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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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学生选择汽车、公交、地铁还是自行车。票价下降会改变多少选择概率，而不是“选择增加几个单位”？",[1796,1804,1805,1806,1886],{},"当结果是 0\u002F1、类别或有序等级时，线性回归可能给出超出 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的概率。离散选择模型直接刻画选择概率，但结构解释仍依赖效用、信息和选择集合假设。",[1888,1889,1890],"h2",{"id":1890},"学习目标",[1796,1892,1893],{},"你应能：",[1895,1896,1897,1901,1904,1907,1910],"ol",{},[1898,1899,1900],"li",{},"从潜在效用推导二元选择概率；",[1898,1902,1903],{},"区分 Logit 系数、优势比、预测概率与边际效应；",[1898,1905,1906],{},"解释多项 Logit 的 IIA；",[1898,1908,1909],{},"识别选择集合、内生价格与尺度归一化问题；",[1898,1911,1912],{},"用概率和政策变化表达结果。",[1888,1914,1916],{"id":1915},"_1-二元选择的潜在效用","1. 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分布：",[1807,2419,2421],{"className":2420},[1920],[1807,2422,2424,2536],{"className":2423},[1810],[1807,2425,2427],{"className":2426},[1814],[1816,2428,2429],{"xmlns":1818,"display":1929},[1820,2430,2431,2533],{},[1823,2432,2433,2436,2439,2445,2447,2449,2452,2458,2461,2463,2466,2468,2476,2478,2480,2482,2531],{},[1938,2434,2435],{},"P",[1826,2437,2438],{"stretchy":1828},"(",[1969,2440,2441,2443],{},[1938,2442,1940],{},[1938,2444,1943],{},[1826,2446,1949],{},[1831,2448,1840],{},[1826,2450,2451],{},"∣",[1969,2453,2454,2456],{},[1938,2455,1954],{},[1938,2457,1943],{},[1826,2459,2460],{"stretchy":1828},")",[1826,2462,1949],{},[1938,2464,2465],{"mathvariant":1959},"Λ",[1826,2467,2438],{"stretchy":1828},[1935,2469,2470,2472,2474],{},[1938,2471,1954],{},[1938,2473,1943],{},[1826,2475,1961],{"mathvariant":1959,"lspace":1960,"rspace":1960},[1938,2477,1964],{},[1826,2479,2460],{"stretchy":1828},[1826,2481,1949],{},[2483,2484,2485,2507],"mfrac",{},[1823,2486,2487,2490,2493,2495,2503,2505],{},[1938,2488,2489],{},"exp",[1826,2491,2492],{},"⁡",[1826,2494,2438],{"stretchy":1828},[1935,2496,2497,2499,2501],{},[1938,2498,1954],{},[1938,2500,1943],{},[1826,2502,1961],{"mathvariant":1959,"lspace":1960,"rspace":1960},[1938,2504,1964],{},[1826,2506,2460],{"stretchy":1828},[1823,2508,2509,2511,2513,2515,2517,2519,2527,2529],{},[1831,2510,1840],{},[1826,2512,1967],{},[1938,2514,2489],{},[1826,2516,2492],{},[1826,2518,2438],{"stretchy":1828},[1935,2520,2521,2523,2525],{},[1938,2522,1954],{},[1938,2524,1943],{},[1826,2526,1961],{"mathvariant":1959,"lspace":1960,"rspace":1960},[1938,2528,1964],{},[1826,2530,2460],{"stretchy":1828},[1938,2532,2013],{"mathvariant":1959},[1845,2534,2535],{"encoding":1847},"P(Y_i=1\\mid X_i)=\\Lambda(X_i'\\beta)\n=\\frac{\\exp(X_i'\\beta)}{1+\\exp(X_i'\\beta)}.",[1807,2537,2539,2601,2619,2678,2760],{"className":2538,"ariaHidden":1836},[1852],[1807,2540,2542,2545,2549,2552,2592,2595,2598],{"className":2541},[1856],[1807,2543],{"className":2544,"style":1861},[1860],[1807,2546,2435],{"className":2547,"style":2548},[1869,2033],"margin-right:0.1389em;",[1807,2550,2438],{"className":2551},[1865],[1807,2553,2555,2558],{"className":2554},[1869],[1807,2556,1940],{"className":2557,"style":2034},[1869,2033],[1807,2559,2561],{"className":2560},[2038],[1807,2562,2564,2584],{"className":2563},[2042,2043],[1807,2565,2567,2581],{"className":2566},[2047],[1807,2568,2570],{"className":2569,"style":2211},[2051],[1807,2571,2572,2575],{"style":2266},[1807,2573],{"className":2574,"style":2060},[2059],[1807,2576,2578],{"className":2577},[2064,2065,2066,2067],[1807,2579,1943],{"className":2580},[1869,2033,2067],[1807,2582,2088],{"className":2583},[2087],[1807,2585,2587],{"className":2586},[2047],[1807,2588,2590],{"className":2589,"style":2233},[2051],[1807,2591],{},[1807,2593],{"className":2594,"style":2101},[1877],[1807,2596,1949],{"className":2597},[2105],[1807,2599],{"className":2600,"style":2101},[1877],[1807,2602,2604,2607,2610,2613,2616],{"className":2603},[1856],[1807,2605],{"className":2606,"style":1861},[1860],[1807,2608,1840],{"className":2609},[1869],[1807,2611],{"className":2612,"style":2101},[1877],[1807,2614,2451],{"className":2615},[2105],[1807,2617],{"className":2618,"style":2101},[1877],[1807,2620,2622,2625,2666,2669,2672,2675],{"className":2621},[1856],[1807,2623],{"className":2624,"style":1861},[1860],[1807,2626,2628,2631],{"className":2627},[1869],[1807,2629,1954],{"className":2630,"style":2122},[1869,2033],[1807,2632,2634],{"className":2633},[2038],[1807,2635,2637,2658],{"className":2636},[2042,2043],[1807,2638,2640,2655],{"className":2639},[2047],[1807,2641,2643],{"className":2642,"style":2211},[2051],[1807,2644,2646,2649],{"style":2645},"top:-2.55em;margin-left:-0.0785em;margin-right:0.05em;",[1807,2647],{"className":2648,"style":2060},[2059],[1807,2650,2652],{"className":2651},[2064,2065,2066,2067],[1807,2653,1943],{"className":2654},[1869,2033,2067],[1807,2656,2088],{"className":2657},[2087],[1807,2659,2661],{"className":2660},[2047],[1807,2662,2664],{"className":2663,"style":2233},[2051],[1807