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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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Alpha 策略全流程","\u002Fzh\u002Fquant\u002F09-case-study","zh\u002Fquant\u002F09-case-study",{"title":1779,"path":1780,"stem":1781},"量化投资文献与软件图谱","\u002Fzh\u002Fquant\u002F10-reading-software-map","zh\u002Fquant\u002F10-reading-software-map",null,{"id":1784,"title":1522,"body":1785,"description":4152,"extension":4153,"features":1782,"hero":1782,"layout":1782,"locale":1782,"meta":4154,"navigation":1782,"path":1523,"published":4157,"seo":4158,"stem":1524,"__hash__":4159},"docs\u002Fzh\u002Fmicroeconometrics\u002F09-count-limited\u002Findex.md",{"type":1786,"value":1787,"toc":4133},"minimark",[1788,1792,1803,1806,1810,1813,1832,1836,2019,2022,2026,2405,2790,2793,2933,2936,2940,2943,2950,2953,2957,2961,2964,2968,3166,3169,3172,3175,3179,3182,3597,3630,3633,3654,3657,3661,3664,4011,4014,4018,4044,4047,4064,4067,4070,4087,4090,4121],[1789,1790,1522],"h1",{"id":1791},"第九章计数数据与受限因变量",[1793,1794,1795],"blockquote",{},[1796,1797,1798,1802],"p",{},[1799,1800,1801],"strong",{},"案例："," 一年就诊次数有大量 0；工作时长也有大量 0。两个“很多零”的结果，为什么不应自动使用同一个模型？",[1796,1804,1805],{},"模型选择应由零值和边界的生成机制决定，而不是由直方图长得像什么决定。",[1807,1808,1809],"h2",{"id":1809},"学习目标",[1796,1811,1812],{},"你应能：",[1814,1815,1816,1820,1823,1826,1829],"ol",{},[1817,1818,1819],"li",{},"区分计数、删失、截断、样本选择与角点解；",[1817,1821,1822],{},"解释 Poisson 条件均值和入射率比；",[1817,1824,1825],{},"诊断过度离散与结构零；",[1817,1827,1828],{},"判断何时使用负二项、hurdle、零膨胀、Tobit 或两部模型；",[1817,1830,1831],{},"把系数转换为条件均值和政策相关概率。",[1807,1833,1835],{"id":1834},"_1-先辨认数据机制","1. 先辨认数据机制",[1837,1838,1839,1855],"table",{},[1840,1841,1842],"thead",{},[1843,1844,1845,1849,1852],"tr",{},[1846,1847,1848],"th",{},"观察现象",[1846,1850,1851],{},"可能机制",[1846,1853,1854],{},"典型模型",[1856,1857,1858,1964,1975,1986,1997,2008],"tbody",{},[1843,1859,1860,1958,1961],{},[1861,1862,1863,1957],"td",{},[1864,1865,1868,1909],"span",{"className":1866},[1867],"katex",[1864,1869,1872],{"className":1870},[1871],"katex-mathml",[1873,1874,1876],"math",{"xmlns":1875},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML",[1877,1878,1879,1904],"semantics",{},[1880,1881,1882,1886,1891,1894,1896,1899,1901],"mrow",{},[1883,1884,1885],"mn",{},"0",[1887,1888,1890],"mo",{"separator":1889},"true",",",[1883,1892,1893],{},"1",[1887,1895,1890],{"separator":1889},[1883,1897,1898],{},"2",[1887,1900,1890],{"separator":1889},[1887,1902,1903],{},"…",[1905,1906,1908],"annotation",{"encoding":1907},"application\u002Fx-tex","0,1,2,\\ldots",[1864,1910,1913],{"className":1911,"ariaHidden":1889},[1912],"katex-html",[1864,1914,1917,1922,1926,1930,1935,1938,1941,1944,1947,1950,1953],{"className":1915},[1916],"base",[1864,1918],{"className":1919,"style":1921},[1920],"strut","height:0.8389em;vertical-align:-0.1944em;",[1864,1923,1885],{"className":1924},[1925],"mord",[1864,1927,1890],{"className":1928},[1929],"mpunct",[1864,1931],{"className":1932,"style":1934},[1933],"mspace","margin-right:0.1667em;",[1864,1936,1893],{"className":1937},[1925],[1864,1939,1890],{"className":1940},[1929],[1864,1942],{"className":1943,"style":1934},[1933],[1864,1945,1898],{"className":1946},[1925],[1864,1948,1890],{"className":1949},[1929],[1864,1951],{"className":1952,"style":1934},[1933],[1864,1954,1903],{"className":1955},[1956],"minner"," 次事件",[1861,1959,1960],{},"给定暴露期内发生次数",[1861,1962,1963],{},"Poisson、负二项",[1843,1965,1966,1969,1972],{},[1861,1967,1968],{},"一部分人必然为 0，其他人再计数",[1861,1970,1971],{},"两种潜在人群",[1861,1973,1974],{},"zero-inflated",[1843,1976,1977,1980,1983],{},[1861,1978,1979],{},"先决定是否发生，再决定正值大小",[1861,1981,1982],{},"两个决策过程",[1861,1984,1985],{},"hurdle \u002F two-part",[1843,1987,1988,1991,1994],{},[1861,1989,1990],{},"潜变量低于 0 都记为 0",[1861,1992,1993],{},"审查或测量边界",[1861,1995,1996],{},"Tobit",[1843,1998,1999,2002,2005],{},[1861,2000,2001],{},"低于阈值的个体根本不进入样本",[1861,2003,2004],{},"截断",[1861,2006,2007],{},"truncated likelihood",[1843,2009,2010,2013,2016],{},[1861,2011,2012],{},"结果只对就业者可见",[1861,2014,2015],{},"样本选择",[1861,2017,2018],{},"选择模型、界限或设计方法",[1796,2020,2021],{},"零工作时长可能是最优劳动供给的角点，不一定存在一个“负的潜在工作时长”被机械审查。此时 two-part 往往比 Tobit 的单一潜变量假设更透明。",[1807,2023,2025],{"id":2024},"_2-poisson-的核心是条件均值","2. Poisson 的核心是条件均值",[1864,2027,2030],{"className":2028},[2029],"katex-display",[1864,2031,2033,2118],{"className":2032},[1867],[1864,2034,2036],{"className":2035},[1871],[1873,2037,2039],{"xmlns":1875,"display":2038},"block",[1877,2040,2041,2115],{},[1880,2042,2043,2047,2051,2060,2063,2070,2073,2076,2083,2085,2088,2091,2094,2106,2109,2112],{},[2044,2045,2046],"mi",{},"E",[1887,2048,2050],{"stretchy":2049},"false","[",[2052,2053,2054,2057],"msub",{},[2044,2055,2056],{},"Y",[2044,2058,2059],{},"i",[1887,2061,2062],{},"∣",[2052,2064,2065,2068],{},[2044,2066,2067],{},"X",[2044,2069,2059],{},[1887,2071,2072],{"stretchy":2049},"]",[1887,2074,2075],{},"=",[2052,2077,2078,2081],{},[2044,2079,2080],{},"μ",[2044,2082,2059],{},[1887,2084,2075],{},[2044,2086,2087],{},"exp",[1887,2089,2090],{},"⁡",[1887,2092,2093],{"stretchy":2049},"(",[2095,2096,2097,2099,2101],"msubsup",{},[2044,2098,2067],{},[2044,2100,2059],{},[1887,2102,2105],{"mathvariant":2103,"lspace":2104,"rspace":2104},"normal","0em","′",[2044,2107,2108],{},"β",[1887,2110,2111],{"stretchy":2049},")",[2044,2113,2114],{"mathvariant":2103},".",[1905,2116,2117],{"encoding":1907},"E[Y_i\\mid