[{"data":1,"prerenderedAt":4943},["ShallowReactive",2],{"navigation_docs":3,"learnalog_locale_counterpart__en_microeconometrics_02-ols":1782,"-zh-microeconometrics-02-ols":1783,"-zh-microeconometrics-02-ols-surround":4938},[4,1038],{"title":5,"path":6,"stem":7,"children":8},"En","\u002Fen","en",[9,12,58,250,406,477,534,715,798,824,943],{"title":10,"path":6,"stem":11},"","en\u002Findex",{"title":13,"path":14,"stem":15,"children":16},"Research Skills and Academic Writing","\u002Fen\u002Facademic-writing","en\u002Facademic-writing\u002Findex",[17,18,22,26,30,34,38,42,46,50,54],{"title":13,"path":14,"stem":15},{"title":19,"path":20,"stem":21},"1. Research Questions, Scope, and Feasibility","\u002Fen\u002Facademic-writing\u002F01-research-questions-and-planning","en\u002Facademic-writing\u002F01-research-questions-and-planning",{"title":23,"path":24,"stem":25},"2. 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 Visualizations","\u002Fen\u002Fplayground\u002F05-chartjs","en\u002Fplayground\u002F05-chartjs",{"title":825,"path":826,"stem":827,"children":828,"page":249},"Statistics For Insurance","\u002Fen\u002Fstatistics-for-insurance","en\u002Fstatistics-for-insurance",[829,835,857,883,905,927,937],{"title":830,"path":831,"stem":832,"children":833},"Statistics for General Insurance","\u002Fen\u002Fstatistics-for-insurance\u002F01-intro","en\u002Fstatistics-for-insurance\u002F01-intro\u002Findex",[834],{"title":830,"path":831,"stem":832},{"title":836,"path":837,"stem":838,"children":839},"Claims Development and Reserving","\u002Fen\u002Fstatistics-for-insurance\u002F02-chain-ladder","en\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002Findex",[840,841,845,849,853],{"title":836,"path":837,"stem":838},{"title":842,"path":843,"stem":844},"Basic Chain Ladder","\u002Fen\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F01-basic-chain","en\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F01-basic-chain",{"title":846,"path":847,"stem":848},"Frequency–Severity Reserving","\u002Fen\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F02-average-per-claim","en\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F02-average-per-claim",{"title":850,"path":851,"stem":852},"Bornhuetter–Ferguson Method","\u002Fen\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F03-b-f-method","en\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F03-b-f-method",{"title":854,"path":855,"stem":856},"Uncertainty and Diagnostics","\u002Fen\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F04-uncertainty-diagnostics","en\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F04-uncertainty-diagnostics",{"title":858,"path":859,"stem":860,"children":861},"Frequency and Severity Models","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002Findex",[862,863,867,871,875,879],{"title":858,"path":859,"stem":860},{"title":864,"path":865,"stem":866},"Core Severity Models","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist\u002F01-loss-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002F01-loss-dist",{"title":868,"path":869,"stem":870},"Tail Models and Extreme Values","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist\u002F02-more-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002F02-more-dist",{"title":872,"path":873,"stem":874},"Claim Count Models","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist\u002F03-case-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002F03-case-dist",{"title":876,"path":877,"stem":878},"Fitting and Validating Loss Models","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist\u002F04-fit-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002F04-fit-dist",{"title":880,"path":881,"stem":882},"Mixtures and Heterogeneity","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist\u002F05-mix-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002F05-mix-dist",{"title":884,"path":885,"stem":886,"children":887},"Reinsurance as a Loss Transformation","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002Findex",[888,889,893,897,901],{"title":884,"path":885,"stem":886},{"title":890,"path":891,"stem":892},"Proportional Reinsurance","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F01-proportional","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F01-proportional",{"title":894,"path":895,"stem":896},"Excess-of-Loss