,2665],{},[1807,2667,2460],{"className":2668},[1885],[1807,2670],{"className":2671,"style":2101},[1877],[1807,2673,1949],{"className":2674},[2105],[1807,2676],{"className":2677,"style":2101},[1877],[1807,2679,2681,2685,2688,2691,2745,2748,2751,2754,2757],{"className":2680},[1856],[1807,2682],{"className":2683,"style":2684},[1860],"height:1.0519em;vertical-align:-0.25em;",[1807,2686,2465],{"className":2687},[1869],[1807,2689,2438],{"className":2690},[1865],[1807,2692,2694,2697],{"className":2693},[1869],[1807,2695,1954],{"className":2696,"style":2122},[1869,2033],[1807,2698,2700],{"className":2699},[2038],[1807,2701,2703,2737],{"className":2702},[2042,2043],[1807,2704,2706,2734],{"className":2705},[2047],[1807,2707,2709,2720],{"className":2708,"style":2135},[2051],[1807,2710,2711,2714],{"style":2138},[1807,2712],{"className":2713,"style":2060},[2059],[1807,2715,2717],{"className":2716},[2064,2065,2066,2067],[1807,2718,1943],{"className":2719},[1869,2033,2067],[1807,2721,2722,2725],{"style":2073},[1807,2723],{"className":2724,"style":2060},[2059],[1807,2726,2728],{"className":2727},[2064,2065,2066,2067],[1807,2729,2731],{"className":2730},[1869,2067],[1807,2732,1961],{"className":2733},[1869,2067],[1807,2735,2088],{"className":2736},[2087],[1807,2738,2740],{"className":2739},[2047],[1807,2741,2743],{"className":2742,"style":2095},[2051],[1807,2744],{},[1807,2746,1964],{"className":2747,"style":2176},[1869,2033],[1807,2749,2460],{"className":2750},[1885],[1807,2752],{"className":2753,"style":2101},[1877],[1807,2755,1949],{"className":2756},[2105],[1807,2758],{"className":2759,"style":2101},[1877],[1807,2761,2763,2767,2985],{"className":2762},[1856],[1807,2764],{"className":2765,"style":2766},[1860],"height:2.3918em;vertical-align:-0.9629em;",[1807,2768,2770,2774,2982],{"className":2769},[1869],[1807,2771],{"className":2772},[1865,2773],"nulldelimiter",[1807,2775,2777],{"className":2776},[2483],[1807,2778,2780,2973],{"className":2779},[2042,2043],[1807,2781,2783,2970],{"className":2782},[2047],[1807,2784,2787,2880,2891],{"className":2785,"style":2786},[2051],"height:1.4289em;",[1807,2788,2790,2794],{"style":2789},"top:-2.314em;",[1807,2791],{"className":2792,"style":2793},[2059],"height:3em;",[1807,2795,2797,2800,2803,2806,2809,2813,2816,2874,2877],{"className":2796},[1869],[1807,2798,1840],{"className":2799},[1869],[1807,2801],{"className":2802,"style":2034},[1877],[1807,2804,1967],{"className":2805},[2083],[1807,2807],{"className":2808,"style":2034},[1877],[1807,2810,2489],{"className":2811},[2812],"mop",[1807,2814,2438],{"className":2815},[1865],[1807,2817,2819,2822],{"className":2818},[1869],[1807,2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Probit。两者通常产生相近概率，但误差方差归一化不同，原始系数不能直接横向比较。",[1888,2992,2994],{"id":2993},"_2-系数不是概率变化","2. 系数不是概率变化",[1796,2996,2997,2998,3073],{},"连续变量 ",[1807,2999,3001,3021],{"className":3000},[1810],[1807,3002,3004],{"className":3003},[1814],[1816,3005,3006],{"xmlns":1818},[1820,3007,3008,3018],{},[1823,3009,3010],{},[1969,3011,3012,3015],{},[1938,3013,3014],{},"x",[1938,3016,3017],{},"k",[1845,3019,3020],{"encoding":1847},"x_k",[1807,3022,3024],{"className":3023,"ariaHidden":1836},[1852],[1807,3025,3027,3031],{"className":3026},[1856],[1807,3028],{"className":3029,"style":3030},[1860],"height:0.5806em;vertical-align:-0.15em;",[1807,3032,3034,3037],{"className":3033},[1869],[1807,3035,3014],{"className":3036},[1869,2033],[1807,3038,3040],{"className":3039},[2038],[1807,3041,3043,3065],{"className":3042},[2042,2043],[1807,3044,3046,3062],{"className":3045},[2047],[1807,3047,3050],{"className":3048,"style":3049},[2051],"height:0.3361em;",[1807,3051,3052,3055],{"style":2214},[1807,3053],{"className":3054,"style":2060},[2059],[1807,3056,3058],{"className":3057},[2064,2065,2066,2067],[1807,3059,3017],{"className":3060,"style":3061},[1869,2033,2067],"margin-right:0.0315em;",[1807,3063,2088],{"className":3064},[2087],[1807,3066,3068],{"className":3067},[2047],[1807,3069,3071],{"className":3070,"style":2233},[2051],[1807,3072],{}," 的 Logit 边际效应：",[1807,3075,3077],{"className":3076},[1920],[1807,3078,3080,3160],{"className":3079},[1810],[1807,3081,3083],{"className":3082},[1814],[1816,3084,3085],{"xmlns":1818,"display":1929},[1820,3086,3087,3157],{},[1823,3088,3089,3122,3124,3130,3132,3134,3136,3138,3140,3142,3145,3147,3149,3151,3153,3155],{},[2483,3090,3091,3112],{},[1823,3092,3093,3096,3098,3100,3102,3104,3106,3108,3110],{},[1938,3094,3095],{"mathvariant":1959},"∂",[1938,3097,2435],{},[1826,3099,2438],{"stretchy":1828},[1938,3101,1940],{},[1826,3103,1949],{},[1831,3105,1840],{},[1826,3107,2451],{},[1938,3109,1954],{},[1826,3111,2460],{"stretchy":1828},[1823,3113,3114,3116],{},[1938,3115,3095],{"mathvariant":1959},[1969,3117,3118,3120],{},[1938,3119,3014],{},[1938,3121,3017],{},[1826,3123,1949],{},[1969,3125,3126,3128],{},[1938,3127,1964],{},[1938,3129,3017],{},[1938,3131,1796],{},[1826,3133,2438],{"stretchy":1828},[1938,3135,1954],{},[1826,3137,2460],{"stretchy":1828},[1826,3139,1829],{"stretchy":1828},[1831,3141,1840],{},[1826,3143,3144],{},"−",[1938,3146,1796],{},[1826,3148,2438],{"stretchy":1828},[1938,3150,1954],{},[1826,3152,2460],{"stretchy":1828},[1826,3154,1843],{"stretchy":1828},[1938,3156,2013],{"mathvariant":1959},[1845,3158,3159],{"encoding":1847},"\\frac{\\partial