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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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分即可获得奖学金。阈值两侧学生的后续入学率跳跃，能否解释为奖学金效应？",[1796,1804,1805],{},"断点回归（RDD）依赖一个简单直觉：若其他决定因素在阈值处平滑变化，紧邻阈值两侧的结果跳跃可归因于处理资格的跳跃。",[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",{},"区分运行变量、阈值、资格与实际处理；",[1817,1821,1822],{},"写出 sharp RDD 的局部目标参数；",[1817,1824,1825],{},"用局部线性回归解释带宽的偏差—方差权衡；",[1817,1827,1828],{},"诊断排序、操纵、协变量不连续与结果测量变化；",[1817,1830,1831],{},"区分 sharp、fuzzy、kink 与 donut RDD。",[1807,1833,1835],{"id":1834},"_1-识别对象是阈值处的局部效应","1. 识别对象是阈值处的局部效应",[1796,1837,1838,1839,1946,1947,1977],{},"令运行变量为 ",[1840,1841,1844,1873],"span",{"className":1842},[1843],"katex",[1840,1845,1848],{"className":1846},[1847],"katex-mathml",[1849,1850,1852],"math",{"xmlns":1851},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML",[1853,1854,1855,1868],"semantics",{},[1856,1857,1858],"mrow",{},[1859,1860,1861,1865],"msub",{},[1862,1863,1864],"mi",{},"X",[1862,1866,1867],{},"i",[1869,1870,1872],"annotation",{"encoding":1871},"application\u002Fx-tex","X_i",[1840,1874,1878],{"className":1875,"ariaHidden":1877},[1876],"katex-html","true",[1840,1879,1882,1887],{"className":1880},[1881],"base",[1840,1883],{"className":1884,"style":1886},[1885],"strut","height:0.8333em;vertical-align:-0.15em;",[1840,1888,1891,1896],{"className":1889},[1890],"mord",[1840,1892,1864],{"className":1893,"style":1895},[1890,1894],"mathnormal","margin-right:0.0785em;",[1840,1897,1900],{"className":1898},[1899],"msupsub",[1840,1901,1905,1937],{"className":1902},[1903,1904],"vlist-t","vlist-t2",[1840,1906,1909,1932],{"className":1907},[1908],"vlist-r",[1840,1910,1914],{"className":1911,"style":1913},[1912],"vlist","height:0.3117em;",[1840,1915,1917,1922],{"style":1916},"top:-2.55em;margin-left:-0.0785em;margin-right:0.05em;",[1840,1918],{"className":1919,"style":1921},[1920],"pstrut","height:2.7em;",[1840,1923,1929],{"className":1924},[1925,1926,1927,1928],"sizing","reset-size6","size3","mtight",[1840,1930,1867],{"className":1931},[1890,1894,1928],[1840,1933,1936],{"className":1934},[1935],"vlist-s","​",[1840,1938,1940],{"className":1939},[1908],[1840,1941,1944],{"className":1942,"style":1943},[1912],"height:0.15em;",[1840,1945],{},"，阈值为 ",[1840,1948,1950,1964],{"className":1949},[1843],[1840,1951,1953],{"className":1952},[1847],[1849,1954,1955],{"xmlns":1851},[1853,1956,1957,1962],{},[1856,1958,1959],{},[1862,1960,1961],{},"c",[1869,1963,1961],{"encoding":1871},[1840,1965,1967],{"className":1966,"ariaHidden":1877},[1876],[1840,1968,1970,1974],{"className":1969},[1881],[1840,1971],{"className":1972,"style":1973},[1885],"height:0.4306em;",[1840,1975,1961],{"className":1976},[1890,1894],"，sharp 设计中",[1840,1979,1982],{"className":1980},[1981],"katex-display",[1840,1983,1985,2036],{"className":1984},[1843],[1840,1986,1988],{"className":1987},[1847],[1849,1989,1991],{"xmlns":1851,"display":1990},"block",[1853,1992,1993,2033],{},[1856,1994,1995,2002,2006,2011,2015,2021,2024,2026,2029],{},[1859,1996,1997,2000],{},[1862,1998,1999],{},"D",[1862,2001,1867],{},[2003,2004,2005],"mo",{},"=",[2007,2008,2010],"mn",{"mathvariant":2009},"bold","1",[2003,2012,2014],{"stretchy":2013},"false","{",[1859,2016,2017,2019],{},[1862,2018,1864],{},[1862,2020,1867],{},[2003,2022,2023],{},"≥",[1862,2025,1961],{},[2003,2027,2028],{"stretchy":2013},"}",[1862,2030,2032],{"mathvariant":2031},"normal",".",[1869,2034,2035],{"encoding":1871},"D_i=\\mathbf 1\\{X_i\\ge