F00-intro\u002Findex",[416],{"title":412,"path":413,"stem":414},{"title":418,"path":419,"stem":420,"children":421},"Three Versions of Time Series","\u002Fen\u002Ffinancial-economic-time-series\u002F01-bridge","en\u002Ffinancial-economic-time-series\u002F01-bridge\u002Findex",[422],{"title":418,"path":419,"stem":420},{"title":424,"path":425,"stem":426,"children":427},"Data, Clocks, and Transformations","\u002Fen\u002Ffinancial-economic-time-series\u002F02-data-transformations","en\u002Ffinancial-economic-time-series\u002F02-data-transformations\u002Findex",[428],{"title":424,"path":425,"stem":426},{"title":430,"path":431,"stem":432,"children":433},"Predictive Regressions and Persistent Predictors","\u002Fen\u002Ffinancial-economic-time-series\u002F03-predictive-regressions","en\u002Ffinancial-economic-time-series\u002F03-predictive-regressions\u002Findex",[434],{"title":430,"path":431,"stem":432},{"title":436,"path":437,"stem":438,"children":439},"Volatility, Tails, and Financial Risk","\u002Fen\u002Ffinancial-economic-time-series\u002F04-volatility-risk","en\u002Ffinancial-economic-time-series\u002F04-volatility-risk\u002Findex",[440],{"title":436,"path":437,"stem":438},{"title":442,"path":443,"stem":444,"children":445},"Unit Roots, Cointegration, and Error Correction","\u002Fen\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration","en\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration\u002Findex",[446],{"title":442,"path":443,"stem":444},{"title":448,"path":449,"stem":450,"children":451},"VAR, Structural Identification, and Local Projections","\u002Fen\u002Ffinancial-economic-time-series\u002F06-var-identification","en\u002Ffinancial-economic-time-series\u002F06-var-identification\u002Findex",[452],{"title":448,"path":449,"stem":450},{"title":454,"path":455,"stem":456,"children":457},"State Space, Mixed Frequency, and Nowcasting","\u002Fen\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting","en\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting\u002Findex",[458],{"title":454,"path":455,"stem":456},{"title":460,"path":461,"stem":462,"children":463},"Forecast Evaluation for Decisions","\u002Fen\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation","en\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation\u002Findex",[464],{"title":460,"path":461,"stem":462},{"title":466,"path":467,"stem":468,"children":469},"Integrated R Laboratory","\u002Fen\u002Ffinancial-economic-time-series\u002F09-r-laboratory","en\u002Ffinancial-economic-time-series\u002F09-r-laboratory\u002Findex",[470],{"title":466,"path":467,"stem":468},{"title":472,"path":473,"stem":474,"children":475},"Capstones, Data, and Reading Ladder","\u002Fen\u002Ffinancial-economic-time-series\u002F10-capstone-readings","en\u002Ffinancial-economic-time-series\u002F10-capstone-readings\u002Findex",[476],{"title":472,"path":473,"stem":474},{"title":478,"path":479,"stem":480,"children":481,"page":249},"Intro To Economics","\u002Fen\u002Fintro-to-economics","en\u002Fintro-to-economics",[482,486,490,494,498,502,506,510,514,518,522,526,530],{"title":483,"path":484,"stem":485},"Introduction to Economics — Decisions, Markets, and the Macroeconomy","\u002Fen\u002Fintro-to-economics\u002F00-intro","en\u002Fintro-to-economics\u002F00-intro",{"title":487,"path":488,"stem":489},"Chapter 1 — Choice, Opportunity Cost, and Trade","\u002Fen\u002Fintro-to-economics\u002F01-foundations","en\u002Fintro-to-economics\u002F01-foundations",{"title":491,"path":492,"stem":493},"Chapter 2 — Demand, Supply, Equilibrium, and Welfare","\u002Fen\u002Fintro-to-economics\u002F02-demand-and-supply","en\u002Fintro-to-economics\u002F02-demand-and-supply",{"title":495,"path":496,"stem":497},"Chapter 3 — Elasticity, Revenue, and Tax Incidence","\u002Fen\u002Fintro-to-economics\u002F03-elasticity","en\u002Fintro-to-economics\u002F03-elasticity",{"title":499,"path":500,"stem":501},"Chapter 4 — Firms, Market Power, and Market Failure","\u002Fen\u002Fintro-to-economics\u002F04-market-structures","en\u002Fintro-to-economics\u002F04-market-structures",{"title":503,"path":504,"stem":505},"Chapter 5 — GDP, Income, Wealth, and Welfare","\u002Fen\u002Fintro-to-economics\u002F05-gdp-and-wealth","en\u002Fintro-to-economics\u002F05-gdp-and-wealth",{"title":507,"path":508,"stem":509},"Chapter 6 — Inflation, Purchasing Power, and Labour Markets","\u002Fen\u002Fintro-to-economics\u002F06-inflation-and-unemployment","en\u002Fintro-to-economics\u002F06-inflation-and-unemployment",{"title":511,"path":512,"stem":513},"Chapter 7 — Productivity, Technology, and Economic Growth","\u002Fen\u002Fintro-to-economics\u002F07-economic-growth","en\u002Fintro-to-economics\u002F07-economic-growth",{"title":515,"path":516,"stem":517},"Chapter 8 — Money, Credit, and Banking","\u002Fen\u002Fintro-to-economics\u002F08-money-and-banking","en\u002Fintro-to-economics\u002F08-money-and-banking",{"title":519,"path":520,"stem":521},"Chapter 9 — Business Cycles, AD–AS, and Monetary Policy","\u002Fen\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as","en\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as",{"title":523,"path":524,"stem":525},"Chapter 