X_i]=\\mu_i=\\exp(X_i'\\beta).",[1864,2119,2121,2205,2266,2323],{"className":2120,"ariaHidden":1889},[1912],[1864,2122,2124,2128,2133,2137,2194,2198,2202],{"className":2123},[1916],[1864,2125],{"className":2126,"style":2127},[1920],"height:1em;vertical-align:-0.25em;",[1864,2129,2046],{"className":2130,"style":2132},[1925,2131],"mathnormal","margin-right:0.0576em;",[1864,2134,2050],{"className":2135},[2136],"mopen",[1864,2138,2140,2144],{"className":2139},[1925],[1864,2141,2056],{"className":2142,"style":2143},[1925,2131],"margin-right:0.2222em;",[1864,2145,2148],{"className":2146},[2147],"msupsub",[1864,2149,2153,2185],{"className":2150},[2151,2152],"vlist-t","vlist-t2",[1864,2154,2157,2180],{"className":2155},[2156],"vlist-r",[1864,2158,2162],{"className":2159,"style":2161},[2160],"vlist","height:0.3117em;",[1864,2163,2165,2170],{"style":2164},"top:-2.55em;margin-left:-0.2222em;margin-right:0.05em;",[1864,2166],{"className":2167,"style":2169},[2168],"pstrut","height:2.7em;",[1864,2171,2177],{"className":2172},[2173,2174,2175,2176],"sizing","reset-size6","size3","mtight",[1864,2178,2059],{"className":2179},[1925,2131,2176],[1864,2181,2184],{"className":2182},[2183],"vlist-s","​",[1864,2186,2188],{"className":2187},[2156],[1864,2189,2192],{"className":2190,"style":2191},[2160],"height:0.15em;",[1864,2193],{},[1864,2195],{"className":2196,"style":2197},[1933],"margin-right:0.2778em;",[1864,2199,2062],{"className":2200},[2201],"mrel",[1864,2203],{"className":2204,"style":2197},[1933],[1864,2206,2208,2211,2253,2257,2260,2263],{"className":2207},[1916],[1864,2209],{"className":2210,"style":2127},[1920],[1864,2212,2214,2218],{"className":2213},[1925],[1864,2215,2067],{"className":2216,"style":2217},[1925,2131],"margin-right:0.0785em;",[1864,2219,2221],{"className":2220},[2147],[1864,2222,2224,2245],{"className":2223},[2151,2152],[1864,2225,2227,2242],{"className":2226},[2156],[1864,2228,2230],{"className":2229,"style":2161},[2160],[1864,2231,2233,2236],{"style":2232},"top:-2.55em;margin-left:-0.0785em;margin-right:0.05em;",[1864,2234],{"className":2235,"style":2169},[2168],[1864,2237,2239],{"className":2238},[2173,2174,2175,2176],[1864,2240,2059],{"className":2241},[1925,2131,2176],[1864,2243,2184],{"className":2244},[2183],[1864,2246,2248],{"className":2247},[2156],[1864,2249,2251],{"className":2250,"style":2191},[2160],[1864,2252],{},[1864,2254,2072],{"className":2255},[2256],"mclose",[1864,2258],{"className":2259,"style":2197},[1933],[1864,2261,2075],{"className":2262},[2201],[1864,2264],{"className":2265,"style":2197},[1933],[1864,2267,2269,2273,2314,2317,2320],{"className":2268},[1916],[1864,2270],{"className":2271,"style":2272},[1920],"height:0.625em;vertical-align:-0.1944em;",[1864,2274,2276,2279],{"className":2275},[1925],[1864,2277,2080],{"className":2278},[1925,2131],[1864,2280,2282],{"className":2281},[2147],[1864,2283,2285,2306],{"className":2284},[2151,2152],[1864,2286,2288,2303],{"className":2287},[2156],[1864,2289,2291],{"className":2290,"style":2161},[2160],[1864,2292,2294,2297],{"style":2293},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[1864,2295],{"className":2296,"style":2169},[2168],[1864,2298,2300],{"className":2299},[2173,2174,2175,2176],[1864,2301,2059],{"className":2302},[1925,2131,2176],[1864,2304,2184],{"className":2305},[2183],[1864,2307,2309],{"className":2308},[2156],[1864,2310,2312],{"className":2311,"style":2191},[2160],[1864,2313],{},[1864,2315],{"className":2316,"style":2197},[1933],[1864,2318,2075],{"className":2319},[2201],[1864,2321],{"className":2322,"style":2197},[1933],[1864,2324,2326,2330,2334,2337,2395,2399,2402],{"className":2325},[1916],[1864,2327],{"className":2328,"style":2329},[1920],"height:1.0519em;vertical-align:-0.25em;",[1864,2331,2087],{"className":2332},[2333],"mop",[1864,2335,2093],{"className":2336},[2136],[1864,2338,2340,2343],{"className":2339},[1925],[1864,2341,2067],{"className":2342,"style":2217},[1925,2131],[1864,2344,2346],{"className":2345},[2147],[1864,2347,2349,2386],{"className":2348},[2151,2152],[1864,2350,2352,2383],{"className":2351},[2156],[1864,2353,2356,2368],{"className":2354,"style":2355},[2160],"height:0.8019em;",[1864,2357,2359,2362],{"style":2358},"top:-2.453em;margin-left:-0.0785em;margin-right:0.05em;",[1864,2360],{"className":2361,"style":2169},[2168],[1864,2363,2365],{"className":2364},[2173,2174,2175,2176],[1864,2366,2059],{"className":2367},[1925,2131,2176],[1864,2369,2371,2374],{"style":2370},"top:-3.113em;margin-right:0.05em;",[1864,2372],{"className":2373,"style":2169},[2168],[1864,2375,2377],{"className":2376},[2173,2174,2175,2176],[1864,2378,2380],{"className":2379},[1925,2176],[1864,2381,2105],{"className":2382},[1925,2176],[1864,2384,2184],{"className":2385},[2183],[1864,2387,2389],{"className":2388},[2156],[1864,2390,2393],{"className":2391,"style":2392},[2160],"height:0.247em;",[1864,2394],{},[1864,2396,2108],{"className":2397,"style":2398},[1925,2131],"margin-right:0.0528em;",[1864,2400,2111],{"className":2401},[2256],[1864,2403,2114],{"className":2404},[1925],[1796,2406,2407,2408,2483,2484,2556,2557,2644,2645,2698,2699,2789],{},"对连续变量 ",[1864,2409,2411,2431],{"className":2410},[1867],[1864,2412,2414],{"className":2413},[1871],[1873,2415,2416],{"xmlns":1875},[1877,2417,2418,2428],{},[1880,2419,2420],{},[2052,2421,2422,2425],{},[2044,2423,2424],{},"x",[2044,2426,2427],{},"k",[1905,2429,2430],{"encoding":1907},"x_k",[1864,2432,2434],{"className":2433,"ariaHidden":1889},[1912],[1864,2435,2437,2441],{"className":2436},[1916],[1864,2438],{"className":2439,"style":2440},[1920],"height:0.5806em;vertical-align:-0.15em;",[1864,2442,2444,2447],{"className":2443},[1925],[1864,2445,2424],{"className":2446},[1925,2131],[1864,2448,2450],{"className":2449},[2147],[1864,2451,2453,2475],{"className":2452},[2151,2152],[1864,2454,2456,2472],{"className":2455},[2156],[1864,2457,2460],{"className":2458,"style":2459},[2160],"height:0.3361em;",[1864,2461,2462,2465],{"style":2293},[1864,2463],{"className":2464,"style":2169},[2168],[1864,2466,2468],{"className":2467},[2173,2174,2175,2176],[1864,2469,2427],{"className":2470,"style":2471},[1925,2131,2176],"margin-right:0.0315em;",[1864,2473,2184],{"className":2474},[2183],[1864,2476,2478],{"className":2477},[2156],[1864,2479,2481],{"className":2480,"style":2191},[2160],[1864,2482],{},"，",[1864,2485,2487,2505],{"className":2486},[1867],[1864,2488,2490],{"className":2489},[1871],[1873,2491,2492],{"xmlns":1875},[1877,2493,2494,2502],{},[1880,2495,2496],{},[2052,2497,2498,2500],{},[2044,2499,2108],{},[2044,2501,2427],{},[1905,2503,2504],{"encoding":1907},"\\beta_k",[1864,2506,2508],{"className":2507,"ariaHidden":1889},[1912],[1864,2509,2511,2515],{"className":2510},[1916],[1864,2512],{"className":2513,"style":2514},[1920],"height:0.8889em;vertical-align:-0.1944em;",[1864,2516,2518,2521],{"className":2517},[1925],[1864,2519,2108],{"className":2520,"style":2398},[1925,2131],[1864,2522,2524],{"className":2523},[2147],[1864,2525,2527,2548],{"className":2526},[2151,2152],[1864,2528,2530,2545],{"className":2529},[2156],[1864,2531,2533],{"className":2532,"style":2459},[2160],[1864,2534,2536,2539],{"style":2535},"top:-2.55em;margin-left:-0.0528em;margin-right:0.05em;",[1864,2537],{"className":2538,"style":2169},[2168],[1864,2540,2542],{"className":2541},[2173,2174,2175,2176],[1864,2543,2427],{"className":2544,"style":2471},[1925,2131,2176],[1864,2546,2184],{"className":2547},[2183],[1864,2549,2551],{"className":2550},[2156],[1864,2552,2554],{"className":2553,"style":2191},[2160],[1864,2555],{}," 是对数条件均值的半弹性；",[1864,2558,2560,2586],{"className":2559},[1867],[1864,2561,2563],{"className":2562},[1871],[1873,2564,2565],{"xmlns":1875},[1877,2566,2567,2583],{},[1880,2568,2569,2571,2573,2575,2581],{},[2044,2570,2087],{},[1887,2572,2090],{},[1887,2574,2093],{"stretchy":2049},[2052,2576,2577,2579],{},[2044,2578,2108],{},[2044,2580,2427],{},[1887,2582,2111],{"stretchy":2049},[1905,2584,2585],{"encoding":1907},"\\exp(\\beta_k)",[1864,2587,2589],{"className":2588,"ariaHidden":1889},[1912],[1864,2590,2592,2595,2598,2601,2641],{"className":2591},[1916],[1864,2593],{"className":2594,"style":2127},[1920],[1864,2596,2087],{"className":2597},[2333],[1864,2599,2093],{"className":2600},[2136],[1864,2602,2604,2607],{"className":2603},[1925],[1864,2605,2108],{"className":2606,"style":2398},[1925,2131],[1864,2608,2610],{"className":2609},[2147],[1864,2611,2613,2633],{"className":2612},[2151,2152],[1864,2614,2616,2630],{"className":2615},[2156],[1864,2617,2619],{"className":2618,"style":2459},[2160],[1864,2620,2621,2624],{"style":2535},[1864,2622],{"className":2623,"style":2169},[2168],[1864,2625,2627],{"className":2626},[2173,2174,2175,2176],[1864,2628,2427],{"className":2629,"style":2471},[1925,2131,2176],[1864,2631,2184],{"className":2632},[2183],[1864,2634,2636],{"className":2635},[2156],[1864,2637,2639],{"className":2638,"style":2191},[2160],[1864,2640],{},[1864,2642,2111],{"className":2643},[2256]," 是入射率比（IRR）。