c\\}.",[1840,2037,2039,2099,2163],{"className":2038,"ariaHidden":1877},[1876],[1840,2040,2042,2045,2087,2092,2096],{"className":2041},[1881],[1840,2043],{"className":2044,"style":1886},[1885],[1840,2046,2048,2052],{"className":2047},[1890],[1840,2049,1999],{"className":2050,"style":2051},[1890,1894],"margin-right:0.0278em;",[1840,2053,2055],{"className":2054},[1899],[1840,2056,2058,2079],{"className":2057},[1903,1904],[1840,2059,2061,2076],{"className":2060},[1908],[1840,2062,2064],{"className":2063,"style":1913},[1912],[1840,2065,2067,2070],{"style":2066},"top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;",[1840,2068],{"className":2069,"style":1921},[1920],[1840,2071,2073],{"className":2072},[1925,1926,1927,1928],[1840,2074,1867],{"className":2075},[1890,1894,1928],[1840,2077,1936],{"className":2078},[1935],[1840,2080,2082],{"className":2081},[1908],[1840,2083,2085],{"className":2084,"style":1943},[1912],[1840,2086],{},[1840,2088],{"className":2089,"style":2091},[2090],"mspace","margin-right:0.2778em;",[1840,2093,2005],{"className":2094},[2095],"mrel",[1840,2097],{"className":2098,"style":2091},[2090],[1840,2100,2102,2106,2110,2114,2154,2157,2160],{"className":2101},[1881],[1840,2103],{"className":2104,"style":2105},[1885],"height:1em;vertical-align:-0.25em;",[1840,2107,2010],{"className":2108},[1890,2109],"mathbf",[1840,2111,2014],{"className":2112},[2113],"mopen",[1840,2115,2117,2120],{"className":2116},[1890],[1840,2118,1864],{"className":2119,"style":1895},[1890,1894],[1840,2121,2123],{"className":2122},[1899],[1840,2124,2126,2146],{"className":2125},[1903,1904],[1840,2127,2129,2143],{"className":2128},[1908],[1840,2130,2132],{"className":2131,"style":1913},[1912],[1840,2133,2134,2137],{"style":1916},[1840,2135],{"className":2136,"style":1921},[1920],[1840,2138,2140],{"className":2139},[1925,1926,1927,1928],[1840,2141,1867],{"className":2142},[1890,1894,1928],[1840,2144,1936],{"className":2145},[1935],[1840,2147,2149],{"className":2148},[1908],[1840,2150,2152],{"className":2151,"style":1943},[1912],[1840,2153],{},[1840,2155],{"className":2156,"style":2091},[2090],[1840,2158,2023],{"className":2159},[2095],[1840,2161],{"className":2162,"style":2091},[2090],[1840,2164,2166,2169,2172,2176],{"className":2165},[1881],[1840,2167],{"className":2168,"style":2105},[1885],[1840,2170,1961],{"className":2171},[1890,1894],[1840,2173,2028],{"className":2174},[2175],"mclose",[1840,2177,2032],{"className":2178},[1890],[1796,2180,2181,2182,2210],{},"若两个潜在结果的条件均值在 ",[1840,2183,2185,2198],{"className":2184},[1843],[1840,2186,2188],{"className":2187},[1847],[1849,2189,2190],{"xmlns":1851},[1853,2191,2192,2196],{},[1856,2193,2194],{},[1862,2195,1961],{},[1869,2197,1961],{"encoding":1871},[1840,2199,2201],{"className":2200,"ariaHidden":1877},[1876],[1840,2202,2204,2207],{"className":2203},[1881],[1840,2205],{"className":2206,"style":1973},[1885],[1840,2208,1961],{"className":2209},[1890,1894]," 处连续，则",[1840,2212,2214],{"className":2213},[1981],[1840,2215,2217,2325],{"className":2216},[1843],[1840,2218,2220],{"className":2219},[1847],[1849,2221,2222],{"xmlns":1851,"display":1990},[1853,2223,2224,2322],{},[1856,2225,2226,2240,2242,2263,2266,2269,2272,2275,2277,2279,2281,2284,2287,2304,2306,2308,2310,2312,2314,2316,2318,2320],{},[1859,2227,2228,2231],{},[1862,2229,2230],{},"τ",[1856,2232,2233,2236,2238],{},[1862,2234,2235],{},"R",[1862,2237,1999],{},[1862,2239,1999],{},[2003,2241,2005],{},[2243,2244,2245,2253],"munder",{},[1856,2246,2247,2250],{},[1862,2248,2249],{},"lim",[2003,2251,2252],{},"⁡",[1856,2254,2255,2258,2261],{},[1862,2256,2257],{},"x",[2003,2259,2260],{},"↓",[1862,2262,1961],{},[1862,2264,2265],{},"E",[2003,2267,2268],{"stretchy":2013},"[",[1862,2270,2271],{},"Y",[2003,2273,2274],{},"∣",[1862,2276,1864],{},[2003,2278,2005],{},[1862,2280,2257],{},[2003,2282,2283],{"stretchy":2013},"]",[2003,2285,2286],{},"−",[2243,2288,2289,2295],{},[1856,2290,2291,2293],{},[1862,2292,2249],{},[2003,2294,2252],{},[1856,2296,2297,2299,2302],{},[1862,2298,2257],{},[2003,2300,2301],{},"↑",[1862,2303,1961],{},[1862,2305,2265],{},[2003,2307,2268],{"stretchy":2013},[1862,2309,2271],{},[2003,2311,2274],{},[1862,2313,1864],{},[2003,2315,2005],{},[1862,2317,2257],{},[2003,2319,2283],{"stretchy":2013},[1862,2321,2032],{"mathvariant":2031},[1869,2323,2324],{"encoding":1871},"\\tau_{RDD}\n=\n\\lim_{x\\downarrow