P(Y=1\\mid X)}{\\partial x_k}\n=\\beta_k p(X)[1-p(X)].",[1807,3161,3163,3320,3394],{"className":3162,"ariaHidden":1836},[1852],[1807,3164,3166,3170,3311,3314,3317],{"className":3165},[1856],[1807,3167],{"className":3168,"style":3169},[1860],"height:2.263em;vertical-align:-0.836em;",[1807,3171,3173,3176,3308],{"className":3172},[1869],[1807,3174],{"className":3175},[1865,2773],[1807,3177,3179],{"className":3178},[2483],[1807,3180,3182,3299],{"className":3181},[2042,2043],[1807,3183,3185,3296],{"className":3184},[2047],[1807,3186,3189,3241,3249],{"className":3187,"style":3188},[2051],"height:1.427em;",[1807,3190,3191,3194],{"style":2789},[1807,3192],{"className":3193,"style":2793},[2059],[1807,3195,3197,3201],{"className":3196},[1869],[1807,3198,3095],{"className":3199,"style":3200},[1869],"margin-right:0.0556em;",[1807,3202,3204,3207],{"className":3203},[1869],[1807,3205,3014],{"className":3206},[1869,2033],[1807,3208,3210],{"className":3209},[2038],[1807,3211,3213,3233],{"className":3212},[2042,2043],[1807,3214,3216,3230],{"className":3215},[2047],[1807,3217,3219],{"className":3218,"style":3049},[2051],[1807,3220,3221,3224],{"style":2214},[1807,3222],{"className":3223,"style":2060},[2059],[1807,3225,3227],{"className":3226},[2064,2065,2066,2067],[1807,3228,3017],{"className":3229,"style":3061},[1869,2033,2067],[1807,3231,2088],{"className":3232},[2087],[1807,3234,3236],{"className":3235},[2047],[1807,3237,3239],{"className":3238,"style":2233},[2051],[1807,3240],{},[1807,3242,3243,3246],{"style":2882},[1807,3244],{"className":3245,"style":2793},[2059],[1807,3247],{"className":3248,"style":2890},[2889],[1807,3250,3251,3254],{"style":2893},[1807,3252],{"className":3253,"style":2793},[2059],[1807,3255,3257,3260,3263,3266,3269,3272,3275,3278,3281,3284,3287,3290,3293],{"className":3256},[1869],[1807,3258,3095],{"className":3259,"style":3200},[1869],[1807,3261,2435],{"className":3262,"style":2548},[1869,2033],[1807,3264,2438],{"className":3265},[1865],[1807,3267,1940],{"className":3268,"style":2034},[1869,2033],[1807,3270],{"className":3271,"style":2101},[1877],[1807,3273,1949],{"className":3274},[2105],[1807,3276],{"className":3277,"style":2101},[1877],[1807,3279,1840],{"className":3280},[1869],[1807,3282],{"className":3283,"style":2101},[1877],[1807,3285,2451],{"className":3286},[2105],[1807,3288],{"className":3289,"style":2101},[1877],[1807,3291,1954],{"className":3292,"style":2122},[1869,2033],[1807,3294,2460],{"className":3295},[1885],[1807,3297,2088],{"className":3298},[2087],[1807,3300,3302],{"className":3301},[2047],[1807,3303,3306],{"className":3304,"style":3305},[2051],"height:0.836em;",[1807,3307],{},[1807,3309],{"className":3310},[1885,2773],[1807,3312],{"className":3313,"style":2101},[1877],[1807,3315,1949],{"className":3316},[2105],[1807,3318],{"className":3319,"style":2101},[1877],[1807,3321,3323,3326,3367,3370,3373,3376,3379,3382,3385,3388,3391],{"className":3322},[1856],[1807,3324],{"className":3325,"style":1861},[1860],[1807,3327,3329,3332],{"className":3328},[1869],[1807,3330,1964],{"className":3331,"style":2176},[1869,2033],[1807,3333,3335],{"className":3334},[2038],[1807,3336,3338,3359],{"className":3337},[2042,2043],[1807,3339,3341,3356],{"className":3340},[2047],[1807,3342,3344],{"className":3343,"style":3049},[2051],[1807,3345,3347,3350],{"style":3346},"top:-2.55em;margin-left:-0.0528em;margin-right:0.05em;",[1807,3348],{"className":3349,"style":2060},[2059],[1807,3351,3353],{"className":3352},[2064,2065,2066,2067],[1807,3354,3017],{"className":3355,"style":3061},[1869,2033,2067],[1807,3357,2088],{"className":3358},[2087],[1807,3360,3362],{"className":3361},[2047],[1807,3363,3365],{"className":3364,"style":2233},[2051],[1807,3366],{},[1807,3368,1796],{"className":3369},[1869,2033],[1807,3371,2438],{"className":3372},[1865],[1807,3374,1954],{"className":3375,"style":2122},[1869,2033],[1807,3377,2460],{"className":3378},[1885],[1807,3380,1829],{"className":3381},[1865],[1807,3383,1840],{"className":3384},[1869],[1807,3386],{"className":3387,"style":2034},[1877],[1807,3389,3144],{"className":3390},[2083],[1807,3392],{"className":3393,"style":2034},[1877],[1807,3395,3397,3400,3403,3406,3409,3413],{"className":3396},[1856],[1807,3398],{"className":3399,"style":1861},[1860],[1807,3401,1796],{"className":3402},[1869,2033],[1807,3404,2438],{"className":3405},[1865],[1807,3407,1954],{"className":3408,"style":2122},[1869,2033],[1807,3410,3412],{"className":3411},[1885],")]",[1807,3414,2013],{"className":3415},[1869],[1796,3417,3418,3419,3448],{},"它随 ",[1807,3420,3422,3435],{"className":3421},[1810],[1807,3423,3425],{"className":3424},[1814],[1816,3426,3427],{"xmlns":1818},[1820,3428,3429,3433],{},[1823,3430,3431],{},[1938,3432,1954],{},[1845,3434,1954],{"encoding":1847},[1807,3436,3438],{"className":3437,"ariaHidden":1836},[1852],[1807,3439,3441,3445],{"className":3440},[1856],[1807,3442],{"className":3443,"style":3444},[1860],"height:0.6833em;",[1807,3446,1954],{"className":3447,"style":2122},[1869,2033]," 改变。