10 — Fiscal Policy, Distribution, and Public Debt","\u002Fen\u002Fintro-to-economics\u002F10-fiscal-policy","en\u002Fintro-to-economics\u002F10-fiscal-policy",{"title":527,"path":528,"stem":529},"Chapter 11 — Trade, Capital Flows, and Exchange Rates","\u002Fen\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates","en\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates",{"title":531,"path":532,"stem":533},"Chapter 12 — Integrated Economic Analysis Studio","\u002Fen\u002Fintro-to-economics\u002F12-review-and-case-studies","en\u002Fintro-to-economics\u002F12-review-and-case-studies",{"title":535,"path":536,"stem":537,"children":538,"page":249},"Microeconometrics","\u002Fen\u002Fmicroeconometrics","en\u002Fmicroeconometrics",[539,557,563,581,603,629,651,673,691,709],{"title":540,"path":541,"stem":542,"children":543},"0. Causal Questions and Designs","\u002Fen\u002Fmicroeconometrics\u002F00-foundations","en\u002Fmicroeconometrics\u002F00-foundations\u002Findex",[544,545,549,553],{"title":540,"path":541,"stem":542},{"title":546,"path":547,"stem":548},"Causal Questions and Estimands","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands","en\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands",{"title":550,"path":551,"stem":552},"Potential Outcomes and Experiments","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments","en\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments",{"title":554,"path":555,"stem":556},"Causal Diagrams and Controls","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls","en\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls",{"title":558,"path":559,"stem":560,"children":561},"Microeconometrics — Designing Credible Counterfactuals","\u002Fen\u002Fmicroeconometrics\u002F00-index","en\u002Fmicroeconometrics\u002F00-index",[562],{"title":558,"path":559,"stem":560},{"title":564,"path":565,"stem":566,"children":567},"1. Regression and Inference","\u002Fen\u002Fmicroeconometrics\u002F01-regression","en\u002Fmicroeconometrics\u002F01-regression\u002Findex",[568,569,573,577],{"title":564,"path":565,"stem":566},{"title":570,"path":571,"stem":572},"OLS as a Projection","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection","en\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection",{"title":574,"path":575,"stem":576},"FWL, Selection and Controls","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls","en\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls",{"title":578,"path":579,"stem":580},"Inference and Clustering","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering","en\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering",{"title":582,"path":583,"stem":584,"children":585},"2. Instruments and Panel Data","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002Findex",[586,587,591,595,599],{"title":582,"path":583,"stem":584},{"title":588,"path":589,"stem":590},"Instrumental Variables","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification",{"title":592,"path":593,"stem":594},"Weak Instruments and LATE","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late",{"title":596,"path":597,"stem":598},"Panel Fixed Effects","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects",{"title":600,"path":601,"stem":602},"Dynamic Panels and Limits","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel",{"title":604,"path":605,"stem":606,"children":607},"3. Policy Evaluation Designs","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs","en\u002Fmicroeconometrics\u002F03-policy-designs\u002Findex",[608,609,613,617,621,625],{"title":604,"path":605,"stem":606},{"title":610,"path":611,"stem":612},"Difference-in-Differences","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core",{"title":614,"path":615,"stem":616},"Staggered DID and Event Studies","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies",{"title":618,"path":619,"stem":620},"Regression Discontinuity","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd",{"title":622,"path":623,"stem":624},"Matching, Weighting and Overlap","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting",{"title":626,"path":627,"stem":628},"Synthetic Control","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control",{"title":630,"path":631,"stem":632,"children":633},"Module 4 — Choice and Limited Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002Findex",[634,635,639,643,647],{"title":630,"path":631,"stem":632},{"title":636,"path":637,"stem":638},"Binary Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice",{"title":640,"path":641,"stem":642},"Multinomial and Ordered Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice",{"title":644,"path":645,"stem":646},"Count Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes",{"title":648,"path":649,"stem":650},"Censoring, Truncation and Selection","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection",{"title":652,"path":653,"stem":654,"children":655},"Module 5 — Modern Causal Analysis","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal","en\u002Fmicroeconometrics\u002F05-modern-causal\u002Findex",[656,657,661,665,669],{"title":652,"path":653,"stem":654},{"title":658,"path":659,"stem":660},"Double