c}E[Y\\mid X=x]\n-\n\\lim_{x\\uparrow c}E[Y\\mid X=x].",[1840,2326,2328,2397,2488,2507,2529,2609,2627],{"className":2327,"ariaHidden":1877},[1876],[1840,2329,2331,2335,2388,2391,2394],{"className":2330},[1881],[1840,2332],{"className":2333,"style":2334},[1885],"height:0.5806em;vertical-align:-0.15em;",[1840,2336,2338,2342],{"className":2337},[1890],[1840,2339,2230],{"className":2340,"style":2341},[1890,1894],"margin-right:0.1132em;",[1840,2343,2345],{"className":2344},[1899],[1840,2346,2348,2380],{"className":2347},[1903,1904],[1840,2349,2351,2377],{"className":2350},[1908],[1840,2352,2355],{"className":2353,"style":2354},[1912],"height:0.3283em;",[1840,2356,2358,2361],{"style":2357},"top:-2.55em;margin-left:-0.1132em;margin-right:0.05em;",[1840,2359],{"className":2360,"style":1921},[1920],[1840,2362,2364],{"className":2363},[1925,1926,1927,1928],[1840,2365,2367,2371,2374],{"className":2366},[1890,1928],[1840,2368,2235],{"className":2369,"style":2370},[1890,1894,1928],"margin-right:0.0077em;",[1840,2372,1999],{"className":2373,"style":2051},[1890,1894,1928],[1840,2375,1999],{"className":2376,"style":2051},[1890,1894,1928],[1840,2378,1936],{"className":2379},[1935],[1840,2381,2383],{"className":2382},[1908],[1840,2384,2386],{"className":2385,"style":1943},[1912],[1840,2387],{},[1840,2389],{"className":2390,"style":2091},[2090],[1840,2392,2005],{"className":2393},[2095],[1840,2395],{"className":2396,"style":2091},[2090],[1840,2398,2400,2404,2464,2468,2472,2475,2479,2482,2485],{"className":2399},[1881],[1840,2401],{"className":2402,"style":2403},[1885],"height:1.6382em;vertical-align:-0.8882em;",[1840,2405,2409],{"className":2406},[2407,2408],"mop","op-limits",[1840,2410,2412,2455],{"className":2411},[1903,1904],[1840,2413,2415,2452],{"className":2414},[1908],[1840,2416,2419,2441],{"className":2417,"style":2418},[1912],"height:0.6944em;",[1840,2420,2422,2426],{"style":2421},"top:-2.3479em;margin-left:0em;",[1840,2423],{"className":2424,"style":2425},[1920],"height:3em;",[1840,2427,2429],{"className":2428},[1925,1926,1927,1928],[1840,2430,2432,2435,2438],{"className":2431},[1890,1928],[1840,2433,2257],{"className":2434},[1890,1894,1928],[1840,2436,2260],{"className":2437},[2095,1928],[1840,2439,1961],{"className":2440},[1890,1894,1928],[1840,2442,2444,2447],{"style":2443},"top:-3em;",[1840,2445],{"className":2446,"style":2425},[1920],[1840,2448,2449],{},[1840,2450,2249],{"className":2451},[2407],[1840,2453,1936],{"className":2454},[1935],[1840,2456,2458],{"className":2457},[1908],[1840,2459,2462],{"className":2460,"style":2461},[1912],"height:0.8882em;",[1840,2463],{},[1840,2465],{"className":2466,"style":2467},[2090],"margin-right:0.1667em;",[1840,2469,2265],{"className":2470,"style":2471},[1890,1894],"margin-right:0.0576em;",[1840,2473,2268],{"className":2474},[2113],[1840,2476,2271],{"className":2477,"style":2478},[1890,1894],"margin-right:0.2222em;",[1840,2480],{"className":2481,"style":2091},[2090],[1840,2483,2274],{"className":2484},[2095],[1840,2486],{"className":2487,"style":2091},[2090],[1840,2489,2491,2495,2498,2501,2504],{"className":2490},[1881],[1840,2492],{"className":2493,"style":2494},[1885],"height:0.6833em;",[1840,2496,1864],{"className":2497,"style":1895},[1890,1894],[1840,2499],{"className":2500,"style":2091},[2090],[1840,2502,2005],{"className":2503},[2095],[1840,2505],{"className":2506,"style":2091},[2090],[1840,2508,2510,2513,2516,2519,2522,2526],{"className":2509},[1881],[1840,2511],{"className":2512,"style":2105},[1885],[1840,2514,2257],{"className":2515},[1890,1894],[1840,2517,2283],{"className":2518},[2175],[1840,2520],{"className":2521,"style":2478},[2090],[1840,2523,2286],{"className":2524},[2525],"mbin",[1840,2527],{"className":2528,"style":2478},[2090],[1840,2530,2532,2535,2588,2591,2594,2597,2600,2603,2606],{"className":2531},[1881],[1840,2533],{"className