例如 ",[1864,2646,2648,2667],{"className":2647},[1867],[1864,2649,2651],{"className":2650},[1871],[1873,2652,2653],{"xmlns":1875},[1877,2654,2655,2664],{},[1880,2656,2657,2659,2661],{},[2044,2658,2108],{},[1887,2660,2075],{},[1883,2662,2663],{},"0.2",[1905,2665,2666],{"encoding":1907},"\\beta=0.2",[1864,2668,2670,2688],{"className":2669,"ariaHidden":1889},[1912],[1864,2671,2673,2676,2679,2682,2685],{"className":2672},[1916],[1864,2674],{"className":2675,"style":2514},[1920],[1864,2677,2108],{"className":2678,"style":2398},[1925,2131],[1864,2680],{"className":2681,"style":2197},[1933],[1864,2683,2075],{"className":2684},[2201],[1864,2686],{"className":2687,"style":2197},[1933],[1864,2689,2691,2695],{"className":2690},[1916],[1864,2692],{"className":2693,"style":2694},[1920],"height:0.6444em;",[1864,2696,2663],{"className":2697},[1925]," 对应条件均值乘以 ",[1864,2700,2702,2728],{"className":2701},[1867],[1864,2703,2705],{"className":2704},[1871],[1873,2706,2707],{"xmlns":1875},[1877,2708,2709,2725],{},[1880,2710,2711,2719,2722],{},[2712,2713,2714,2717],"msup",{},[2044,2715,2716],{},"e",[1883,2718,2663],{},[1887,2720,2721],{},"≈",[1883,2723,2724],{},"1.22",[1905,2726,2727],{"encoding":1907},"e^{0.2}\\approx1.22",[1864,2729,2731,2780],{"className":2730,"ariaHidden":1889},[1912],[1864,2732,2734,2738,2771,2774,2777],{"className":2733},[1916],[1864,2735],{"className":2736,"style":2737},[1920],"height:0.8141em;",[1864,2739,2741,2744],{"className":2740},[1925],[1864,2742,2716],{"className":2743},[1925,2131],[1864,2745,2747],{"className":2746},[2147],[1864,2748,2750],{"className":2749},[2151],[1864,2751,2753],{"className":2752},[2156],[1864,2754,2756],{"className":2755,"style":2737},[2160],[1864,2757,2759,2762],{"style":2758},"top:-3.063em;margin-right:0.05em;",[1864,2760],{"className":2761,"style":2169},[2168],[1864,2763,2765],{"className":2764},[2173,2174,2175,2176],[1864,2766,2768],{"className":2767},[1925,2176],[1864,2769,2663],{"className":2770},[1925,2176],[1864,2772],{"className":2773,"style":2197},[1933],[1864,2775,2721],{"className":2776},[2201],[1864,2778],{"className":2779,"style":2197},[1933],[1864,2781,2783,2786],{"className":2782},[1916],[1864,2784],{"className":2785,"style":2694},[1920],[1864,2787,2724],{"className":2788},[1925],"。",[1796,2791,2792],{},"完整 Poisson 分布要求",[1864,2794,2796],{"className":2795},[2029],[1864,2797,2799,2842],{"className":2798},[1867],[1864,2800,2802],{"className":2801},[1871],[1873,2803,2804],{"xmlns":1875,"display":2038},[1877,2805,2806,2839],{},[1880,2807,2808,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837],{},[2044,2809,2810],{"mathvariant":2103},"Var",[1887,2812,2090],{},[1887,2814,2093],{"stretchy":2049},[2044,2816,2056],{},[1887,2818,2062],{},[2044,2820,2067],{},[1887,2822,2111],{"stretchy":2049},[1887,2824,2075],{},[2044,2826,2046],{},[1887,2828,2050],{"stretchy":2049},[2044,2830,2056],{},[1887,2832,2062],{},[2044,2834,2067],{},[1887,2836,2072],{"stretchy":2049},[1887,2838,1890],{"separator":1889},[1905,2840,2841],{"encoding":1907},"\\operatorname{Var}(Y\\mid X)=E[Y\\mid X],",[1864,2843,2845,2873,2894,2918],{"className":2844,"ariaHidden":1889},[1912],[1864,2846,2848,2851,2858,2861,2864,2867,2870],{"className":2847},[1916],[1864,2849],{"className":2850,"style":2127},[1920],[1864,2852,2854],{"className":2853},[2333],[1864,2855,2810],{"className":2856},[1925,2857],"mathrm",[1864,2859,2093],{"className":2860},[2136],[1864,2862,2056],{"className":2863,"style":2143},[1925,2131],[1864,2865],{"className":2866,"style":2197},[1933],[1864,2868,2062],{"className":2869},[2201],[1864,2871],{"className":2872,"style":2197},[1933],[1864,2874,2876,2879,2882,2885,2888,2891],{"className":2875},[1916],[1864,2877],{"className":2878,"style":2127},[1920],[1864,2880,2067],{"className":2881,"style":2217},[1925,2131],[1864,2883,2111],{"className":2884},[2256],[1864,2886],{"className":2887,"style":2197},[1933],[1864,2889,2075],{"className":2890},[2201],[1864,2892],{"className":2893,"style":2197},[1933],[1864,2895,2897,2900,2903,2906,2909,2912,2915],{"className":2896},[1916],[1864,2898],{"className":2899,"style":2127},[1920],[1864,2901,2046],{"className":2902,"style":2132},[1925,2131],[1864,2904,2050],{"className":2905},[2136],[1864,2907,2056],{"className":2908,"style":2143},[1925,2131],[1864,2910],{"className":2911,"style":2197},[1933],[1864,2913,2062],{"className":2914},[2201],[1864,2916],{"className":2917,"style":2197},[1933],[1864,2919,2921,2924,2927,2930],{"className":2920},[1916],[1864,2922],{"className":2923,"style":2127},[1920],[1864,2925,2067],{"className":2926,"style":2217},[1925,2131],[1864,2928,2072],{"className":2929},[2256],[1864,2931,1890],{"className":2932},[1929],[1796,2934,2935],{},"但 Poisson QMLE 在条件均值正确时，即使方差不等于均值也可一致；此时必须使用稳健方差。负二项模型进一步参数化过度离散。",[1807,2937,2939],{"id":2938},"_3-可运行案例过度离散下的-poisson-qmle","3. 可运行案例：过度离散下的 Poisson QMLE",[1796,2941,2942],{},"模拟中未观测但与协变量独立的 frailty 使方差远大于均值，条件均值仍正确。Poisson 系数接近真值，但模型式标准误会失真，实际分析应使用 sandwich\u002Fcluster-robust 推断。",[2944,2945],"pyodide",{"code64":2946,"layout":2947,"locale":7,"packages":2948,"title":2949},"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","vertical","numpy","Python：Poisson IRLS、过度离散与 IRR",[1796,2951,2952],{},"“方差大于均值”提示 Poisson 分布不合适，却不自动否定其条件均值估计。应分别审查均值设定与推断。",[1807,2954,2956],{"id":2955},"_4-大量零三种不同解释","4. 