Machine Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml",{"title":662,"path":663,"stem":664},"Heterogeneity and Policy Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy",{"title":666,"path":667,"stem":668},"Sensitivity and Partial Identification","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id",{"title":670,"path":671,"stem":672},"External Validity and Transport","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity",{"title":674,"path":675,"stem":676,"children":677},"Module 6 — From Data to Auditable Evidence","\u002Fen\u002Fmicroeconometrics\u002F06-workflow","en\u002Fmicroeconometrics\u002F06-workflow\u002Findex",[678,679,683,687],{"title":674,"path":675,"stem":676},{"title":680,"path":681,"stem":682},"Data Provenance","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance","en\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance",{"title":684,"path":685,"stem":686},"Reproducibility and Reporting","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting","en\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting",{"title":688,"path":689,"stem":690},"Paper and Evidence Audit","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit","en\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit",{"title":692,"path":693,"stem":694,"children":695},"Appendix — Revision and Evidence Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix","en\u002Fmicroeconometrics\u002Fappendix\u002Findex",[696,697,701,705],{"title":692,"path":693,"stem":694},{"title":698,"path":699,"stem":700},"Formula and Design Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map","en\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map",{"title":702,"path":703,"stem":704},"Ten Worked Microeconometric Pitfalls","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls","en\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls",{"title":706,"path":707,"stem":708},"Reading and Software Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map","en\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map",{"title":710,"path":711,"stem":712,"children":713},"Capstone — Should Northbridge Expand Pathways?","\u002Fen\u002Fmicroeconometrics\u002Fcapstone","en\u002Fmicroeconometrics\u002Fcapstone\u002Findex",[714],{"title":710,"path":711,"stem":712},{"title":716,"path":717,"stem":718,"children":719,"page":249},"Microeconomics","\u002Fen\u002Fmicroeconomics","en\u002Fmicroeconomics",[720,726,732,738,744,750,756,762,768,774,780,786,792],{"title":721,"path":722,"stem":723,"children":724},"Advanced Microeconomics — Course Guide","\u002Fen\u002Fmicroeconomics\u002F00-intro","en\u002Fmicroeconomics\u002F00-intro\u002Findex",[725],{"title":721,"path":722,"stem":723},{"title":727,"path":728,"stem":729,"children":730},"Module 1 — Choice, Duality, and Revealed Preference","\u002Fen\u002Fmicroeconomics\u002F01-fundations","en\u002Fmicroeconomics\u002F01-fundations\u002Findex",[731],{"title":727,"path":728,"stem":729},{"title":733,"path":734,"stem":735,"children":736},"Module 2 — Comparative Statics and Welfare Measurement","\u002Fen\u002Fmicroeconomics\u002F02-comparative-statics","en\u002Fmicroeconomics\u002F02-comparative-statics\u002Findex",[737],{"title":733,"path":734,"stem":735},{"title":739,"path":740,"stem":741,"children":742},"Module 3 — Choice under Risk and Insurance","\u002Fen\u002Fmicroeconomics\u002F03-uncertainty","en\u002Fmicroeconomics\u002F03-uncertainty\u002Findex",[743],{"title":739,"path":740,"stem":741},{"title":745,"path":746,"stem":747,"children":748},"Module 4 — General Equilibrium and Welfare","\u002Fen\u002Fmicroeconomics\u002F04-general-equilibrium","en\u002Fmicroeconomics\u002F04-general-equilibrium\u002Findex",[749],{"title":745,"path":746,"stem":747},{"title":751,"path":752,"stem":753,"children":754},"Module 5 — Static, Dynamic, and Repeated Games","\u002Fen\u002Fmicroeconomics\u002F05-game-theory","en\u002Fmicroeconomics\u002F05-game-theory\u002Findex",[755],{"title":751,"path":752,"stem":753},{"title":757,"path":758,"stem":759,"children":760},"Module 6 — Oligopoly, Entry, and Algorithmic Pricing","\u002Fen\u002Fmicroeconomics\u002F06-oligopoly","en\u002Fmicroeconomics\u002F06-oligopoly\u002Findex",[761],{"title":757,"path":758,"stem":759},{"title":763,"path":764,"stem":765,"children":766},"Module 7 — Information Economics and Contracts","\u002Fen\u002Fmicroeconomics\u002F07-information-economics","en\u002Fmicroeconomics\u002F07-information-economics\u002Findex",[767],{"title":763,"path":764,"stem":765},{"title":769,"path":770,"stem":771,"children":772},"Module 8 — Mechanism Design and Auctions","\u002Fen\u002Fmicroeconomics\u002F08-mechanism-design","en\u002Fmicroeconomics\u002F08-mechanism-design\u002Findex",[773],{"title":769,"path":770,"stem":771},{"title":775,"path":776,"stem":777,"children":778},"Module 9 — Behavioural and Experimental Microeconomics","\u002Fen\u002Fmicroeconomics\u002F09-behavioural-economics","en\u002Fmicroeconomics\u002F09-behavioural-economics\u002Findex",[779],{"title":775,"path":776,"stem":777},{"title":781,"path":782,"stem":783,"children":784},"Module 