":2534,"style":2403},[1885],[1840,2536,2538],{"className":2537},[2407,2408],[1840,2539,2541,2580],{"className":2540},[1903,1904],[1840,2542,2544,2577],{"className":2543},[1908],[1840,2545,2547,2567],{"className":2546,"style":2418},[1912],[1840,2548,2549,2552],{"style":2421},[1840,2550],{"className":2551,"style":2425},[1920],[1840,2553,2555],{"className":2554},[1925,1926,1927,1928],[1840,2556,2558,2561,2564],{"className":2557},[1890,1928],[1840,2559,2257],{"className":2560},[1890,1894,1928],[1840,2562,2301],{"className":2563},[2095,1928],[1840,2565,1961],{"className":2566},[1890,1894,1928],[1840,2568,2569,2572],{"style":2443},[1840,2570],{"className":2571,"style":2425},[1920],[1840,2573,2574],{},[1840,2575,2249],{"className":2576},[2407],[1840,2578,1936],{"className":2579},[1935],[1840,2581,2583],{"className":2582},[1908],[1840,2584,2586],{"className":2585,"style":2461},[1912],[1840,2587],{},[1840,2589],{"className":2590,"style":2467},[2090],[1840,2592,2265],{"className":2593,"style":2471},[1890,1894],[1840,2595,2268],{"className":2596},[2113],[1840,2598,2271],{"className":2599,"style":2478},[1890,1894],[1840,2601],{"className":2602,"style":2091},[2090],[1840,2604,2274],{"className":2605},[2095],[1840,2607],{"className":2608,"style":2091},[2090],[1840,2610,2612,2615,2618,2621,2624],{"className":2611},[1881],[1840,2613],{"className":2614,"style":2494},[1885],[1840,2616,1864],{"className":2617,"style":1895},[1890,1894],[1840,2619],{"className":2620,"style":2091},[2090],[1840,2622,2005],{"className":2623},[2095],[1840,2625],{"className":2626,"style":2091},[2090],[1840,2628,2630,2633,2636,2639],{"className":2629},[1881],[1840,2631],{"className":2632,"style":2105},[1885],[1840,2634,2257],{"className":2635},[1890,1894],[1840,2637,2283],{"className":2638},[2175],[1840,2640,2032],{"className":2641},[1890],[1796,2643,2644],{},"这估计阈值附近单位的效应。它不能自动推广到成绩 50 分或 100 分的学生。",[1807,2646,2648],{"id":2647},"_2-局部线性模型","2. 局部线性模型",[1796,2650,2651,2652,2681],{},"在带宽 ",[1840,2653,2655,2669],{"className":2654},[1843],[1840,2656,2658],{"className":2657},[1847],[1849,2659,2660],{"xmlns":1851},[1853,2661,2662,2667],{},[1856,2663,2664],{},[1862,2665,2666],{},"h",[1869,2668,2666],{"encoding":1871},[1840,2670,2672],{"className":2671,"ariaHidden":1877},[1876],[1840,2673,2675,2678],{"className":2674},[1881],[1840,2676],{"className":2677,"style":2418},[1885],[1840,2679,2666],{"className":2680},[1890,1894]," 内常估计：",[1840,2683,2685],{"className":2684},[1981],[1840,2686,2688,2812],{"className":2687},[1843],[1840,2689,2691],{"className":2690},[1847],[1849,2692,2693],{"xmlns":1851,"display":1990},[1853,2694,2695,2809],{},[1856,2696,2697,2703,2705,2708,2711,2713,2719,2721,2728,2731,2737,2739,2741,2744,2746,2753,2759,2761,2767,2769,2771,2773,2775,2782,2785,2788,2790,2796,2798,2800,2802,2805,2807],{},[1859,2698,2699,2701],{},[1862,2700,2271],{},[1862,2702,1867],{},[2003,2704,2005],{},[1862,2706,2707],{},"α",[2003,2709,2710],{},"+",[1862,2712,2230],{},[1859,2714,2715,2717],{},[1862,2716,1999],{},[1862,2718,1867],{},[2003,2720,2710],{},[1859,2722,2723,2726],{},[1862,2724,2725],{},"β",[2007,2727,2010],{},[2003,2729,2730],{"stretchy":2013},"(",[1859,2732,2733,2735],{},[1862,2734,1864],{},[1862,2736,1867],{},[2003,2738,2286],{},[1862,2740,1961],{},[2003,2742,2743],{"stretchy":2013},")",[2003,2745,2710],{},[1859,2747,2748,2750],{},[1862,2749,2725],{},[2007,2751,2752],{},"2",[1859,2754,2755,2757],{},[1862,2756,1999],{},[1862,2758,1867],{},[2003,2760,2730],{"stretchy":2013},[1859,2762,2763,2765],{},[1862,2764,1864],{},[1862,2766,1867],{},[2003,2768,2286],{},[1862,2770,1961],{},[2003,2772,2743],{"stretchy":2013},[2003,2774,2710],{},[1859,2776,2777,2780],{},[1862,2778,2779],{},"ε",[1862,2781,1867],{},[2003,2783,2784],{"separator":1877},",",[2090,2786],{"width":2787},"2em",[1862,2789,2274],{"mathvariant":2031},[1859,2791,2792,2794],{},[1862,2793,1864],{},[1862,2795,1867],{},[2003,2797,2286],{},[1862,2799,1961],{},[1862,2801,2274],{"mathvariant":2031},[2003,2803,2804],{},"≤",[1862,2806,2666],{},[1862,2808,2032],{"mathvariant":2031},[1869,2810,2811],{"encoding":1871},"Y_i=\\alpha+\\tau