应报告：",[3450,3451,3452,3455,3458,3461],"ul",{},[1898,3453,3454],{},"平均边际效应（AME）；",[1898,3456,3457],{},"代表性或政策相关人群的预测概率；",[1898,3459,3460],{},"离散变量从 0 改为 1 的概率差；",[1898,3462,3463],{},"置信区间，而不只报告优势比。",[1796,3465,3466,3467,3578],{},"例如 ",[1807,3468,3470,3501],{"className":3469},[1810],[1807,3471,3473],{"className":3472},[1814],[1816,3474,3475],{"xmlns":1818},[1820,3476,3477,3498],{},[1823,3478,3479,3481,3483,3485,3491,3493,3495],{},[1938,3480,2489],{},[1826,3482,2492],{},[1826,3484,2438],{"stretchy":1828},[1969,3486,3487,3489],{},[1938,3488,1964],{},[1938,3490,3017],{},[1826,3492,2460],{"stretchy":1828},[1826,3494,1949],{},[1831,3496,3497],{},"1.5",[1845,3499,3500],{"encoding":1847},"\\exp(\\beta_k)=1.5",[1807,3502,3504,3568],{"className":3503,"ariaHidden":1836},[1852],[1807,3505,3507,3510,3513,3516,3556,3559,3562,3565],{"className":3506},[1856],[1807,3508],{"className":3509,"style":1861},[1860],[1807,3511,2489],{"className":3512},[2812],[1807,3514,2438],{"className":3515},[1865],[1807,3517,3519,3522],{"className":3518},[1869],[1807,3520,1964],{"className":3521,"style":2176},[1869,2033],[1807,3523,3525],{"className":3524},[2038],[1807,3526,3528,3548],{"className":3527},[2042,2043],[1807,3529,3531,3545],{"className":3530},[2047],[1807,3532,3534],{"className":3533,"style":3049},[2051],[1807,3535,3536,3539],{"style":3346},[1807,3537],{"className":3538,"style":2060},[2059],[1807,3540,3542],{"className":3541},[2064,2065,2066,2067],[1807,3543,3017],{"className":3544,"style":3061},[1869,2033,2067],[1807,3546,2088],{"className":3547},[2087],[1807,3549,3551],{"className":3550},[2047],[1807,3552,3554],{"className":3553,"style":2233},[2051],[1807,3555],{},[1807,3557,2460],{"className":3558},[1885],[1807,3560],{"className":3561,"style":2101},[1877],[1807,3563,1949],{"className":3564},[2105],[1807,3566],{"className":3567,"style":2101},[1877],[1807,3569,3571,3575],{"className":3570},[1856],[1807,3572],{"className":3573,"style":3574},[1860],"height:0.6444em;",[1807,3576,3497],{"className":3577},[1869]," 表示优势乘以 1.5，不表示概率增加 50% 或 50 个百分点。",[1888,3580,3582],{"id":3581},"_3-可运行案例助学金与大学入学概率","3. 可运行案例：助学金与大学入学概率",[1796,3584,3585],{},"模拟中入学取决于标准化成绩、是否获得助学金和通勤负担。代码用 Newton–Raphson 估计 Logit，并把成绩系数转成平均边际效应。",[3587,3588],"pyodide",{"code64":3589,"layout":3590,"locale":7,"packages":3591,"title":3592},"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","vertical","numpy","Python：手工估计 Logit 与平均边际效应",[1796,3594,3595],{},"这个模拟把助学金设为外生。真实助学金通常按家庭收入、成绩或申请行为发放；若目标是助学金因果效应，还需随机化、RDD、IV 或可辩护的无混杂设计。",[1888,3597,3599],{"id":3598},"_4-多项选择与随机效用","4. 多项选择与随机效用",[1796,3601,3602,3603,3632,3633,3664],{},"个体 ",[1807,3604,3606,3619],{"className":3605},[1810],[1807,3607,3609],{"className":3608},[1814],[1816,3610,3611],{"xmlns":1818},[1820,3612,3613,3617],{},[1823,3614,3615],{},[1938,3616,1943],{},[1845,3618,1943],{"encoding":1847},[1807,3620,3622],{"className":3621,"ariaHidden":1836},[1852],[1807,3623,3625,3629],{"className":3624},[1856],[1807,3626],{"className":3627,"style":3628},[1860],"height:0.6595em;",[1807,3630,1943],{"className":3631},[1869,2033]," 对选项 ",[1807,3634,3636,3650],{"className":3635},[1810],[1807,3637,3639],{"className":3638},[1814],[1816,3640,3641],{"xmlns":1818},[1820,3642,3643,3648],{},[1823,3644,3645],{},[1938,3646,3647],{},"j",[1845,3649,3647],{"encoding":1847},[1807,3651,3653],{"className":3652,"ariaHidden":1836},[1852],[1807,3654,3656,3660],{"className":3655},[1856],[1807,3657],{"className":3658,"style":3659},[1860],"height:0.854em;vertical-align:-0.1944em;",[1807,3661,3647],{"className":3662,"style":3663},[1869,2033],"margin-right:0.0572em;"," 