大量零：三种不同解释",[2958,2959,2960],"h3",{"id":2960},"普通计数过程",[1796,2962,2963],{},"低事件率本来就会产生许多零；Poisson 可能已足够。",[2958,2965,2967],{"id":2966},"hurdle-two-part","Hurdle \u002F two-part",[1796,2969,2970,2971,3062,3063,3165],{},"先建模 ",[1864,2972,2974,3004],{"className":2973},[1867],[1864,2975,2977],{"className":2976},[1871],[1873,2978,2979],{"xmlns":1875},[1877,2980,2981,3001],{},[1880,2982,2983,2986,2988,2990,2993,2995,2997,2999],{},[2044,2984,2985],{},"P",[1887,2987,2093],{"stretchy":2049},[2044,2989,2056],{},[1887,2991,2992],{},">",[1883,2994,1885],{},[1887,2996,2062],{},[2044,2998,2067],{},[1887,3000,2111],{"stretchy":2049},[1905,3002,3003],{"encoding":1907},"P(Y>0\\mid X)",[1864,3005,3007,3032,3050],{"className":3006,"ariaHidden":1889},[1912],[1864,3008,3010,3013,3017,3020,3023,3026,3029],{"className":3009},[1916],[1864,3011],{"className":3012,"style":2127},[1920],[1864,3014,2985],{"className":3015,"style":3016},[1925,2131],"margin-right:0.1389em;",[1864,3018,2093],{"className":3019},[2136],[1864,3021,2056],{"className":3022,"style":2143},[1925,2131],[1864,3024],{"className":3025,"style":2197},[1933],[1864,3027,2992],{"className":3028},[2201],[1864,3030],{"className":3031,"style":2197},[1933],[1864,3033,3035,3038,3041,3044,3047],{"className":3034},[1916],[1864,3036],{"className":3037,"style":2127},[1920],[1864,3039,1885],{"className":3040},[1925],[1864,3042],{"className":3043,"style":2197},[1933],[1864,3045,2062],{"className":3046},[2201],[1864,3048],{"className":3049,"style":2197},[1933],[1864,3051,3053,3056,3059],{"className":3052},[1916],[1864,3054],{"className":3055,"style":2127},[1920],[1864,3057,2067],{"className":3058,"style":2217},[1925,2131],[1864,3060,2111],{"className":3061},[2256],"，再建模 ",[1864,3064,3066,3098],{"className":3065},[1867],[1864,3067,3069],{"className":3068},[1871],[1873,3070,3071],{"xmlns":1875},[1877,3072,3073,3095],{},[1880,3074,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093],{},[2044,3076,2046],{},[1887,3078,2050],{"stretchy":2049},[2044,3080,2056],{},[1887,3082,2062],{},[2044,3084,2056],{},[1887,3086,2992],{},[1883,3088,1885],{},[1887,3090,1890],{"separator":1889},[2044,3092,2067],{},[1887,3094,2072],{"stretchy":2049},[1905,3096,3097],{"encoding":1907},"E[Y\\mid Y>0,X]",[1864,3099,3101,3125,3144],{"className":3100,"ariaHidden":1889},[1912],[1864,3102,3104,3107,3110,3113,3116,3119,3122],{"className":3103},[1916],[1864,3105],{"className":3106,"style":2127},[1920],[1864,3108,2046],{"className":3109,"style":2132},[1925,2131],[1864,3111,2050],{"className":3112},[2136],[1864,3114,2056],{"className":3115,"style":2143},[1925,2131],[1864,3117],{"className":3118,"style":2197},[1933],[1864,3120,2062],{"className":3121},[2201],[1864,3123],{"className":3124,"style":2197},[1933],[1864,3126,3128,3132,3135,3138,3141],{"className":3127},[1916],[1864,3129],{"className":3130,"style":3131},[1920],"height:0.7224em;vertical-align:-0.0391em;",[1864,3133,2056],{"className":3134,"style":2143},[1925,2131],[1864,3136],{"className":3137,"style":2197},[1933],[1864,3139,2992],{"className":3140},[2201],[1864,3142],{"className":3143,"style":2197},[1933],[1864,3145,3147,3150,3153,3156,3159,3162],{"className":3146},[1916],[1864,3148],{"className":3149,"style":2127},[1920],[1864,3151,1885],{"className":3152},[1925],[1864,3154,1890],{"className":3155},[1929],[1864,3157],{"className":3158,"style":1934},[1933],[1864,3160,2067],{"className":3161,"style":2217},[1925,2131],[1864,3163,2072],{"className":3164},[2256],"。适合“是否使用服务”与“使用多少”由不同决策控制的场景。",[2958,3167,3168],{"id":1974},"Zero-inflated",[1796,3170,3171],{},"假设存在结构零类与普通计数类。它是强潜类别假设，需要实质机制支持；仅因为零很多而采用会过度解释。",[1796,3173,3174],{},"非嵌套模型选择指标只能比较拟合，不能证明潜在机制。",[1807,3176,3178],{"id":3177},"_5-tobit截断与选择","5. Tobit、截断与选择",[1796,3180,3181],{},"Tobit I 型模型：",[1864,3183,3185],{"className":3184},[2029],[1864,3186,3188,3267],{"className":3187},[1867],[1864,3189,3191],{"className":3190},[1871],[1873,3192,3193],{"xmlns":1875,"display":2038},[1877,3194,3195,3264],{},[1880,3196,3197,3206,3208,3216,3218,3221,3228,3230,3233,3239,3241,3244,3246,3248,3250,3252,3260,3262],{},[2095,3198,3199,3201,3203],{},[2044,3200,2056],{},[2044,3202,2059],{},[1887,3204,3205],{},"∗",[1887,3207,2075],{},[2095,3209,3210,3212,3214],{},[2044,3211,2067],{},[2044,3213,2059],{},[1887,3215,2105],{"mathvariant":2103,"lspace":2104,"rspace":2104},[2044,3217,2108],{},[1887,3219,3220],{},"+",[2052,3222,3223,3226],{},[2044,3224,3225],{},"ε",[2044,3227,2059],{},[1887,3229,1890],{"separator":1889},[1933,3231],{"width":3232},"2em",[2052,3234,3235,3237],{},[2044,3236,2056],{},[2044,3238,2059],{},[1887,3240,2075],{},[2044,3242,3243],{},"max",[1887,3245,2090],{},[1887,3247,2093],{"stretchy":2049},[1883,3249,1885],{},[1887,3251,1890],{"separator":1889},[2095,3253,3254,3256,3258],{},[2044,3255,2056],{},[2044,3257,2059],{},[1887,3259,3205],{},[1887,3261,2111],{"stretchy":2049},[2044,3263,2114],{"mathvariant":2103},[1905,3265,3266],{"encoding":1907},"Y_i^*=X_i'\\beta+\\varepsilon_i,\\qquad\nY_i=\\max(0,Y_i^*).",[1864,3268,3270,3340,3413,3519],{"className":3269,"ariaHidden":1889},[1912],[1864,3271,3273,3277,3331,3334,3337],{"className":3272},[1916],[1864,3274],{"className":3275,"style":3276},[1920],"height:0.9857em;vertical-align:-0.247em;",[1864,3278,3280,3283],{"className":3279},[1925],[1864,3281,2056],{"className":3282,"style":2143},[1925,2131],[1864,3284,3286],{"className":3285},[2147],[1864,3287,3289,3323],{"className":3288},[2151,2152],[1864,3290,3292,3320],{"className":3291},[2156],[1864,3293,3296,3308],{"className