D_i+\\beta_1(X_i-c)\n+\\beta_2D_i(X_i-c)+\\varepsilon_i,\n\\qquad |X_i-c|\\le h.",[1840,2813,2815,2871,2891,2949,3050,3071,3209,3230,3340,3361],{"className":2814,"ariaHidden":1877},[1876],[1840,2816,2818,2821,2862,2865,2868],{"className":2817},[1881],[1840,2819],{"className":2820,"style":1886},[1885],[1840,2822,2824,2827],{"className":2823},[1890],[1840,2825,2271],{"className":2826,"style":2478},[1890,1894],[1840,2828,2830],{"className":2829},[1899],[1840,2831,2833,2854],{"className":2832},[1903,1904],[1840,2834,2836,2851],{"className":2835},[1908],[1840,2837,2839],{"className":2838,"style":1913},[1912],[1840,2840,2842,2845],{"style":2841},"top:-2.55em;margin-left:-0.2222em;margin-right:0.05em;",[1840,2843],{"className":2844,"style":1921},[1920],[1840,2846,2848],{"className":2847},[1925,1926,1927,1928],[1840,2849,1867],{"className":2850},[1890,1894,1928],[1840,2852,1936],{"className":2853},[1935],[1840,2855,2857],{"className":2856},[1908],[1840,2858,2860],{"className":2859,"style":1943},[1912],[1840,2861],{},[1840,2863],{"className":2864,"style":2091},[2090],[1840,2866,2005],{"className":2867},[2095],[1840,2869],{"className":2870,"style":2091},[2090],[1840,2872,2874,2878,2882,2885,2888],{"className":2873},[1881],[1840,2875],{"className":2876,"style":2877},[1885],"height:0.6667em;vertical-align:-0.0833em;",[1840,2879,2707],{"className":2880,"style":2881},[1890,1894],"margin-right:0.0037em;",[1840,2883],{"className":2884,"style":2478},[2090],[1840,2886,2710],{"className":2887},[2525],[1840,2889],{"className":2890,"style":2478},[2090],[1840,2892,2894,2897,2900,2940,2943,2946],{"className":2893},[1881],[1840,2895],{"className":2896,"style":1886},[1885],[1840,2898,2230],{"className":2899,"style":2341},[1890,1894],[1840,2901,2903,2906],{"className":2902},[1890],[1840,2904,1999],{"className":2905,"style":2051},[1890,1894],[1840,2907,2909],{"className":2908},[1899],[1840,2910,2912,2932],{"className":2911},[1903,1904],[1840,2913,2915,2929],{"className":2914},[1908],[1840,2916,2918],{"className":2917,"style":1913},[1912],[1840,2919,2920,2923],{"style":2066},[1840,2921],{"className":2922,"style":1921},[1920],[1840,2924,2926],{"className":2925},[1925,1926,1927,1928],[1840,2927,1867],{"className":2928},[1890,1894,1928],[1840,2930,1936],{"className":2931},[1935],[1840,2933,2935],{"className":2934},[1908],[1840,2936,2938],{"className":2937,"style":1943},[1912],[1840,2939],{},[1840,2941],{"className":2942,"style":2478},[2090],[1840,2944,2710],{"className":2945},[2525],[1840,2947],{"className":2948,"style":2478},[2090],[1840,2950,2952,2955,2998,3001,3041,3044,3047],{"className":2951},[1881],[1840,2953],{"className":2954,"style":2105},[1885],[1840,2956,2958,2962],{"className":2957},[1890],[1840,2959,2725],{"className":2960,"style":2961},[1890,1894],"margin-right:0.0528em;",[1840,2963,2965],{"className":2964},[1899],[1840,2966,2968,2990],{"className":2967},[1903,1904],[1840,2969,2971,2987],{"className":2970},[1908],[1840,2972,2975],{"className":2973,"style":2974},[1912],"height:0.3011em;",[1840,2976,2978,2981],{"style":2977},"top:-2.55em;margin-left:-0.0528em;margin-right:0.05em;",[1840,2979],{"className":2980,"style":1921},[1920],[1840,2982,2984],{"className":2983},[1925,1926,1927,1928],[1840,2985,2010],{"className":2986},[1890,1928],[1840,2988,1936],{"className":2989},[1935],[1840,2991,2993],{"className":2992},[1908],[1840,2994,2996],{"className":2995,"style":1943},[1912],[1840,2997],{},[1840,2999,2730],{"className":3000},[2113],[1840,3002,3004,3007],{"className":3003},[1890],[1840,3005,1864],{"className":3006,"style":1895},[1890,1894],[1840,3008,3010],{"className":3009},[1899],[1840,3011,3013,3033],{"className":3012},[1903,1904],[1840,3014,