10 — Externalities, Public Goods, and Collective Action","\u002Fen\u002Fmicroeconomics\u002F10-externalities-public-goods","en\u002Fmicroeconomics\u002F10-externalities-public-goods\u002Findex",[785],{"title":781,"path":782,"stem":783},{"title":787,"path":788,"stem":789,"children":790},"Module 11 — Matching and Market Design","\u002Fen\u002Fmicroeconomics\u002F11-market-design","en\u002Fmicroeconomics\u002F11-market-design\u002Findex",[791],{"title":787,"path":788,"stem":789},{"title":793,"path":794,"stem":795,"children":796},"Module 12 — Integrated Microeconomic Design Studio","\u002Fen\u002Fmicroeconomics\u002F12-review","en\u002Fmicroeconomics\u002F12-review\u002Findex",[797],{"title":793,"path":794,"stem":795},{"title":799,"path":800,"stem":801,"children":802},"Playground","\u002Fen\u002Fplayground","en\u002Fplayground\u002Findex",[803,804,808,812,816,820],{"title":799,"path":800,"stem":801},{"title":805,"path":806,"stem":807},"Typst Plugin Playground","\u002Fen\u002Fplayground\u002F01-typstex","en\u002Fplayground\u002F01-typstex",{"title":809,"path":810,"stem":811},"Citation Plugin Test","\u002Fen\u002Fplayground\u002F02-citation","en\u002Fplayground\u002F02-citation",{"title":813,"path":814,"stem":815},"Pyodide Playground","\u002Fen\u002Fplayground\u002F03-pyodide","en\u002Fplayground\u002F03-pyodide",{"title":817,"path":818,"stem":819},"WebR Playground","\u002Fen\u002Fplayground\u002F04-webr","en\u002Fplayground\u002F04-webr",{"title":821,"path":822,"stem":823},"Chart.js 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文献综述的构建与写作","\u002Fzh\u002Facademic-writing\u002F04-writing-the-literature-review","zh\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":1056,"path":1057,"stem":1058},"8. 文献综述清单与模板","\u002Fzh\u002Facademic-writing\u002F08-literature-review-checklist-and-template","zh\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":1060,"path":1061,"stem":1062},"10. 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用于评估不确定性，模型检验和 Python 案例则帮助你把论文中的方法搬到自己的数据上。",[1800,1801,1802],"h2",{"id":1802},"本章路线",[1804,1805,1806,1819],"table",{},[1807,1808,1809],"thead",{},[1810,1811,1812,1816],"tr",{},[1813,1814,1815],"th",{},"课次",[1813,1817,1818],{},"核心问题",[1820,1821,1822,1831,1839,1847,1855,1863],"tbody",{},[1810,1823,1824,1828],{},[1825,1826,1827],"td",{},"10.1 Monte Carlo",[1825,1829,1830],{},"模拟误差怎样随路径数下降？",[1810,1832,1833,1836],{},[1825,1834,1835],{},"10.2 优化",[1825,1837,1838],{},"权重约束、目标函数与数值误差如何互动？",[1810,1840,1841,1844],{},[1825,1842,1843],{},"10.3 估计与校准",[1825,1845,1846],{},"估计对象、损失函数和识别条件是什么？",[1810,1848,1849,1852],{},[1825,1850,1851],{},"10.4 模型检验",[1825,1853,1854],{},"定价误差、预测误差和经济价值怎样区分？",[1810,1856,1857,1860],{},[1825,1858,1859],{},"10.5 Bootstrap",[1825,1861,1862],{},"时间依赖下应如何重抽样？",[1810,1864,1865,1868],{},[1825,1866,1867],{},"10.6—10.7 ML 与实现",[1825,1869,1870],{},"信息时点、样本外评价和交易成本如何进入代码？",[1800,1872,1873],{"id":1873},"学习目标",[1793,1875,1876],{},"通过本章学习，你将掌握：",[1878,1879,1880,1884,1887,1890,1893],"ol",{},[1881,1882,1883],"li",{},"蒙特卡洛模拟的原理和应用",[1881,1885,1886],{},"常用的数值优化算法",[1881,1888,1889],{},"参数估计的方法（MLE、GMM等）",[1881,1891,1892],{},"模型检验和比较技术",[1881,1894,1895],{},"使用Python实现资产定价模型",[1793,1897,1898],{},"更具体地说，读完本章后你应能：",[1900,1901,1902,1905,1908,1911,1914],"ul",{},[1881,1903,1904],{},"为一个资产定价问题选择合适的模拟、优化或估计方法。",[1881,1906,1907],{},"说明 Monte Carlo、Bootstrap、GMM、MLE 和回归检验分别解决什么问题。",[1881,1909,1910],{},"识别实证研究中的前视偏差、样本选择、幸存者偏差和多重检验问题。",[1881,1912,1913],{},"用清晰的工作流组织数据清洗、模型估计、稳健性分析和结果解释。",[1881,1915,1916],{},"判断一个模型“统计显著”是否同时具有经济意义和可交易性。",[1800,1918,1919],{"id":1919},"金融与经济动机",[1793,1921,1922],{},"资产定价研究最后都要回到数据。一个模型可能推导漂亮，但如果参数无法稳定估计、检验结果对样本期高度敏感、或者交易成本吃掉全部收益，它就很难指导真实投资。反过来，一个实证异象即使在数据中显著，也需要通过经济机制、稳健性和可实施性检验，才可能成为可信结论。",[1804,1924,1925,1938],{},[1807,1926,1927],{},[1810,1928,1929,1932,1935],{},[1813,1930,1931],{},"研究目标",[1813,1933,1934],{},"常用方法",[1813,1936,1937],{},"主要风险",[1820,1939,1940,1951,1962,1973,1984],{},[1810,1941,1942,1945,1948],{},[1825,1943,1944],{},"估计风险溢价",[1825,1946,1947],{},"回归、GMM、MLE",[1825,1949,1950],{},"标准误错误、弱识别",[1810,1952,1953,1956,1959],{},[1825,1954,1955],{},"评估策略表现",[1825,1957,1958],{},"回测、Bootstrap",[1825,1960,1961],{},"前视偏差、交易成本遗漏",[1810,1963,1964,1967,1970],{},[1825,1965,1966],{},"定价复杂衍生品",[1825,1968,1969],{},"Monte Carlo、树、有限差分",[1825,1971,1972],{},"离散误差、随机误差",[1810,1974,1975,1978,1981],{},[1825,1976,1977],{},"校准模型参数",[1825,1979,1980],{},"数值优化",[1825,1982,1983],{},"局部最优、过拟合",[1810,1985,1986,1989,1992],{},[1825,1987,1988],{},"比较多个模型",[1825,1990,1991],{},"统计检验、样本外验证",[1825,1993,1994],{},"数据挖掘、多重检验",[1800,1996,1997],{"id":1997},"模型设置与直觉",[1793,1999,2000],{},"实证资产定价通常从一个矩条件、回归方程或定价误差开始。