的效用：",[1807,3666,3668],{"className":3667},[1920],[1807,3669,3671,3787],{"className":3670},[1810],[1807,3672,3674],{"className":3673},[1814],[1816,3675,3676],{"xmlns":1818,"display":1929},[1820,3677,3678,3784],{},[1823,3679,3680,3691,3693,3704,3706,3716,3718,3720,3730,3732,3782],{},[1969,3681,3682,3685],{},[1938,3683,3684],{},"U",[1823,3686,3687,3689],{},[1938,3688,1943],{},[1938,3690,3647],{},[1826,3692,1949],{},[1969,3694,3695,3698],{},[1938,3696,3697],{},"V",[1823,3699,3700,3702],{},[1938,3701,1943],{},[1938,3703,3647],{},[1826,3705,1967],{},[1969,3707,3708,3710],{},[1938,3709,1973],{},[1823,3711,3712,3714],{},[1938,3713,1943],{},[1938,3715,3647],{},[1826,3717,1837],{"separator":1836},[1877,3719],{"width":1980},[1969,3721,3722,3724],{},[1938,3723,2435],{},[1823,3725,3726,3728],{},[1938,3727,1943],{},[1938,3729,3647],{},[1826,3731,1949],{},[2483,3733,3734,3754],{},[1823,3735,3736,3738,3740,3742,3752],{},[1938,3737,2489],{},[1826,3739,2492],{},[1826,3741,2438],{"stretchy":1828},[1969,3743,3744,3746],{},[1938,3745,3697],{},[1823,3747,3748,3750],{},[1938,3749,1943],{},[1938,3751,3647],{},[1826,3753,2460],{"stretchy":1828},[1823,3755,3756,3764,3766,3768,3770,3780],{},[3757,3758,3759,3762],"munder",{},[1826,3760,3761],{},"∑",[1938,3763,3017],{},[1938,3765,2489],{},[1826,3767,2492],{},[1826,3769,2438],{"stretchy":1828},[1969,3771,3772,3774],{},[1938,3773,3697],{},[1823,3775,3776,3778],{},[1938,3777,1943],{},[1938,3779,3017],{},[1826,3781,2460],{"stretchy":1828},[1938,3783,2013],{"mathvariant":1959},[1845,3785,3786],{"encoding":1847},"U_{ij}=V_{ij}+\\varepsilon_{ij},\n\\qquad\nP_{ij}=\\frac{\\exp(V_{ij})}{\\sum_k\\exp(V_{ik})}.",[1807,3788,3790,3853,3911,4022],{"className":3789,"ariaHidden":1836},[1852],[1807,3791,3793,3797,3844,3847,3850],{"className":3792},[1856],[1807,3794],{"className":3795,"style":3796},[1860],"height:0.9694em;vertical-align:-0.2861em;",[1807,3798,3800,3804],{"className":3799},[1869],[1807,3801,3684],{"className":3802,"style":3803},[1869,2033],"margin-right:0.109em;",[1807,3805,3807],{"className":3806},[2038],[1807,3808,3810,3835],{"className":3809},[2042,2043],[1807,3811,3813,3832],{"className":3812},[2047],[1807,3814,3816],{"className":3815,"style":2211},[2051],[1807,3817,3819,3822],{"style":3818},"top:-2.55em;margin-left:-0.109em;margin-right:0.05em;",[1807,3820],{"className":3821,"style":2060},[2059],[1807,3823,3825],{"className":3824},[2064,2065,2066,2067],[1807,3826,3828],{"className":3827},[1869,2067],[1807,3829,3831],{"className":3830,"style":3663},[1869,2033,2067],"ij",[1807,3833,2088],{"className":3834},[2087],[1807,3836,3838],{"className":3837},[2047],[1807,3839,3842],{"className":3840,"style":3841},[2051],"height:0.2861em;",[1807,3843],{},[1807,3845],{"className":3846,"style":2101},[1877],[1807,3848,1949],{"className":3849},[2105],[1807,3851],{"className":3852,"style":2101},[1877],[1807,3854,3856,3859,3902,3905,3908],{"className":3855},[1856],[1807,3857],{"className":3858,"style":3796},[1860],[1807,3860,3862,3865],{"className":3861},[1869],[1807,3863,3697],{"className":3864,"style":2034},[1869,2033],[1807,3866,3868],{"className":3867},[2038],[1807,3869,3871,3894],{"className":3870},[2042,2043],[1807,3872,3874,3891],{"className":3873},[2047],[1807,3875,3877],{"className":3876,"style":2211},[2051],[1807,3878,3879,3882],{"style":2266},[1807,3880],{"className":3881,"style":2060},[2059],[1807,3883,3885],{"className":3884},[2064,2065,2066,2067],[1807,3886,3888],{"className":3887},[1869,2067],[1807,3889,3831],{"className":3890,"style":3663},[1869,2033,2067],[1807,3892,2088],{"className":3893},[2087],[1807,3895,3897],{"className":3896},[2047],[1807,3898,3900],{"className":3899,"style":3841},[2051],[1807,3901],{},[1807,3903],{"className":3904,"style":2034},[1877],[1807,3906,1967],{"className":3907},[2083],[1807,3909],{"className":3910,"style":2034},[1877],[1807,3912,3914,3917,3960,3963,3966,3969,4013,4016,4019],{"className":3913},[1856],[1807,3915],{"className":3916,"style":3796},[1860],[1807,3918,3920,3923],{"className":3919},[1869],[1807,3921,1973],{"className":3922},[1869,2033],[1807,3924,3926],{"className":3925},[2038],[1807,3927,3929,3952],{"className":3928},[2042,2043],[1807,3930,3932,3949],{"className":3931},[2047],[1807,3933,3935],{"className":3934,"style":2211},[2051],[1807,3936,3937,3940],{"style":2214},[1807,3938],{"className":3939,"style":2060},[2059],[1807,3941,3943],{"className":3942},[2064,2065,2066,2067],[1807,3944,3946],{"className":3945},[1869,2067],[1807,3947,3831],{"className":3948,"style":3663},[1869,2033,2067],[1807,3950,2088],{"className":3951},[2087],[1807,3953,3955],{"className":3954},[2047],[1807,3956,3958],{"className":3957,"style":3841},[2051],[1807,3959],{},[1807,3961,1837],{"className":3962},[1873],[1807,3964],{"className":3965,"style":2242},[1877],[1807,3967],{"className":3968,"style":1878},[1877],[1807,3970,3972,3975],{"className":3971},[1869],[1807,3973,2435],{"className":3974,"style":2548},[1869,2033],[1807,3976,3978],{"className":3977},[2038],[1807,3979,3981,4005],{"className":3980},[2042,2043],[1807,3982,3984,4002],{"className":3983},[2047],[1807,3985,3987],{"className":3986,"style":2211},[2051],[1807,3988,3990,3993],{"style":3989},"top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;",[1807,3991],{"className":3992,"style":2060},[2059],[1807,3994,3996],{"className":3995},[2064,2065,2066,2067],[1807,3997,3999],{"className":3998},[1869,2067],[1807,4000,3831],{"className":4001,"style":3663},[1869,2033,2067],[1807,4003,2088],{"className":4004},[2087],[1807,4006,4008],{"className":4007