3016,3030],{"className":3015},[1908],[1840,3017,3019],{"className":3018,"style":1913},[1912],[1840,3020,3021,3024],{"style":1916},[1840,3022],{"className":3023,"style":1921},[1920],[1840,3025,3027],{"className":3026},[1925,1926,1927,1928],[1840,3028,1867],{"className":3029},[1890,1894,1928],[1840,3031,1936],{"className":3032},[1935],[1840,3034,3036],{"className":3035},[1908],[1840,3037,3039],{"className":3038,"style":1943},[1912],[1840,3040],{},[1840,3042],{"className":3043,"style":2478},[2090],[1840,3045,2286],{"className":3046},[2525],[1840,3048],{"className":3049,"style":2478},[2090],[1840,3051,3053,3056,3059,3062,3065,3068],{"className":3052},[1881],[1840,3054],{"className":3055,"style":2105},[1885],[1840,3057,1961],{"className":3058},[1890,1894],[1840,3060,2743],{"className":3061},[2175],[1840,3063],{"className":3064,"style":2478},[2090],[1840,3066,2710],{"className":3067},[2525],[1840,3069],{"className":3070,"style":2478},[2090],[1840,3072,3074,3077,3117,3157,3160,3200,3203,3206],{"className":3073},[1881],[1840,3075],{"className":3076,"style":2105},[1885],[1840,3078,3080,3083],{"className":3079},[1890],[1840,3081,2725],{"className":3082,"style":2961},[1890,1894],[1840,3084,3086],{"className":3085},[1899],[1840,3087,3089,3109],{"className":3088},[1903,1904],[1840,3090,3092,3106],{"className":3091},[1908],[1840,3093,3095],{"className":3094,"style":2974},[1912],[1840,3096,3097,3100],{"style":2977},[1840,3098],{"className":3099,"style":1921},[1920],[1840,3101,3103],{"className":3102},[1925,1926,1927,1928],[1840,3104,2752],{"className":3105},[1890,1928],[1840,3107,1936],{"className":3108},[1935],[1840,3110,3112],{"className":3111},[1908],[1840,3113,3115],{"className":3114,"style":1943},[1912],[1840,3116],{},[1840,3118,3120,3123],{"className":3119},[1890],[1840,3121,1999],{"className":3122,"style":2051},[1890,1894],[1840,3124,3126],{"className":3125},[1899],[1840,3127,3129,3149],{"className":3128},[1903,1904],[1840,3130,3132,3146],{"className":3131},[1908],[1840,3133,3135],{"className":3134,"style":1913},[1912],[1840,3136,3137,3140],{"style":2066},[1840,3138],{"className":3139,"style":1921},[1920],[1840,3141,3143],{"className":3142},[1925,1926,1927,1928],[1840,3144,1867],{"className":3145},[1890,1894,1928],[1840,3147,1936],{"className":3148},[1935],[1840,3150,3152],{"className":3151},[1908],[1840,3153,3155],{"className":3154,"style":1943},[1912],[1840,3156],{},[1840,3158,2730],{"className":3159},[2113],[1840,3161,3163,3166],{"className":3162},[1890],[1840,3164,1864],{"className":3165,"style":1895},[1890,1894],[1840,3167,3169],{"className":3168},[1899],[1840,3170,3172,3192],{"className":3171},[1903,1904],[1840,3173,3175,3189],{"className":3174},[1908],[1840,3176,3178],{"className":3177,"style":1913},[1912],[1840,3179,3180,3183],{"style":1916},[1840,3181],{"className":3182,"style":1921},[1920],[1840,3184,3186],{"className":3185},[1925,1926,1927,1928],[1840,3187,1867],{"className":3188},[1890,1894,1928],[1840,3190,1936],{"className":3191},[1935],[1840,3193,3195],{"className":3194},[1908],[1840,3196,3198],{"className":3197,"style":1943},[1912],[1840,3199],{},[1840,3201],{"className":3202,"style":2478},[2090],[1840,3204,2286],{"className":3205},[2525],[1840,3207],{"className":3208,"style":2478},[2090],[1840,3210,3212,3215,3218,3221,3224,3227],{"className":3211},[1881],[1840,3213],{"className":3214,"style":2105},[1885],[1840,3216,1961],{"className":3217},[1890,1894],[1840,3219,2743],{"className":3220},[2175],[1840,3222],{"className":3223,"style":2478},[2090],[1840,3225,2710],{"className":3226},[2525],[1840,3228],{"className":3229,"style":2478},[2090],[1840,3231,3233,3236,3277,3281,3285,3288,3291,3331,3334,3337],{"className":3232},[1881],[1840,3234],{"className":3235,"style":2105},[1885],[1840,3237,3239,3242],{"className":3238},[1890],[1840,3240,2779],{"className":3241},[1890,1894],[1840,3243,3245],{"className":3244},[1899],[1840,3246,3248