例如因子模型可以写成：",[2002,2003,2006],"span",{"className":2004},[2005],"katex-display",[2002,2007,2010,2101],{"className":2008},[2009],"katex",[2002,2011,2014],{"className":2012},[2013],"katex-mathml",[2015,2016,2019],"math",{"xmlns":2017,"display":2018},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML","block",[2020,2021,2022,2096],"semantics",{},[2023,2024,2025,2048,2051,2059,2062,2074,2081,2083],"mrow",{},[2026,2027,2028,2032,2045],"msubsup",{},[2029,2030,2031],"mi",{},"R",[2023,2033,2034,2037,2042],{},[2029,2035,2036],{},"i",[2038,2039,2041],"mo",{"separator":2040},"true",",",[2029,2043,2044],{},"t",[2029,2046,2047],{},"e",[2038,2049,2050],{},"=",[2052,2053,2054,2057],"msub",{},[2029,2055,2056],{},"α",[2029,2058,2036],{},[2038,2060,2061],{},"+",[2026,2063,2064,2067,2069],{},[2029,2065,2066],{},"β",[2029,2068,2036],{},[2038,2070,2073],{"mathvariant":2071,"lspace":2072,"rspace":2072},"normal","0em","′",[2052,2075,2076,2079],{},[2029,2077,2078],{},"F",[2029,2080,2044],{},[2038,2082,2061],{},[2052,2084,2085,2088],{},[2029,2086,2087],{},"ε",[2023,2089,2090,2092,2094],{},[2029,2091,2036],{},[2038,2093,2041],{"separator":2040},[2029,2095,2044],{},[2097,2098,2100],"annotation",{"encoding":2099},"application\u002Fx-tex","R_{i,t}^e=\\alpha_i+\\beta_i'F_t+\\varepsilon_{i,t}",[2002,2102,2105,2207,2269,2386],{"className":2103,"ariaHidden":2040},[2104],"katex-html",[2002,2106,2109,2114,2195,2200,2204],{"className":2107},[2108],"base",[2002,2110],{"className":2111,"style":2113},[2112],"strut","height:1.0975em;vertical-align:-0.3831em;",[2002,2115,2118,2123],{"className":2116},[2117],"mord",[2002,2119,2031],{"className":2120,"style":2122},[2117,2121],"mathnormal","margin-right:0.0077em;",[2002,2124,2127],{"className":2125},[2126],"msupsub",[2002,2128,2132,2186],{"className":2129},[2130,2131],"vlist-t","vlist-t2",[2002,2133,2136,2181],{"className":2134},[2135],"vlist-r",[2002,2137,2141,2169],{"className":2138,"style":2140},[2139],"vlist","height:0.7144em;",[2002,2142,2144,2149],{"style":2143},"top:-2.453em;margin-left:-0.0077em;margin-right:0.05em;",[2002,2145],{"className":2146,"style":2148},[2147],"pstrut","height:2.7em;",[2002,2150,2156],{"className":2151},[2152,2153,2154,2155],"sizing","reset-size6","size3","mtight",[2002,2157,2159,2162,2166],{"className":2158},[2117,2155],[2002,2160,2036],{"className":2161},[2117,2121,2155],[2002,2163,2041],{"className":2164},[2165,2155],"mpunct",[2002,2167,2044],{"className":2168},[2117,2121,2155],[2002,2170,2172,2175],{"style":2171},"top:-3.113em;margin-right:0.05em;",[2002,2173],{"className":2174,"style":2148},[2147],[2002,2176,2178],{"className":2177},[2152,2153,2154,2155],[2002,2179,2047],{"className":2180},[2117,2121,2155],[2002,2182,2185],{"className":2183},[2184],"vlist-s","​",[2002,2187,2189],{"className":2188},[2135],[2002,2190,2193],{"className":2191,"style":2192},[2139],"height:0.3831em;",[2002,2194],{},[2002,2196],{"className":2197,"style":2199},[2198],"mspace","margin-right:0.2778em;",[2002,2201,2050],{"className":2202},[2203],"mrel",[2002,2205],{"className":2206,"style":2199},[2198],[2002,2208,2210,2214,2258,2262,2266],{"className":2209},[2108],[2002,2211],{"className":2212,"style":2213},[2112],"height:0.7333em;vertical-align:-0.15em;",[2002,2215,2217,2221],{"className":2216},[2117],[2002,2218,2056],{"className":2219,"style":2220},[2117,2121],"margin-right:0.0037em;",[2002,2222,2224],{"className":2223},[2126],[2002,2225,2227,2249],{"className":2226},[2130,2131],[2002,2228,2230,2246],{"className":2229},[2135],[2002,2231,2234],{"className":2232,"style":2233},[2139],"height:0.3117em;",[2002,2235,2237,2240],{"style":2236},"top:-2.55em;margin-left:-0.0037em;margin-right:0.05em;",[2002,2238],{"className":2239,"style":2148},[2147],[2002,2241,2243],{"className":2242},[2152,2153,2154,2155],[2002,2244,2036],{"className":2245},[2117,2121,2155],[2002,2247,2185],{"className":2248},[2184],[2002,2250,2252],{"className":2251},[2135],[2002,2253,2256],{"className":2254,"style":2255},[2139],"height:0.15em;",[2002,2257],{},[2002,2259],{"className":2260,"style":2261},[2198],"margin-right:0.2222em;",[2002,2263,2061],{"className":2264},[2265],"mbin",[2002,2267],{"className":2268,"style":2261},[2198],[2002,2270,2272,2276,2334,2377,2380,2383],{"className":2271},[2108],[2002,2273],{"className":2274,"style":2275},[2112],"height:1.0489em;vertical-align:-0.247em;",[2002,2277,2279,2283],{"className":2278},[2117],[2002,2280,2066],{"className":2281,"style":2282},[2117,2121],"margin-right:0.0528em;",[2002,2284,2286],{"className":2285},[2126],[2002,2287,2289,2325],{"className":2288},[2130,2131],[2002,2290,2292,2322],{"className":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",[2002,2448,2450,2468],{"className":2449},[2009],[2002,2451,2453],{"className":2452},[2013],[2015,2454,2455],{"xmlns":2017},[2020,2456,2457,2465],{},[2023,2458,2459],{},[2052,2460,2461,2463],{},[2029,2462,2056],{},[2029,2464,2036],{},[2097,2466,2467],{"encoding":2099},"\\alpha_i",[2002,2469,2471],{"className":2470,"ariaHidden":2040},[2104],[2002,2472,2474,2478],{"className":2473},[2108],[2002,2475],{"className":2476,"style":2477},[2112],"height:0.5806em;vertical-align:-0.15em;",[2002,2479,2481,2484],{"className":2480},[2117],[2002,2482,2056],{"className":2483,"style":2220},[2117,2121],[2002,2485,2487],{"className":2486},[2126],[2002,2488,2490,2510],{"className":2489},[2130,2131],[2002,2491,2493,2507],{"className":2492},[2135],[2002,2494,2496],{"className":2495,"style":2233},[2139],[2002,2497,2498,2501],{"style":2236},[2002,2499],{"className":2500,"style":2148},[2147],[2002,2502,2504],{"className":2503},[2152,2153,2154,2155],[2002,2505,2036],{"className":2506},[2117,2121,2155],[2002,2508,2185],{"className":2509},[2184],[2002,2511,2513],{"className":2512},[2135],[2002,2514,2516],{"className":2515,"style":2255},[2139],[2002,2517],{}," 是否系统性偏离 0。