},[2047],[1807,4009,4011],{"className":4010,"style":3841},[2051],[1807,4012],{},[1807,4014],{"className":4015,"style":2101},[1877],[1807,4017,1949],{"className":4018},[2105],[1807,4020],{"className":4021,"style":2101},[1877],[1807,4023,4025,4029,4240],{"className":4024},[1856],[1807,4026],{"className":4027,"style":4028},[1860],"height:2.4127em;vertical-align:-0.9857em;",[1807,4030,4032,4035,4237],{"className":4031},[1869],[1807,4033],{"className":4034},[1865,2773],[1807,4036,4038],{"className":4037},[2483],[1807,4039,4041,4228],{"className":4040},[2042,2043],[1807,4042,4044,4225],{"className":4043},[2047],[1807,4045,4047,4157,4165],{"className":4046,"style":3188},[2051],[1807,4048,4049,4052],{"style":2789},[1807,4050],{"className":4051,"style":2793},[2059],[1807,4053,4055,4101,4104,4107,4110,4154],{"className":4054},[1869],[1807,4056,4058,4064],{"className":4057},[2812],[1807,4059,3761],{"className":4060,"style":4063},[2812,4061,4062],"op-symbol","small-op","position:relative;top:0em;",[1807,4065,4067],{"className":4066},[2038],[1807,4068,4070,4092],{"className":4069},[2042,2043],[1807,4071,4073,4089],{"className":4072},[2047],[1807,4074,4077],{"className":4075,"style":4076},[2051],"height:0.1864em;",[1807,4078,4080,4083],{"style":4079},"top:-2.4003em;margin-left:0em;margin-right:0.05em;",[1807,4081],{"className":4082,"style":2060},[2059],[1807,4084,4086],{"className":4085},[2064,2065,2066,2067],[1807,4087,3017],{"className":4088,"style":3061},[1869,2033,2067],[1807,4090,2088],{"className":4091},[2087],[1807,4093,4095],{"className":4094},[2047],[1807,4096,4099],{"className":4097,"style":4098},[2051],"height:0.2997em;",[1807,4100],{},[1807,4102],{"className":4103,"style":1878},[1877],[1807,4105,2489],{"className":4106},[2812],[1807,4108,2438],{"className":4109},[1865],[1807,4111,4113,4116],{"className":4112},[1869],[1807,4114,3697],{"className":4115,"style":2034},[1869,2033],[1807,4117,4119],{"className":4118},[2038],[1807,4120,4122,4146],{"className":4121},[2042,2043],[1807,4123,4125,4143],{"className":4124},[2047],[1807,4126,4128],{"className":4127,"style":3049},[2051],[1807,4129,4130,4133],{"style":2266},[1807,4131],{"className":4132,"style":2060},[2059],[1807,4134,4136],{"className":4135},[2064,2065,2066,2067],[1807,4137,4139],{"className":4138},[1869,2067],[1807,4140,4142],{"className":4141,"style":3061},[1869,2033,2067],"ik",[1807,4144,2088],{"className":4145},[2087],[1807,4147,4149],{"className":4148},[2047],[1807,4150,4152],{"className":4151,"style":2233},[2051],[1807,4153],{},[1807,4155,2460],{"className":4156},[1885],[1807,4158,4159,4162],{"style":2882},[1807,4160],{"className":4161,"style":2793},[2059],[1807,4163],{"className":4164,"style":2890},[2889],[1807,4166,4167,4170],{"style":2893},[1807,4168],{"className":4169,"style":2793},[2059],[1807,4171,4173,4176,4179,4222],{"className":4172},[1869],[1807,4174,2489],{"className":4175},[2812],[1807,4177,2438],{"className":4178},[1865],[1807,4180,4182,4185],{"className":4181},[1869],[1807,4183,3697],{"className":4184,"style":2034},[1869,2033],[1807,4186,4188],{"className":4187},[2038],[1807,4189,4191,4214],{"className":4190},[2042,2043],[1807,4192,4194,4211],{"className":4193},[2047],[1807,4195,4197],{"className":4196,"style":2211},[2051],[1807,4198,4199,4202],{"style":2266},[1807,4200],{"className":4201,"style":2060},[2059],[1807,4203,4205],{"className":4204},[2064,2065,2066,2067],[1807,4206,4208],{"className":4207},[1869,2067],[1807,4209,3831],{"className":4210,"style":3663},[1869,2033,2067],[1807,4212,2088],{"className":4213},[2087],[1807,4215,4217],{"className":4216},[2047],[1807,4218,4220],{"className":4219,"style":3841},[2051],[1807,4221],{},[1807,4223,2460],{"className":4224},[1885],[1807,4226,2088],{"className":4227},[2087],[1807,4229,4231],{"className":4230},[2047],[1807,4232,4235],{"className":4233,"style":4234},[2051],"height:0.9857em;",[1807,4236],{},[1807,4238],{"className":4239},[1885,2773],[1807,4241,2013],{"className":4242},[1869],[1796,4244,4245,4322],{},[1807,4246,4248,4270],{"className":4247},[1810],[1807,4249,4251],{"className":4250},[1814],[1816,4252,4253],{"xmlns":1818},[1820,4254,4255,4267],{},[1823,4256,4257],{},[1969,4258,4259,4261],{},[1938,4260,3697],{},[1823,4262,4263,4265],{},[1938,4264,1943],{},[1938,4266,3647],{},[1845,4268,4269],{"encoding":1847},"V_{ij}",[1807,4271,4273],{"className":4272,"ariaHidden":1836},[1852],[1807,4274,4276,4279],{"className":4275},[1856],[1807,4277],{"className":4278,"style":3796},[1860],[1807,4280,4282,4285],{"className":4281},[1869],[1807,4283,3697],{"className":4284,"style":2034},[1869,2033],[1807,4286,4288],{"className":4287},[2038],[1807,4289,4291,4314],{"className":4290},[2042,2043],[1807,4292,4294,4311],{"className":4293},[2047],[1807,4295,4297],{"className":4296,"style":2211},[2051],[1807,4298,4299,4302],{"style":2266},[1807,4300],{"className":4301,"style":2060},[2059],[1807,4303,4305],{"className":4304},[2064,2065,2066,2067],[1807,4306,4308],{"className":4307},[1869,2067],[1807,4309,3831],{"className":4310,"style":3663},[1869,2033,2067],[1807,4312,2088],{"className":4313},[2087],[1807,4315,4317],{"className":4316},[2047],[1807,4318,4320],{"className":4319,"style":3841},[2051],[1807,4321],{}," 