":3294,"style":3295},[2160],"height:0.7387em;",[1864,3297,3299,3302],{"style":3298},"top:-2.453em;margin-left:-0.2222em;margin-right:0.05em;",[1864,3300],{"className":3301,"style":2169},[2168],[1864,3303,3305],{"className":3304},[2173,2174,2175,2176],[1864,3306,2059],{"className":3307},[1925,2131,2176],[1864,3309,3310,3313],{"style":2370},[1864,3311],{"className":3312,"style":2169},[2168],[1864,3314,3316],{"className":3315},[2173,2174,2175,2176],[1864,3317,3205],{"className":3318},[3319,2176],"mbin",[1864,3321,2184],{"className":3322},[2183],[1864,3324,3326],{"className":3325},[2156],[1864,3327,3329],{"className":3328,"style":2392},[2160],[1864,3330],{},[1864,3332],{"className":3333,"style":2197},[1933],[1864,3335,2075],{"className":3336},[2201],[1864,3338],{"className":3339,"style":2197},[1933],[1864,3341,3343,3347,3401,3404,3407,3410],{"className":3342},[1916],[1864,3344],{"className":3345,"style":3346},[1920],"height:1.0489em;vertical-align:-0.247em;",[1864,3348,3350,3353],{"className":3349},[1925],[1864,3351,2067],{"className":3352,"style":2217},[1925,2131],[1864,3354,3356],{"className":3355},[2147],[1864,3357,3359,3393],{"className":3358},[2151,2152],[1864,3360,3362,3390],{"className":3361},[2156],[1864,3363,3365,3376],{"className":3364,"style":2355},[2160],[1864,3366,3367,3370],{"style":2358},[1864,3368],{"className":3369,"style":2169},[2168],[1864,3371,3373],{"className":3372},[2173,2174,2175,2176],[1864,3374,2059],{"className":3375},[1925,2131,2176],[1864,3377,3378,3381],{"style":2370},[1864,3379],{"className":3380,"style":2169},[2168],[1864,3382,3384],{"className":3383},[2173,2174,2175,2176],[1864,3385,3387],{"className":3386},[1925,2176],[1864,3388,2105],{"className":3389},[1925,2176],[1864,3391,2184],{"className":3392},[2183],[1864,3394,3396],{"className":3395},[2156],[1864,3397,3399],{"className":3398,"style":2392},[2160],[1864,3400],{},[1864,3402,2108],{"className":3403,"style":2398},[1925,2131],[1864,3405],{"className":3406,"style":2143},[1933],[1864,3408,3220],{"className":3409},[3319],[1864,3411],{"className":3412,"style":2143},[1933],[1864,3414,3416,3420,3460,3463,3467,3470,3510,3513,3516],{"className":3415},[1916],[1864,3417],{"className":3418,"style":3419},[1920],"height:0.8778em;vertical-align:-0.1944em;",[1864,3421,3423,3426],{"className":3422},[1925],[1864,3424,3225],{"className":3425},[1925,2131],[1864,3427,3429],{"className":3428},[2147],[1864,3430,3432,3452],{"className":3431},[2151,2152],[1864,3433,3435,3449],{"className":3434},[2156],[1864,3436,3438],{"className":3437,"style":2161},[2160],[1864,3439,3440,3443],{"style":2293},[1864,3441],{"className":3442,"style":2169},[2168],[1864,3444,3446],{"className":3445},[2173,2174,2175,2176],[1864,3447,2059],{"className":3448},[1925,2131,2176],[1864,3450,2184],{"className":3451},[2183],[1864,3453,3455],{"className":3454},[2156],[1864,3456,3458],{"className":3457,"style":2191},[2160],[1864,3459],{},[1864,3461,1890],{"className":3462},[1929],[1864,3464],{"className":3465,"style":3466},[1933],"margin-right:2em;",[1864,3468],{"className":3469,"style":1934},[1933],[1864,3471,3473,3476],{"className":3472},[1925],[1864,3474,2056],{"className":3475,"style":2143},[1925,2131],[1864,3477,3479],{"className":3478},[2147],[1864,3480,3482,3502],{"className":3481},[2151,2152],[1864,3483,3485,3499],{"className":3484},[2156],[1864,3486,3488],{"className":3487,"style":2161},[2160],[1864,3489,3490,3493],{"style":2164},[1864,3491],{"className":3492,"style":2169},[2168],[1864,3494,3496],{"className":3495},[2173,2174,2175,2176],[1864,3497,2059],{"className":3498},[1925,2131,2176],[1864,3500,2184],{"className":3501},[2183],[1864,3503,3505],{"className":3504},[2156],[1864,3506,3508],{"className":3507,"style":2191},[2160],[1864,3509],{},[1864,3511],{"className":3512,"style":2197},[1933],[1864,3514,2075],{"className":3515},[2201],[1864,3517],{"className":3518,"style":2197},[1933],[1864,3520,3522,3525,3528,3531,3534,3537,3540,3591,3594],{"className":3521},[1916],[1864,3523],{"className":3524,"style":2127},[1920],[1864,3526,3243],{"className":3527},[2333],[1864,3529,2093],{"className":3530},[2136],[1864,3532,1885],{"className":3533},[1925],[1864,3535,1890],{"className":3536},[1929],[1864,3538],{"className":3539,"style":1934},[1933],[1864,3541,3543,3546],{"className":3542},[1925],[1864,3544,2056],{"className":3545,"style":2143},[1925,2131],[1864,3547,3549],{"className":3548},[2147],[1864,3550,3552,3583],{"className":3551},[2151,2152],[1864,3553,3555,3580],{"className":3554},[2156],[1864,3556,3558,3569],{"className":3557,"style":3295},[2160],[1864,3559,3560,3563],{"style":3298},[1864,3561],{"className":3562,"style":2169},[2168],[1864,3564,3566],{"className":3565},[2173,2174,2175,2176],[1864,3567,2059],{"className":3568},[1925,2131,2176],[1864,3570,3571,3574],{"style":2370},[1864,3572],{"className":3573,"style":2169},[2168],[1864,3575,3577],{"className":3576},[2173,2174,2175,2176],[1864,3578,3205],{"className":3579},[3319,2176],[1864,3581,2184],{"className":3582},[2183],[1864,3584,3586],{"className":3585},[2156],[1864,3587,3589],{"className":3588,"style":2392},[2160],[1864,3590],{},[1864,3592,2111],{"className":3593},[2256],[1864,3595,2114],{"className":3596},[1925],[1796,3598,3599,3600,3629],{},"它把参与概率与正值大小绑定在同一 ",[1864,3601,3603,3617],{"className":3602},[1867],[1864,3604,3606],{"className":3605},[1871],[1873,3607,3608],{"xmlns":1875},[1877,3609,3610,3614],{},[1880,3611,3612],{},[2044,3613,2108],{},[1905,3615,3616],{"encoding":1907},"\\beta",[1864,3618,3620],{"className":3619,"ariaHidden":1889},[1912],[1864,3621,3623,3626],{"className":3622},[1916],[1864,3624],{"className":3625,"style":2514},[1920],[1864,3627,2108],{"className":3628,"style":2398},[1925,2131]," 和误差分布上。