SDF\u002FGMM 框架则常写成：",[2002,2520,2522],{"className":2521},[2005],[2002,2523,2525,2577],{"className":2524},[2009],[2002,2526,2528],{"className":2527},[2013],[2015,2529,2530],{"xmlns":2017,"display":2018},[2020,2531,2532,2574],{},[2023,2533,2534,2537,2541,2548,2551,2554,2557,2565,2568,2570],{},[2029,2535,2536],{},"E",[2038,2538,2540],{"stretchy":2539},"false","[",[2052,2542,2543,2546],{},[2029,2544,2545],{},"m",[2029,2547,2044],{},[2038,2549,2550],{"stretchy":2539},"(",[2029,2552,2553],{},"θ",[2038,2555,2556],{"stretchy":2539},")",[2026,2558,2559,2561,2563],{},[2029,2560,2031],{},[2029,2562,2044],{},[2029,2564,2047],{},[2038,2566,2567],{"stretchy":2539},"]",[2038,2569,2050],{},[2571,2572,2573],"mn",{},"0",[2097,2575,2576],{"encoding":2099},"E[m_t(\\theta)R_t^e]=0",[2002,2578,2580,2709],{"className":2579,"ariaHidden":2040},[2104],[2002,2581,2583,2587,2591,2595,2635,2638,2642,2646,2697,2700,2703,2706],{"className":2582},[2108],[2002,2584],{"className":2585,"style":2586},[2112],"height:1em;vertical-align:-0.25em;",[2002,2588,2536],{"className":2589,"style":2590},[2117,2121],"margin-right:0.0576em;",[2002,2592,2540],{"className":2593},[2594],"mopen",[2002,2596,2598,2601],{"className":2597},[2117],[2002,2599,2545],{"className":2600},[2117,2121],[2002,2602,2604],{"className":2603},[2126],[2002,2605,2607,2627],{"className":2606},[2130,2131],[2002,2608,2610,2624],{"className":2609},[2135],[2002,2611,2613],{"className":2612,"style":2353},[2139],[2002,2614,2615,2618],{"style":2413},[2002,2616],{"className":2617,"style":2148},[2147],[2002,2619,2621],{"className":2620},[2152,2153,2154,2155],[2002,2622,2044],{"className":2623},[2117,2121,2155],[2002,2625,2185],{"className":2626},[2184],[2002,2628,2630],{"className":2629},[2135],[2002,2631,2633],{"className":2632,"style":2255},[2139],[2002,2634],{},[2002,2636,2550],{"className":2637},[2594],[2002,2639,2553],{"className":2640,"style":2641},[2117,2121],"margin-right:0.0278em;",[2002,2643,2556],{"className":2644},[2645],"mclose",[2002,2647,2649,2652],{"className":2648},[2117],[2002,2650,2031],{"className":2651,"style":2122},[2117,2121],[2002,2653,2655],{"className":2654},[2126],[2002,2656,2658,2689],{"className":2657},[2130,2131],[2002,2659,2661,2686],{"className":2660},[2135],[2002,2662,2664,2675],{"className":2663,"style":2140},[2139],[2002,2665,2666,2669],{"style":2143},[2002,2667],{"className":2668,"style":2148},[2147],[2002,2670,2672],{"className":2671},[2152,2153,2154,2155],[2002,2673,2044],{"className":2674},[2117,2121,2155],[2002,2676,2677,2680],{"style":2171},[2002,2678],{"className":2679,"style":2148},[2147],[2002,2681,2683],{"className":2682},[2152,2153,2154,2155],[2002,2684,2047],{"className":2685},[2117,2121,2155],[2002,2687,2185],{"className":2688},[2184],[2002,2690,2692],{"className":2691},[2135],[2002,2693,2695],{"className":2694,"style":2331},[2139],[2002,2696],{},[2002,2698,2567],{"className":2699},[2645],[2002,2701],{"className":2702,"style":2199},[2198],[2002,2704,2050],{"className":2705},[2203],[2002,2707],{"className":2708,"style":2199},[2198],[2002,2710,2712,2716],{"className":2711},[2108],[2002,2713],{"className":2714,"style":2715},[2112],"height:0.6444em;",[2002,2717,2573],{"className":2718},[2117],[1793,2720,2721],{},"估计的目标不是让样本内误差消失，而是在统计噪声、经济约束和可复现性之间取得可靠结论。好的实证工作流会把“提出假设”和“检查假设”分开，避免研究者不断试错后只汇报最好看的结果。",[1800,2723,2724],{"id":2724},"实证工作流",[2726,2727],"mermaid-diagram",{"code64":2728,"locale":7},"Zmxvd2NoYXJ0IFRECiAgQVsi5o+Q5Ye65a6a5Lu35YGH6K6+Il0gLS0+IEJbIuaVtOeQhuaUtuebiueOh+OAgeWboOWtkOWSjOaOp+WItuWPmOmHjyJdCiAgQiAtLT4gQ1si5Lyw6K6h5Y+C5pWw5oiW5p6E6YCg57uE5ZCIIl0KICBDIC0tPiBEWyLov5vooYzmqKHlnovmo4DpqozlkoznqLPlgaXmgKfliIbmnpAiXQogIEQgLS0+IEVbIuino+mHiue7j+a1juWQq+S5iSJdCiAgRSAtLT4gRlsi5aSN546w44CB6K6w5b2V5ZKM5omp5bGVIl0=",[1804,2730,2731,2744],{},[1807,2732,2733],{},[1810,2734,2735,2738,2741],{},[1813,2736,2737],{},"环节",[1813,2739,2740],{},"常见问题",[1813,2742,2743],{},"本章对应内容",[1820,2745,2746,2757,2768,2779,2790],{},[1810,2747,2748,2751,2754],{},[1825,2749,2750],{},"模拟",[1825,2752,2753],{},"模型在不同市场状态下会怎样表现",[1825,2755,2756],{},"蒙特卡洛、方差缩减",[1810,2758,2759,2762,2765],{},[1825,2760,2761],{},"优化",[1825,2763,2764],{},"如何在约束下求组合权重或校准参数",[1825,2766,2767],{},"数值优化、组合约束",[1810,2769,2770,2773,2776],{},[1825,2771,2772],{},"估计",[1825,2774,2775],{},"参数是否稳定，标准误是否可信",[1825,2777,2778],{},"MLE、GMM、Bootstrap",[1810,2780,2781,2784,2787],{},[1825,2782,2783],{},"检验",[1825,2785,2786],{},"模型能否解释收益差异",[1825,2788,2789],{},"模型比较、横截面检验",[1810,2791,2792,2795,2798],{},[1825,2793,2794],{},"实现",[1825,2796,2797],{},"代码结果是否可复现",[1825,2799,2800],{},"Python 案例、数据清洗",[2802,2803,2805],"warning",{"title":2804},"常见误区","实证资产定价最容易出问题的地方不是公式，而是样本选择、变量口径、缺失值处理和重复试验。看到显著结果时，先检查数据生成过程和检验设计，再讨论经济解释。",[1800,2807,2808],{"id":2808},"关键方法速查",[2810,2811,2813],"h3",{"id":2812},"monte-carlo","Monte Carlo",[1793,2815,2816],{},"通过大量随机路径近似期望、分布或价格。",[1793,2818,2819,2820,2927],{},"中文解释：当解析解困难时，用模拟把“积分问题”变成“平均问题”。