可包含：",[3450,4324,4325,4328,4331],{},[1898,4326,4327],{},"选项特征：票价、时间、可靠性；",[1898,4329,4330],{},"个体特征与选项的交互：收入 × 汽车；",[1898,4332,4333],{},"备选项常数：未测平均偏好。",[1796,4335,4336],{},"所有效用都加同一常数不会改变选择，因此必须设基准选项；误差尺度也需要归一化。",[1888,4338,4340],{"id":4339},"_5-iia红公交与蓝公交","5. IIA：红公交与蓝公交",[1796,4342,4343],{},"标准多项 Logit 的任意两项概率比",[1807,4345,4347],{"className":4346},[1920],[1807,4348,4350,4416],{"className":4349},[1810],[1807,4351,4353],{"className":4352},[1814],[1816,4354,4355],{"xmlns":1818,"display":1929},[1820,4356,4357,4413],{},[1823,4358,4359,4381,4383,4385,4387,4389,4399,4401,4411],{},[2483,4360,4361,4371],{},[1969,4362,4363,4365],{},[1938,4364,2435],{},[1823,4366,4367,4369],{},[1938,4368,1943],{},[1938,4370,3647],{},[1969,4372,4373,4375],{},[1938,4374,2435],{},[1823,4376,4377,4379],{},[1938,4378,1943],{},[1938,4380,3017],{},[1826,4382,1949],{},[1938,4384,2489],{},[1826,4386,2492],{},[1826,4388,2438],{"stretchy":1828},[1969,4390,4391,4393],{},[1938,4392,3697],{},[1823,4394,4395,4397],{},[1938,4396,1943],{},[1938,4398,3647],{},[1826,4400,3144],{},[1969,4402,4403,4405],{},[1938,4404,3697],{},[1823,4406,4407,4409],{},[1938,4408,1943],{},[1938,4410,3017],{},[1826,4412,2460],{"stretchy":1828},[1845,4414,4415],{"encoding":1847},"\\frac{P_{ij}}{P_{ik}}=\\exp(V_{ij}-V_{ik})",[1807,4417,4419,4578,4643],{"className":4418,"ariaHidden":1836},[1852],[1807,4420,4422,4426,4569,4572,4575],{"className":4421},[1856],[1807,4423],{"className":4424,"style":4425},[1860],"height:2.1963em;vertical-align:-0.836em;",[1807,4427,4429,4432,4566],{"className":4428},[1869],[1807,4430],{"className":4431},[1865,2773],[1807,4433,4435],{"className":4434},[2483],[1807,4436,4438,4558],{"className":4437},[2042,2043],[1807,4439,4441,4555],{"className":4440},[2047],[1807,4442,4445,4496,4504],{"className":4443,"style":4444},[2051],"height:1.3603em;",[1807,4446,4447,4450],{"style":2789},[1807,4448],{"className":4449,"style":2793},[2059],[1807,4451,4453],{"className":4452},[1869],[1807,4454,4456,4459],{"className":4455},[1869],[1807,4457,2435],{"className":4458,"style":2548},[1869,2033],[1807,4460,4462],{"className":4461},[2038],[1807,4463,4465,4488],{"className":4464},[2042,2043],[1807,4466,4468,4485],{"className":4467},[2047],[1807,4469,4471],{"className":4470,"style":3049},[2051],[1807,4472,4473,4476],{"style":3989},[1807,4474],{"className":4475,"style":2060},[2059],[1807,4477,4479],{"className":4478},[2064,2065,2066,2067],[1807,4480,4482],{"className":4481},[1869,2067],[1807,4483,4142],{"className":4484,"style":3061},[1869,2033,2067],[1807,4486,2088],{"className":4487},[2087],[1807,4489,4491],{"className":4490},[2047],[1807,4492,4494],{"className":4493,"style":2233},[2051],[1807,4495],{},[1807,4497,4498,4501],{"style":2882},[1807,4499],{"className":4500,"style":2793},[2059],[1807,4502],{"className":4503,"style":2890},[2889],[1807,4505,4506,4509],{"style":2893},[1807,4507],{"className":4508,"style":2793},[2059],[1807,4510,4512],{"className":4511},[1869],[1807,4513,4515,4518],{"className":4514},[1869],[1807,4516,2435],{"className":4517,"style":2548},[1869,2033],[1807,4519,4521],{"className":4520},[2038],[1807,4522,4524,4547],{"className":4523},[2042,2043],[1807,4525,4527,4544],{"className":4526},[2047],[1807,4528,4530],{"className":4529,"style":2211},[2051],[1807,4531,4532,4535],{"style":3989},[1807,4533],{"className":4534,"style":2060},[2059],[1807,4536,4538],{"className":4537},[2064,2065,2066,2067],[1807,4539,4541],{"className":4540},[1869,2067],[1807,4542,3831],{"className":4543,"style":3663},[1869,2033,2067],[1807,4545,2088],{"className":4546},[2087],[1807,4548,4550],{"className":4549},[2047],[1807,4551,4553],{"className":4552,"style":3841},[2051],[1807,4554],{},[1807,4556,2088],{"className":4557},[2087],[1807,4559,4561],{"className":4560},[2047],[1807,4562,4564],{"className":4563,"style":3305},[2051],[1807,4565],{},[1807,4567],{"className":4568},[1885,2773],[1807,4570],{"className":4571,"style":2101},[1877],[1807,4573,1949],{"className":4574},[2105],[1807,4576],{"className":4577,"style":2101},[1877],[1807,4579,4581,4585,4588,4591,4634,4637,4640],{"className":4580},[1856],[1807,4582],{"className":4583,"style":4584},[1860],"height:1.0361em;vertical-align:-0.2861em;",[1807,4586,2489],{"className":4587},[2812],[1807,4589,2438],{"className":4590},[1865],[1807,4592,4594,4597],{"className":4593},[1869],[1807,4595,3697],{"className":4596,"style":2034},[1869,2033],[1807,4598,4600],{"className":4599},[2038],[1807,4601,4603,4626],{"className":4602},[2042,2043],[1807,4604,4606,4623],{"className