若是否工作由托育可得性决定，而工作后的时长由工资决定，这一限制通常过强。",[1796,3631,3632],{},"还要区分：",[3634,3635,3636,3642,3648],"ul",{},[1817,3637,3638,3641],{},[1799,3639,3640],{},"删失："," 个体在样本中，但结果只知道落在某边界；",[1817,3643,3644,3647],{},[1799,3645,3646],{},"截断："," 边界外个体完全不在样本中；",[1817,3649,3650,3653],{},[1799,3651,3652],{},"样本选择："," 结果可见性由另一个相关过程决定。",[1796,3655,3656],{},"三者的似然和目标人群都不同。",[1807,3658,3660],{"id":3659},"_6-暴露量与固定效应","6. 暴露量与固定效应",[1796,3662,3663],{},"计数常受观察时间影响。医院住院 10 天与 100 天的感染次数不能直接比较，应使用 offset：",[1864,3665,3667],{"className":3666},[2029],[1864,3668,3670,3739],{"className":3669},[1867],[1864,3671,3673],{"className":3672},[1871],[1873,3674,3675],{"xmlns":1875,"display":2038},[1877,3676,3677,3736],{},[1880,3678,3679,3682,3684,3686,3688,3694,3696,3702,3704,3706,3714,3716,3718,3720,3722,3724,3732,3734],{},[2044,3680,3681],{},"log",[1887,3683,2090],{},[2044,3685,2046],{},[1887,3687,2050],{"stretchy":2049},[2052,3689,3690,3692],{},[2044,3691,2056],{},[2044,3693,2059],{},[1887,3695,2062],{},[2052,3697,3698,3700],{},[2044,3699,2067],{},[2044,3701,2059],{},[1887,3703,2072],{"stretchy":2049},[1887,3705,2075],{},[2095,3707,3708,3710,3712],{},[2044,3709,2067],{},[2044,3711,2059],{},[1887,3713,2105],{"mathvariant":2103,"lspace":2104,"rspace":2104},[2044,3715,2108],{},[1887,3717,3220],{},[2044,3719,3681],{},[1887,3721,2090],{},[1887,3723,2093],{"stretchy":2049},[2052,3725,3726,3730],{},[3727,3728,3729],"mtext",{},"exposure",[2044,3731,2059],{},[1887,3733,2111],{"stretchy":2049},[2044,3735,2114],{"mathvariant":2103},[1905,3737,3738],{"encoding":1907},"\\log E[Y_i\\mid X_i]\n=X_i'\\beta+\\log(\\text{exposure}_i).",[1864,3740,3742,3814,3872,3944],{"className":3741,"ariaHidden":1889},[1912],[1864,3743,3745,3748,3756,3759,3762,3765,3805,3808,3811],{"className":3744},[1916],[1864,3746],{"className":3747,"style":2127},[1920],[1864,3749,3751,3752],{"className":3750},[2333],"lo",[1864,3753,3755],{"style":3754},"margin-right:0.0139em;","g",[1864,3757],{"className":3758,"style":1934},[1933],[1864,3760,2046],{"className":3761,"style":2132},[1925,2131],[1864,3763,2050],{"className":3764},[2136],[1864,3766,3768,3771],{"className":3767},[1925],[1864,3769,2056],{"className":3770,"style":2143},[1925,2131],[1864,3772,3774],{"className":3773},[2147],[1864,3775,3777,3797],{"className":3776},[2151,2152],[1864,3778,3780,3794],{"className":3779},[2156],[1864,3781,3783],{"className":3782,"style":2161},[2160],[1864,3784,3785,3788],{"style":2164},[1864,3786],{"className":3787,"style":2169},[2168],[1864,3789,3791],{"className":3790},[2173,2174,2175,2176],[1864,3792,2059],{"className":3793},[1925,2131,2176],[1864,3795,2184],{"className":3796},[2183],[1864,3798,3800],{"className":3799},[2156],[1864,3801,3803],{"className":3802,"style":2191},[2160],[1864,3804],{},[1864,3806],{"className":3807,"style":2197},[1933],[1864,3809,2062],{"className":3810},[2201],[1864,3812],{"className":3813,"style":2197},[1933],[1864,3815,3817,3820,3860,3863,3866,3869],{"className":3816},[1916],[1864,3818],{"className":3819,"style":2127},[1920],[1864,3821,3823,3826],{"className":3822},[1925],[1864,3824,2067],{"className":3825,"style":2217},[1925,2131],[1864,3827,3829],{"className":3828},[2147],[1864,3830,3832,3852],{"className":3831},[2151,2152],[1864,3833,3835,3849],{"className":3834},[2156],[1864,3836,3838],{"className":3837,"style":2161},[2160],[1864,3839,3840,3843],{"style":2232},[1864,3841],{"className":3842,"style":2169},[2168],[1864,3844,3846],{"className":3845},[2173,2174,2175,2176],[1864,3847,2059],{"className":3848},[1925,2131,2176],[1864,3850,2184],{"className":3851},[2183],[1864,3853,3855],{"className":3854},[2156],[1864,3856,3858],{"className":3857,"style":2191},[2160],[1864,3859],{},[1864,3861,2072],{"className":3862},[2256],[1864,3864],{"className":3865,"style":2197},[1933],[1864,3867,2075],{"className":3868},[2201],[1864,3870],{"className":3871,"style":2197},[1933],[1864,3873,3875,3878,3932,3935,3938,3941],{"className":3874},[1916],[1864,3876],{"className":3877,"style":3346},[1920],[1864,3879,3881,3884],{"className":3880},[1925],[1864,3882,2067],{"className":3883,"style":2217},[1925,2131],[1864,3885,3887],{"className":3886},[2147],[1864,3888,3890,3924],{"className":3889},[2151,2152],[1864,3891,3893,3921],{"className":3892},[2156],[1864,3894,3896,3907],{"className":3895,"style":2355},[2160],[1864,3897,3898,3901],{"style":2358},[1864,3899],