误差随路径数增加按 ",[2002,2821,2823,2847],{"className":2822},[2009],[2002,2824,2826],{"className":2825},[2013],[2015,2827,2828],{"xmlns":2017},[2020,2829,2830,2844],{},[2023,2831,2832,2835,2838],{},[2571,2833,2834],{},"1",[2029,2836,2837],{"mathvariant":2071},"\u002F",[2839,2840,2841],"msqrt",{},[2029,2842,2843],{},"N",[2097,2845,2846],{"encoding":2099},"1\u002F\\sqrt{N}",[2002,2848,2850],{"className":2849,"ariaHidden":2040},[2104],[2002,2851,2853,2857,2861],{"className":2852},[2108],[2002,2854],{"className":2855,"style":2856},[2112],"height:1.1767em;vertical-align:-0.25em;",[2002,2858,2860],{"className":2859},[2117],"1\u002F",[2002,2862,2865],{"className":2863},[2117,2864],"sqrt",[2002,2866,2868,2918],{"className":2867},[2130,2131],[2002,2869,2871,2915],{"className":2870},[2135],[2002,2872,2875,2892],{"className":2873,"style":2874},[2139],"height:0.9267em;",[2002,2876,2880,2884],{"className":2877,"style":2879},[2878],"svg-align","top:-3em;",[2002,2881],{"className":2882,"style":2883},[2147],"height:3em;",[2002,2885,2888],{"className":2886,"style":2887},[2117],"padding-left:0.833em;",[2002,2889,2843],{"className":2890,"style":2891},[2117,2121],"margin-right:0.109em;",[2002,2893,2895,2898],{"style":2894},"top:-2.8867em;",[2002,2896],{"className":2897,"style":2883},[2147],[2002,2899,2903],{"className":2900,"style":2902},[2901],"hide-tail","min-width:0.853em;height:1.08em;",[2904,2905,2911],"svg",{"xmlns":2906,"width":2907,"height":2908,"viewBox":2909,"preserveAspectRatio":2910},"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg","400em","1.08em","0 0 400000 1080","xMinYMin slice",[2912,2913],"path",{"d":2914},"M95,702\nc-2.7,0,-7.17,-2.7,-13.5,-8c-5.8,-5.3,-9.5,-10,-9.5,-14\nc0,-2,0.3,-3.3,1,-4c1.3,-2.7,23.83,-20.7,67.5,-54\nc44.2,-33.3,65.8,-50.3,66.5,-51c1.3,-1.3,3,-2,5,-2c4.7,0,8.7,3.3,12,10\ns173,378,173,378c0.7,0,35.3,-71,104,-213c68.7,-142,137.5,-285,206.5,-429\nc69,-144,104.5,-217.7,106.5,-221\nl0 -0\nc5.3,-9.3,12,-14,20,-14\nH400000v40H845.2724\ns-225.272,467,-225.272,467s-235,486,-235,486c-2.7,4.7,-9,7,-19,7\nc-6,0,-10,-1,-12,-3s-194,-422,-194,-422s-65,47,-65,47z\nM834 80h400000v40h-400000z",[2002,2916,2185],{"className":2917},[2184],[2002,2919,2921],{"className":2920},[2135],[2002,2922,2925],{"className":2923,"style":2924},[2139],"height:0.1133em;",[2002,2926],{}," 下降。",[2810,2929,1980],{"id":1980},[1793,2931,2932],{},"在目标函数下寻找最优参数或组合权重。",[1793,2934,2935],{},"中文解释：资产配置、模型校准和最大似然估计都离不开优化；约束条件和初值选择会明显影响结果。",[2810,2937,2939],{"id":2938},"mle","MLE",[1793,2941,2942],{},"在假设完整概率分布后，选择使观测数据最可能出现的参数。",[1793,2944,2945],{},"中文解释：效率高，但对分布假设敏感。",[2810,2947,2949],{"id":2948},"gmm","GMM",[1793,2951,2952],{},"利用理论给出的矩条件估计参数，不要求完整分布。",[1793,2954,2955],{},"中文解释：适合 Euler 方程和资产定价检验，但工具变量、权重矩阵和弱识别会影响结论。",[2810,2957,2959],{"id":2958},"bootstrap","Bootstrap",[1793,2961,2962],{},"通过重抽样估计统计量的不确定性。",[1793,2964,2965],{},"中文解释：当标准误公式复杂或样本分布非正态时，Bootstrap 能给出更直观的置信区间；时间序列要用块 Bootstrap 保留相关性。",[1800,2967,2969],{"id":2968},"例子一个最小实证检验设计","例子：一个最小实证检验设计",[1793,2971,2972],{},"想检验一个价值因子是否能解释股票横截面收益，可以按以下方式组织：",[1878,2974,2975,2978,2981,2984,2987,2990],{},[1881,2976,2977],{},"明确样本：A 股或美股、起止日期、剔除规则、调仓频率。",[1881,2979,2980],{},"构造因子：按账面市值比分组，形成多空组合。",[1881,2982,2983],{},"估计暴露：对测试组合做时间序列回归。",[1881,2985,2986],{},"检验 alpha：查看加入价值因子后 alpha 是否下降并失去显著性。",[1881,2988,2989],{},"稳健性：换样本期、换分组、加入规模和动量、考虑交易成本。",[1881,2991,2992],{},"解释：若有效，说明是风险补偿、行为偏差，还是会计变量代理其他信息。",[1793,2994,2995],{},"这个流程比直接报告“价值因子 t 值大于 2”更可靠，因为它把数据、模型、检验和经济解释分开检查。",[1800,2997,2999],{"id":2998},"可运行例题未来信息会制造漂亮的样本外结果","可运行例题：未来信息会制造漂亮的样本外结果",[1793,3001,3002],{},"两个模型都严格按前 200 期训练、后 100 期测试；区别在于第二个特征偷偷含有未来收益。时间切分本身不能修复特征泄漏。",[3004,3005],"pyodide",{"code64":3006,"layout":3007,"locale":7,"packages":3008,"title":3009},"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\u002FlvpfnibnlvoEiKQpmaXRfYW5kX3Rlc3QobGVha3lfZmVhdHVyZSwgIuWQq+acquadpeS\u002FoeaBr+eahOeJueW+gSIp","vertical","numpy","Python：point-in-time 特征与前视偏差",[1793,3011,3012,3013,3076],{},"泄漏模型的高 ",[2002,3014,3016,3036],{"className":3015},[2009],[2002,3017,3019],{"className":3018},[2013],[2015,3020,3021],{"xmlns":2017},[2020,3022,3023,3033],{},[2023,3024,3025],{},[3026,3027,3028,3030],"msup",{},[2029,3029,2031],{},[2571,3031,3032],{},"2",[2097,3034,3035],{"encoding":2099},"R^2",[2002,3037,3039],{"className":3038,"ariaHidden":2040},[2104],[2002,3040,3042,3046],{"className":3041},[2108],[2002,3043],{"className":3044,"style":3045},[2112],"height:0.8141em;",[2002,3047,3049,3052],{"className":3048},[2117],[2002,3050,2031],{"className":3051,"style":2122},[2117,2121],[2002,3053,3055],{"className":3054},[2126],[2002,3056,3058],{"className":3057},[2130],[2002,3059,3061],{"className":3060},[2135],[2002,3062,3064],{"className":3063,"style":3045},[2139],[2002,3065,3067,3070],{"style":3066},"top:-3.063em;margin-right:0.05em;",[2002,3068],{"className":3069,"style":2148},[2147],[2002,3071,3073],{"className":3072},[2152,2153,2154,2155],[2002,3074,3032],{"className":3075},[2117,2155]," 不代表可预测性。