,3269],{"className":3247},[1903,1904],[1840,3249,3251,3266],{"className":3250},[1908],[1840,3252,3254],{"className":3253,"style":1913},[1912],[1840,3255,3257,3260],{"style":3256},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[1840,3258],{"className":3259,"style":1921},[1920],[1840,3261,3263],{"className":3262},[1925,1926,1927,1928],[1840,3264,1867],{"className":3265},[1890,1894,1928],[1840,3267,1936],{"className":3268},[1935],[1840,3270,3272],{"className":3271},[1908],[1840,3273,3275],{"className":3274,"style":1943},[1912],[1840,3276],{},[1840,3278,2784],{"className":3279},[3280],"mpunct",[1840,3282],{"className":3283,"style":3284},[2090],"margin-right:2em;",[1840,3286],{"className":3287,"style":2467},[2090],[1840,3289,2274],{"className":3290},[1890],[1840,3292,3294,3297],{"className":3293},[1890],[1840,3295,1864],{"className":3296,"style":1895},[1890,1894],[1840,3298,3300],{"className":3299},[1899],[1840,3301,3303,3323],{"className":3302},[1903,1904],[1840,3304,3306,3320],{"className":3305},[1908],[1840,3307,3309],{"className":3308,"style":1913},[1912],[1840,3310,3311,3314],{"style":1916},[1840,3312],{"className":3313,"style":1921},[1920],[1840,3315,3317],{"className":3316},[1925,1926,1927,1928],[1840,3318,1867],{"className":3319},[1890,1894,1928],[1840,3321,1936],{"className":3322},[1935],[1840,3324,3326],{"className":3325},[1908],[1840,3327,3329],{"className":3328,"style":1943},[1912],[1840,3330],{},[1840,3332],{"className":3333,"style":2478},[2090],[1840,3335,2286],{"className":3336},[2525],[1840,3338],{"className":3339,"style":2478},[2090],[1840,3341,3343,3346,3349,3352,3355,3358],{"className":3342},[1881],[1840,3344],{"className":3345,"style":2105},[1885],[1840,3347,1961],{"className":3348},[1890,1894],[1840,3350,2274],{"className":3351},[1890],[1840,3353],{"className":3354,"style":2091},[2090],[1840,3356,2804],{"className":3357},[2095],[1840,3359],{"className":3360,"style":2091},[2090],[1840,3362,3364,3367,3370],{"className":3363},[1881],[1840,3365],{"className":3366,"style":2418},[1885],[1840,3368,2666],{"className":3369},[1890,1894],[1840,3371,2032],{"className":3372},[1890],[1796,3374,3375,3376,3405],{},"两侧允许不同斜率；",[1840,3377,3379,3393],{"className":3378},[1843],[1840,3380,3382],{"className":3381},[1847],[1849,3383,3384],{"xmlns":1851},[1853,3385,3386,3390],{},[1856,3387,3388],{},[1862,3389,2230],{},[1869,3391,3392],{"encoding":1871},"\\tau",[1840,3394,3396],{"className":3395,"ariaHidden":1877},[1876],[1840,3397,3399,3402],{"className":3398},[1881],[1840,3400],{"className":3401,"style":1973},[1885],[1840,3403,2230],{"className":3404,"style":2341},[1890,1894]," 是阈值处截距跳跃。实践中常使用三角核，使距离阈值更近的观测权重更高。",[3407,3408,3409,3425],"table",{},[3410,3411,3412],"thead",{},[3413,3414,3415,3419,3422],"tr",{},[3416,3417,3418],"th",{},"带宽",[3416,3420,3421],{},"优点",[3416,3423,3424],{},"风险",[3426,3427,3428,3440],"tbody",{},[3413,3429,3430,3434,3437],{},[3431,3432,3433],"td",{},"小",[3431,3435,3436],{},"更接近局部随机比较",[3431,3438,3439],{},"样本少、方差大",[3413,3441,3442,3445,3448],{},[3431,3443,3444],{},"大",[3431,3446,3447],{},"精度更高",[3431,3449,3450],{},"函数形式偏误、混入远端单位",[1796,3452,3453],{},"应报告数据驱动带宽，同时展示合理替代带宽，而不是挑选最显著者。",[1807,3455,3457],{"id":3456},"_3-可运行案例奖学金阈值","3. 可运行案例：奖学金阈值",[1796,3459,3460],{},"模拟中未处理结果随成绩平滑变化，奖学金在 80 分处产生真实 5 分提升。代码用三角核局部线性回归比较不同带宽。",[3462,3463],"pyodide",{"code64":3464,"layout":3465,"locale":7,"packages":3466,"title":3467},"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","vertical","numpy","Python：局部线性 RDD 与带宽敏感性",[1796,3469,3470],{},"带宽变化时估计会波动；这不是要求所有结果完全相同，而是检查结论是否由一个任意窗口驱动。",[1807,3472,3474],{"id":3473},"_4-设计有效性的证据链","4. 设计有效性的证据链",[3476,3477,3478],"h3",{"id":3478},"运行变量能否被精确操纵",[1796,3480,3481],{},"若学生知道评分规则并能把 79.9 调整到 80.0，阈值两侧可能不再可比。