":4605},[2047],[1807,4607,4609],{"className":4608,"style":2211},[2051],[1807,4610,4611,4614],{"style":2266},[1807,4612],{"className":4613,"style":2060},[2059],[1807,4615,4617],{"className":4616},[2064,2065,2066,2067],[1807,4618,4620],{"className":4619},[1869,2067],[1807,4621,3831],{"className":4622,"style":3663},[1869,2033,2067],[1807,4624,2088],{"className":4625},[2087],[1807,4627,4629],{"className":4628},[2047],[1807,4630,4632],{"className":4631,"style":3841},[2051],[1807,4633],{},[1807,4635],{"className":4636,"style":2034},[1877],[1807,4638,3144],{"className":4639},[2083],[1807,4641],{"className":4642,"style":2034},[1877],[1807,4644,4646,4649,4692],{"className":4645},[1856],[1807,4647],{"className":4648,"style":1861},[1860],[1807,4650,4652,4655],{"className":4651},[1869],[1807,4653,3697],{"className":4654,"style":2034},[1869,2033],[1807,4656,4658],{"className":4657},[2038],[1807,4659,4661,4684],{"className":4660},[2042,2043],[1807,4662,4664,4681],{"className":4663},[2047],[1807,4665,4667],{"className":4666,"style":3049},[2051],[1807,4668,4669,4672],{"style":2266},[1807,4670],{"className":4671,"style":2060},[2059],[1807,4673,4675],{"className":4674},[2064,2065,2066,2067],[1807,4676,4678],{"className":4677},[1869,2067],[1807,4679,4142],{"className":4680,"style":3061},[1869,2033,2067],[1807,4682,2088],{"className":4683},[2087],[1807,4685,4687],{"className":4686},[2047],[1807,4688,4690],{"className":4689,"style":2233},[2051],[1807,4691],{},[1807,4693,2460],{"className":4694},[1885],[1796,4696,4697],{},"不依赖其他选项。若新增一条几乎相同的“蓝公交”线路，模型可能按比例从汽车和原公交吸走乘客，而现实中替代主要发生在两条公交之间。",[1796,4699,4700],{},"可选扩展：",[3450,4702,4703,4706,4709,4712],{},[1898,4704,4705],{},"nested Logit：把相似选项放入同一巢；",[1898,4707,4708],{},"mixed Logit：允许随机偏好并产生灵活替代；",[1898,4710,4711],{},"conditional Logit：强调选项层属性；",[1898,4713,4714],{},"panel choice：处理同一个人的重复选择。",[1888,4716,4718],{"id":4717},"_6-结构与因果边界","6. 结构与因果边界",[1796,4720,4721],{},"票价系数不自动是需求因果效应。运营商可能在高需求线路定高价，未测服务质量同时影响价格与选择。需要：",[3450,4723,4724,4727,4730,4733],{},[1898,4725,4726],{},"随机价格实验；",[1898,4728,4729],{},"成本或规则型工具变量；",[1898,4731,4732],{},"清楚的供需结构；",[1898,4734,4735],{},"对可用选择集合与缺失选项的建模。",[1796,4737,4738],{},"政策反事实还要说明新票价是否改变拥挤、班次和长期居住选择。",[1888,4740,4742],{"id":4741},"_7-诊断与报告","7. 诊断与报告",[3450,4744,4745,4748,4751,4754,4757,4760,4763,4766],{},[1898,4746,4747],{},"0\u002F1 和类别编码是否正确，基准类是什么？",[1898,4749,4750],{},"是否存在完全或准完全分离？",[1898,4752,4753],{},"连续变量函数形式是否合理？",[1898,4755,4756],{},"报告的是概率、AME 还是优势比？",[1898,4758,4759],{},"选择集合是否对每个人真实可用？",[1898,4761,4762],{},"多项模型的替代模式是否符合制度逻辑？",[1898,4764,4765],{},"价格、时间和质量是否内生？",[1898,4767,4768],{},"样本外政策是否落在数据支持范围内？",[1888,4770,4771],{"id":4771},"课堂任务",[1796,4773,4774],{},"设计一个通勤方式选择模型：",[1895,4776,4777,4780,4783,4786,4789],{},[1898,4778,4779],{},"列出两个个体属性和三个选项属性；",[1898,4781,4782],{},"说明收入为何要与汽车选项交互；",[1898,4784,4785],{},"用红\u002F蓝公交说明 IIA 失败；",[1898,4787,4788],{},"指出票价内生的一条路径；",[1898,4790,4791],{},"把“地铁降价 10%”写成概率和福利反事实。",[1888,4793,4794],{"id":4794},"核心阅读",[3450,4796,4797,4811,4820],{},[1898,4798,4799,4800,4810],{},"Train, ",[4801,4802,4806],"a",{"href":4803,"rel":4804},"https:\u002F\u002Feml.berkeley.edu\u002Fbooks\u002Fchoice2.html",[4805],"nofollow",[4807,4808,4809],"em",{},"Discrete Choice Methods with Simulation","（开放教材）。",[1898,4812,4813,4814,4819],{},"McFadden (1974), ",[4801,4815,4818],{"href":4816,"rel":4817},"https:\u002F\u002Feml.berkeley.edu\u002Freprints\u002Fmcfadden\u002Fzarembka.pdf",[4805],"“Conditional Logit Analysis of Qualitative Choice Behavior”","。",[1898,4821,4822,4823,4819],{},"McFadden & Train (2000), ",[4801,4824,4827],{"href":4825,"rel":4826},"https:\u002F\u002Fdoi.org\u002F10.1002\u002F1099-1255(200009\u002F10)15:5%3C447::AID-JAE570%3E3.0.CO;2-1",[4805],"“Mixed MNL Models for Discrete Response”",[1796,4829,4830,4831,4835,4836,4819],{},"上一章：",[4801,4832,4834],{"href":4833},"..\u002F07-matching\u002F","匹配与倾向得分","｜下一章：",[4801,4837,4839],{"href":4838},"..\u002F09-count-limited\u002F","计数数据与受限因变量",{"title":10,"searchDepth":4841,"depth":4841,"links":4842},2,[4843,4844,4845,4846,4847,4848,4849,4850,4851,4852],{"id":1890,"depth":4841,"text":1890},{"id":1915,"depth":4841,"text":1916},{"id":2993,"depth":4841,"text":2994},{"id":3581,"depth":4841,"text":3582},{"id":3598,"depth":4841,"text":3599},{"id":4339,"depth":4841,"text":4340},{"id":4717,"depth":4841,"text":4718},{"id":4741,"depth":4841,"text":4742},{"id":4771,"depth":4841,"text":4771},{"id":4794,"depth":4841,"text":4794},"从随机效用出发理解 Logit、Probit、多项选择、边际效应、IIA 与政策反事实。","md",{"sidebar":4856},{"order":4857},8,true,{"title":1516,"description":4853},"uABVObKigcHoJuhfbTNRTvN7b3NgB6YegyzMlUUsr6M",[4862,4864],{"title":1510,"path":1511,"stem":1512,"description":4863,"children":-1},"围绕无混杂与重叠条件，掌握匹配、倾向得分加权、平衡诊断和双重稳健估计。",{"title":1522,"path":1523,"stem":1524,"description":4865,"children":-1},"按数据生成机制区分 Poisson、负二项、零膨胀、两部模型、删失、截断与选择模型。",1785754751966]