{"className":3900,"style":2169},[2168],[1864,3902,3904],{"className":3903},[2173,2174,2175,2176],[1864,3905,2059],{"className":3906},[1925,2131,2176],[1864,3908,3909,3912],{"style":2370},[1864,3910],{"className":3911,"style":2169},[2168],[1864,3913,3915],{"className":3914},[2173,2174,2175,2176],[1864,3916,3918],{"className":3917},[1925,2176],[1864,3919,2105],{"className":3920},[1925,2176],[1864,3922,2184],{"className":3923},[2183],[1864,3925,3927],{"className":3926},[2156],[1864,3928,3930],{"className":3929,"style":2392},[2160],[1864,3931],{},[1864,3933,2108],{"className":3934,"style":2398},[1925,2131],[1864,3936],{"className":3937,"style":2143},[1933],[1864,3939,3220],{"className":3940},[3319],[1864,3942],{"className":3943,"style":2143},[1933],[1864,3945,3947,3950,3955,3958,4005,4008],{"className":3946},[1916],[1864,3948],{"className":3949,"style":2127},[1920],[1864,3951,3751,3953],{"className":3952},[2333],[1864,3954,3755],{"style":3754},[1864,3956,2093],{"className":3957},[2136],[1864,3959,3961,3968],{"className":3960},[1925],[1864,3962,3965],{"className":3963},[1925,3964],"text",[1864,3966,3729],{"className":3967},[1925],[1864,3969,3971],{"className":3970},[2147],[1864,3972,3974,3996],{"className":3973},[2151,2152],[1864,3975,3977,3993],{"className":3976},[2156],[1864,3978,3981],{"className":3979,"style":3980},[2160],"height:0.2175em;",[1864,3982,3984,3987],{"style":3983},"top:-2.4559em;margin-right:0.05em;",[1864,3985],{"className":3986,"style":2169},[2168],[1864,3988,3990],{"className":3989},[2173,2174,2175,2176],[1864,3991,2059],{"className":3992},[1925,2131,2176],[1864,3994,2184],{"className":3995},[2183],[1864,3997,3999],{"className":3998},[2156],[1864,4000,4003],{"className":4001,"style":4002},[2160],"height:0.2441em;",[1864,4004],{},[1864,4006,2111],{"className":4007},[2256],[1864,4009,2114],{"className":4010},[1925],[1796,4012,4013],{},"面板 Poisson 固定效应可处理单位不变异质性，但仍需严格外生性；不要因模型名有“固定效应”就忽略反向因果。",[1807,4015,4017],{"id":4016},"_7-诊断与报告","7. 诊断与报告",[3634,4019,4020,4023,4026,4029,4032,4035,4038,4041],{},[1817,4021,4022],{},"结果是次数、率、金额还是潜变量？",[1817,4024,4025],{},"观察窗口和暴露量是否一致？",[1817,4027,4028],{},"零来自低发生率、结构状态还是参与决策？",[1817,4030,4031],{},"均值—方差关系如何，尾部是否由极端值驱动？",[1817,4033,4034],{},"报告 IRR 之外，是否给出典型人群预测均值？",[1817,4036,4037],{},"是否使用稳健或聚类标准误？",[1817,4039,4040],{},"模型比较是否包含机制解释、校准和样本外表现？",[1817,4042,4043],{},"删失、截断或选择规则是否被准确记录？",[1807,4045,4046],{"id":4046},"常见误区",[3634,4048,4049,4052,4055,4058,4061],{},[1817,4050,4051],{},"看到过度离散就认定 Poisson 系数一定不一致；",[1817,4053,4054],{},"看到很多零就自动使用 zero-inflated 模型；",[1817,4056,4057],{},"把 Tobit 系数直接解释为观测结果的边际效应；",[1817,4059,4060],{},"忽略暴露时间；",[1817,4062,4063],{},"对只有就业者可见的工资简单删除非就业者。",[1807,4065,4066],{"id":4066},"课堂任务",[1796,4068,4069],{},"比较“医生就诊次数”和“年度慈善捐款”：",[1814,4071,4072,4075,4078,4081,4084],{},[1817,4073,4074],{},"为每个结果解释零值机制；",[1817,4076,4077],{},"判断是否需要 offset；",[1817,4079,4080],{},"选择 Poisson、two-part 或其他模型并说明理由；",[1817,4082,4083],{},"指出一个未观测异质性来源；",[1817,4085,4086],{},"把一个系数转成 IRR 或预测均值差。",[1807,4088,4089],{"id":4089},"核心阅读",[3634,4091,4092,4105,4113],{},[1817,4093,4094,4095,2789],{},"Cameron & Trivedi, ",[4096,4097,4101],"a",{"href":4098,"rel":4099},"https:\u002F\u002Fdoi.org\u002F10.1017\u002FCBO9781139013567",[4100],"nofollow",[4102,4103,4104],"em",{},"Regression Analysis of Count Data",[1817,4106,4107,4108,2789],{},"Gourieroux, Monfort & Trognon (1984), ",[4096,4109,4112],{"href":4110,"rel":4111},"https:\u002F\u002Fdoi.org\u002F10.2307\u002F1913859",[4100],"“Pseudo Maximum Likelihood Methods”",[1817,4114,4115,4116,2789],{},"Santos Silva & Tenreyro (2006), ",[4096,4117,4120],{"href":4118,"rel":4119},"https:\u002F\u002Fdoi.org\u002F10.1162\u002Frest.88.4.641",[4100],"“The Log of Gravity”",[1796,4122,4123,4124,4128,4129,2789],{},"上一章：",[4096,4125,4127],{"href":4126},"..\u002F08-discrete-choice\u002F","离散选择模型","｜下一章：",[4096,4130,4132],{"href":4131},"..\u002F10-synthetic-control\u002F","合成控制法",{"title":10,"searchDepth":4134,"depth":4134,"links":4135},2,[4136,4137,4138,4139,4140,4146,4147,4148,4149,4150,4151],{"id":1809,"depth":4134,"text":1809},{"id":1834,"depth":4134,"text":1835},{"id":2024,"depth":4134,"text":2025},{"id":2938,"depth":4134,"text":2939},{"id":2955,"depth":4134,"text":2956,"children":4141},[4142,4144,4145],{"id":2960,"depth":4143,"text":2960},3,{"id":2966,"depth":4143,"text":2967},{"id":1974,"depth":4143,"text":3168},{"id":3177,"depth":4134,"text":3178},{"id":3659,"depth":4134,"text":3660},{"id":4016,"depth":4134,"text":4017},{"id":4046,"depth":4134,"text":4046},{"id":4066,"depth":4134,"text":4066},{"id":4089,"depth":4134,"text":4089},"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。","md",{"sidebar":4155},{"order":4156},9,true,{"title":1522,"description":4152},"tCec9sfzeSSU5NoWAhLse0YKWx3KZnDsgmGzmf8LyNM",[4161,4163],{"title":1516,"path":1517,"stem":1518,"description":4162,"children":-1},"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。",{"title":1528,"path":1529,"stem":1530,"description":4164,"children":-1},"为单个处理单位构造加权反事实，掌握供体池、处理前拟合、安慰剂推断与现代扩展。",1785754752059]