应结合：",[3483,3484,3485,3488,3491,3494],"ul",{},[1817,3486,3487],{},"运行变量密度与堆积；",[1817,3489,3490],{},"制度上谁能看到、修改或复核分数；",[1817,3492,3493],{},"阈值附近个体特征是否跳跃；",[1817,3495,3496],{},"报名、缺失或样本进入是否在阈值处改变。",[1796,3498,3499],{},"密度检验不显著不能证明无操纵；行政流程证据同样重要。",[3476,3501,3502],{"id":3502},"其他政策是否共享阈值",[1796,3504,3505],{},"若 80 分同时决定奖学金、宿舍和导师资格，结果跳跃是政策束的效应，不能单独归因于现金奖学金。",[3476,3507,3508],{"id":3508},"结果是否按同一规则测量",[1796,3510,3511],{},"阈值以上学生若被更频繁追踪，结果缺失本身也可能跳跃。",[1807,3513,3515],{"id":3514},"_5-sharpfuzzy-与其他设计","5. Sharp、fuzzy 与其他设计",[3407,3517,3518,3531],{},[3410,3519,3520],{},[3413,3521,3522,3525,3528],{},[3416,3523,3524],{},"设计",[3416,3526,3527],{},"阈值改变什么",[3416,3529,3530],{},"目标",[3426,3532,3533,3544,3555,3566],{},[3413,3534,3535,3538,3541],{},[3431,3536,3537],{},"sharp RDD",[3431,3539,3540],{},"处理从 0 跳到 1",[3431,3542,3543],{},"阈值处局部处理效应",[3413,3545,3546,3549,3552],{},[3431,3547,3548],{},"fuzzy RDD",[3431,3550,3551],{},"处理概率跳跃",[3431,3553,3554],{},"阈值附近顺从者 LATE",[3413,3556,3557,3560,3563],{},[3431,3558,3559],{},"regression kink",[3431,3561,3562],{},"处理强度斜率改变",[3431,3564,3565],{},"阈值处边际效应",[3413,3567,3568,3571,3574],{},[3431,3569,3570],{},"donut RDD",[3431,3572,3573],{},"删除最靠近阈值观测",[3431,3575,3576],{},"对 heaping\u002F操纵的敏感性分析",[1796,3578,3579],{},"fuzzy RDD 的 Wald 比率是“结果跳跃 \u002F 处理概率跳跃”，因此继承 IV 的排除限制、单调性与弱第一阶段问题。",[1807,3581,3583],{"id":3582},"_6-推断与报告","6. 推断与报告",[1796,3585,3586],{},"最低报告：",[3483,3588,3589,3592,3595,3598,3601,3604],{},[1817,3590,3591],{},"阈值规则、运行变量精度与是否可操纵；",[1817,3593,3594],{},"原始散点或分箱图，但估计不依赖视觉拟合；",[1817,3596,3597],{},"主带宽、核函数、局部多项式阶数和偏差校正区间；",[1817,3599,3600],{},"两侧样本量与有效观测；",[1817,3602,3603],{},"密度、处理前协变量和伪阈值检查；",[1817,3605,3606],{},"局部 estimand 与外推边界。",[1796,3608,3609],{},"不要使用高阶全局多项式跨越整个运行变量范围；它容易在边界产生不稳定形状和虚假跳跃。",[1807,3611,3612],{"id":3612},"课堂任务",[1796,3614,3615],{},"评估“年龄满 65 岁获得医疗福利”：",[1814,3617,3618,3621,3624,3627,3630],{},[1817,3619,3620],{},"明确运行变量的时间单位；",[1817,3622,3623],{},"列出同时在 65 岁发生的其他制度变化；",[1817,3625,3626],{},"设计一个处理前协变量连续性检查；",[1817,3628,3629],{},"说明为何 64 岁 11 个月与 65 岁 1 个月仍可能受季节影响；",[1817,3631,3632],{},"判断估计能否推广到 40 岁人群。",[1807,3634,3635],{"id":3635},"核心阅读",[3483,3637,3638,3652,3660],{},[1817,3639,3640,3641,3651],{},"Cattaneo, Idrobo & Titiunik, ",[3642,3643,3647],"a",{"href":3644,"rel":3645},"https:\u002F\u002Fdoi.org\u002F10.1017\u002F9781108684606",[3646],"nofollow",[3648,3649,3650],"em",{},"A Practical Introduction to Regression Discontinuity Designs","。",[1817,3653,3654,3655,3651],{},"Cattaneo, Jansson & Ma (2020), ",[3642,3656,3659],{"href":3657,"rel":3658},"https:\u002F\u002Fdoi.org\u002F10.1080\u002F01621459.2019.1635480",[3646],"“Simple Local Polynomial Density Estimators”",[1817,3661,3662,3663,3651],{},"Gelman & Imbens (2019), ",[3642,3664,3667],{"href":3665,"rel":3666},"https:\u002F\u002Fdoi.org\u002F10.1080\u002F07350015.2017.1366909",[3646],"“Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs”",[1796,3669,3670,3671,3675,3676,3651],{},"上一章：",[3642,3672,3674],{"href":3673},"..\u002F05-did\u002F","双重差分法","｜下一章：",[3642,3677,3679],{"href":3678},"..\u002F07-matching\u002F","匹配与倾向得分",{"title":10,"searchDepth":3681,"depth":3681,"links":3682},2,[3683,3684,3685,3686,3687,3693,3694,3695,3696],{"id":1809,"depth":3681,"text":1809},{"id":1834,"depth":3681,"text":1835},{"id":2647,"depth":3681,"text":2648},{"id":3456,"depth":3681,"text":3457},{"id":3473,"depth":3681,"text":3474,"children":3688},[3689,3691,3692],{"id":3478,"depth":3690,"text":3478},3,{"id":3502,"depth":3690,"text":3502},{"id":3508,"depth":3690,"text":3508},{"id":3514,"depth":3681,"text":3515},{"id":3582,"depth":3681,"text":3583},{"id":3612,"depth":3681,"text":3612},{"id":3635,"depth":3681,"text":3635},"用阈值附近的局部比较理解连续性、带宽、操纵检验、局部多项式与模糊断点。","md",{"sidebar":3700},{"order":3701},6,true,{"title":1504,"description":3697},"MFRl6Tnkn9n2WQdbjm_j-glfdDTceD7AO9SczDsCVdw",[3706,3708],{"title":1498,"path":1499,"stem":1500,"description":3707,"children":-1},"从二乘二设计进入事件研究与交错实施，聚焦平行趋势、动态效应和有效比较组。",{"title":1510,"path":1511,"stem":1512,"description":3709,"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。",1785754751773]