实证审计必须逐列记录发布时间、修订历史、可交易时点和形成期；语言模型还要核对训练语料截止日。",[1800,3078,3079],{"id":3079},"学习顺序建议",[1804,3081,3082,3095],{},[1807,3083,3084],{},[1810,3085,3086,3089,3092],{},[1813,3087,3088],{},"小节",[1813,3090,3091],{},"先抓住的问题",[1813,3093,3094],{},"需要特别留意",[1820,3096,3097,3107,3117,3128,3138,3148,3159],{},[1810,3098,3099,3101,3104],{},[1825,3100,1827],{},[1825,3102,3103],{},"如何用随机模拟近似价格或风险",[1825,3105,3106],{},"随机误差、收敛速度",[1810,3108,3109,3111,3114],{},[1825,3110,1835],{},[1825,3112,3113],{},"如何求组合权重或校准参数",[1825,3115,3116],{},"约束、局部最优、数值稳定",[1810,3118,3119,3122,3125],{},[1825,3120,3121],{},"10.3 参数估计",[1825,3123,3124],{},"如何从数据识别模型参数",[1825,3126,3127],{},"MLE、GMM、标准误",[1810,3129,3130,3132,3135],{},[1825,3131,1851],{},[1825,3133,3134],{},"如何判断模型解释力",[1825,3136,3137],{},"alpha、J 检验、样本外",[1810,3139,3140,3142,3145],{},[1825,3141,1859],{},[1825,3143,3144],{},"如何评估统计不确定性",[1825,3146,3147],{},"重抽样单位、块长度",[1810,3149,3150,3153,3156],{},[1825,3151,3152],{},"10.6 机器学习",[1825,3154,3155],{},"如何处理高维预测问题",[1825,3157,3158],{},"过拟合、交叉验证、可解释性",[1810,3160,3161,3164,3167],{},[1825,3162,3163],{},"10.7 Python 案例",[1825,3165,3166],{},"如何把流程复现为代码",[1825,3168,3169],{},"数据清洗、随机种子、结果记录",[1800,3171,3172],{"id":3172},"常见错误与考试陷阱",[1878,3174,3175,3182,3188,3194,3200],{},[1881,3176,3177,3181],{},[3178,3179,3180],"strong",{},"只报告点估计，不报告不确定性","：参数估计没有标准误或置信区间，很难解释。",[1881,3183,3184,3187],{},[3178,3185,3186],{},"把样本内拟合当成预测能力","：资产定价更重视样本外表现和稳健性。",[1881,3189,3190,3193],{},[3178,3191,3192],{},"忽略时间序列相关性","：普通 IID 标准误常低估不确定性。",[1881,3195,3196,3199],{},[3178,3197,3198],{},"反复试验后不做多重检验调整","：因子越试越多，偶然显著越常见。",[1881,3201,3202,3205],{},[3178,3203,3204],{},"把统计显著当成经济显著","：年化 alpha 若小于交易成本或容量很小，投资意义有限。",[1800,3207,3208],{"id":3208},"自测题",[1878,3210,3211,3214,3217,3220],{},[1881,3212,3213],{},"Monte Carlo 路径数增加 4 倍，标准误大约如何变化？",[1881,3215,3216],{},"为什么时间序列收益数据做 Bootstrap 时常用块 Bootstrap？",[1881,3218,3219],{},"GMM 相比 MLE 的优势和代价是什么？",[1881,3221,3222],{},"一个策略样本内 Sharpe 很高，但换样本期后消失，你会如何解释？",[1800,3224,3225],{"id":3225},"答案指引",[1878,3227,3228,3313,3316,3319],{},[1881,3229,3230,3231,3312],{},"标准误约减半，因为 Monte Carlo 误差按 ",[2002,3232,3234,3253],{"className":3233},[2009],[2002,3235,3237],{"className":3236},[2013],[2015,3238,3239],{"xmlns":2017},[2020,3240,3241,3251],{},[2023,3242,3243,3245,3247],{},[2571,3244,2834],{},[2029,3246,2837],{"mathvariant":2071},[2839,3248,3249],{},[2029,3250,2843],{},[2097,3252,2846],{"encoding":2099},[2002,3254,3256],{"className":3255,"ariaHidden":2040},[2104],[2002,3257,3259,3262,3265],{"className":3258},[2108],[2002,3260],{"className":3261,"style":2856},[2112],[2002,3263,2860],{"className":3264},[2117],[2002,3266,3268],{"className":3267},[2117,2864],[2002,3269,3271,3304],{"className":3270},[2130,2131],[2002,3272,3274,3301],{"className":3273},[2135],[2002,3275,3277,3289],{"className":3276,"style":2874},[2139],[2002,3278,3280,3283],{"className":3279,"style":2879},[2878],[2002,3281],{"className":3282,"style":2883},[2147],[2002,3284,3286],{"className":3285,"style":2887},[2117],[2002,3287,2843],{"className":3288,"style":2891},[2117,2121],[2002,3290,3291,3294],{"style":2894},[2002,3292],{"className":3293,"style":2883},[2147],[2002,3295,3297],{"className":3296,"style":2902},[2901],[2904,3298,3299],{"xmlns":2906,"width":2907,"height":2908,"viewBox":2909,"preserveAspectRatio":2910},[2912,3300],{"d":2914},[2002,3302,2185],{"className":3303},[2184],[2002,3305,3307],{"className":3306},[2135],[2002,3308,3310],{"className":3309,"style":2924},[2139],[2002,3311],{}," 收敛。",[1881,3314,3315],{},"金融收益可能存在自相关、波动率聚集和状态持续性；块 Bootstrap 能保留局部时间依赖。",[1881,3317,3318],{},"GMM 不要求完整分布，只需矩条件，适合资产定价 Euler 方程；代价是可能效率较低，且对工具变量和权重矩阵敏感。",[1881,3320,3321],{},"可能是过拟合、数据挖掘、市场结构变化、交易拥挤，也可能是样本外风险状态不同；需要重新检查经济机制和稳健性。",[1800,3323,3324],{"id":3324},"小结与课程收束",[1793,3326,3327],{},"本章把前面所有理论变成可估计、可检验、可复现的研究流程。完成本章后，建议回到 CAPM、因子模型、跨期定价或期权定价中任选一个模型，做一次完整小项目：写清假设，整理数据，估计参数，检验模型，解释结果，并记录所有失败和修改。资产定价的真正训练，往往就在这些细节里。",[1793,3329,3330,3331,3336],{},"下一步进入",[3332,3333,3335],"a",{"href":3334},"..\u002F11-interactive-labs\u002F","资产定价浏览器交互实验","，用成对的 Python\u002FR 单元复现收益率、组合、CAPM、多因子、SDF、期权和债券计算。",{"title":10,"searchDepth":3338,"depth":3338,"links":3339},2,[3340,3341,3342,3343,3344,3345,3353,3354,3355,3356,3357,3358,3359],{"id":1802,"depth":3338,"text":1802},{"id":1873,"depth":3338,"text":1873},{"id":1919,"depth":3338,"text":1919},{"id":1997,"depth":3338,"text":1997},{"id":2724,"depth":3338,"text":2724},{"id":2808,"depth":3338,"text":2808,"children":3346},[3347,3349,3350,3351,3352],{"id":2812,"depth":3348,"text":2813},3,{"id":1980,"depth":3348,"text":1980},{"id":2938,"depth":3348,"text":2939},{"id":2948,"depth":3348,"text":2949},{"id":2958,"depth":3348,"text":2959},{"id":2968,"depth":3338,"text":2969},{"id":2998,"depth":3338,"text":2999},{"id":3079,"depth":3338,"text":3079},{"id":3172,"depth":3338,"text":3172},{"id":3208,"depth":3338,"text":3208},{"id":3225,"depth":3338,"text":3225},{"id":3324,"depth":3338,"text":3324},"蒙特卡洛模拟、优化方法与模型检验","md",{"sidebar":3363},{"order":3364},10,true,{"title":1308,"description":3360},"YRMyadUrNsgyTG3T0esv9TfBZ3MD-iKA2uJlGwL5JLk",[3369,3371],{"title":1302,"path":1303,"stem":1304,"description":3370,"children":-1},"有效市场假说、资产定价异象与行为金融",{"title":1314,"path":1315,"stem":1316,"description":3372,"children":-1},"使用可点击运行的 Python 与 R 单元完成收益率、组合优化、CAPM、多因子、SDF、期权和债券定价实验。",1785754746871]