Reinsurance","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F02-excess","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F02-excess",{"title":898,"path":899,"stem":900},"Inflation and Layer Erosion","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F03-inflation","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F03-inflation",{"title":902,"path":903,"stem":904},"Reinsurance Decision Lab","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F04-examples","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F04-examples",{"title":906,"path":907,"stem":908,"children":909},"Aggregate Risk and Capital","\u002Fen\u002Fstatistics-for-insurance\u002F05-risk-theory","en\u002Fstatistics-for-insurance\u002F05-risk-theory\u002Findex",[910,911,915,919,923],{"title":906,"path":907,"stem":908},{"title":912,"path":913,"stem":914},"Collective Risk Model","\u002Fen\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F01-collective","en\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F01-collective",{"title":916,"path":917,"stem":918},"Individual Risk Model","\u002Fen\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F02-individual","en\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F02-individual",{"title":920,"path":921,"stem":922},"Aggregate Risk Computation Lab","\u002Fen\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F03-examples","en\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F03-examples",{"title":924,"path":925,"stem":926},"Tail Risk, Dependence, and Capital","\u002Fen\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F04-tail-capital","en\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F04-tail-capital",{"title":928,"path":929,"stem":930,"children":931},"Surplus and Ruin Theory","\u002Fen\u002Fstatistics-for-insurance\u002F06-ruin-theory","en\u002Fstatistics-for-insurance\u002F06-ruin-theory\u002Findex",[932,933],{"title":928,"path":929,"stem":930},{"title":934,"path":935,"stem":936},"Finite-Time Ruin Simulation","\u002Fen\u002Fstatistics-for-insurance\u002F06-ruin-theory\u002F01-finite-time-simulation","en\u002Fstatistics-for-insurance\u002F06-ruin-theory\u002F01-finite-time-simulation",{"title":938,"path":939,"stem":940,"children":941},"Portfolio Risk Capstone","\u002Fen\u002Fstatistics-for-insurance\u002F07-capstone","en\u002Fstatistics-for-insurance\u002F07-capstone\u002Findex",[942],{"title":938,"path":939,"stem":940},{"title":944,"path":945,"stem":946,"children":947,"page":249},"Time Series","\u002Fen\u002Ftime-series","en\u002Ftime-series",[948,958,964,970,976,982,988,994,1016],{"title":949,"path":950,"stem":951,"children":952},"Classical Time Series — Course Guide","\u002Fen\u002Ftime-series\u002F00-intro","en\u002Ftime-series\u002F00-intro\u002Findex",[953,954],{"title":949,"path":950,"stem":951},{"title":955,"path":956,"stem":957},"Preparation — Stationarity in 30 Minutes","\u002Fen\u002Ftime-series\u002F00-intro\u002F01-stationary","en\u002Ftime-series\u002F00-intro\u002F01-stationary",{"title":959,"path":960,"stem":961,"children":962},"Module 1 — Processes, Dependence, and Stationarity","\u002Fen\u002Ftime-series\u002F01-stochastic-process","en\u002Ftime-series\u002F01-stochastic-process\u002Findex",[963],{"title":959,"path":960,"stem":961},{"title":965,"path":966,"stem":967,"children":968},"Module 2 — ARMA, ARIMA, and Model Identification","\u002Fen\u002Ftime-series\u002F02-arma","en\u002Ftime-series\u002F02-arma\u002Findex",[969],{"title":965,"path":966,"stem":967},{"title":971,"path":972,"stem":973,"children":974},"Module 3 — Linear Prediction and State-Space Recursions","\u002Fen\u002Ftime-series\u002F03-prediction","en\u002Ftime-series\u002F03-prediction\u002Findex",[975],{"title":971,"path":972,"stem":973},{"title":977,"path":978,"stem":979,"children":980},"Module 4 — Estimation, Likelihood, and Inference","\u002Fen\u002Ftime-series\u002F04-estimation","en\u002Ftime-series\u002F04-estimation\u002Findex",[981],{"title":977,"path":978,"stem":979},{"title":983,"path":984,"stem":985,"children":986},"Module 5 — Systems, Seasonality, and Cointegration","\u002Fen\u002Ftime-series\u002F05-multi-ar","en\u002Ftime-series\u002F05-multi-ar\u002Findex",[987],{"title":983,"path":984,"stem":985},{"title":989,"path":990,"stem":991,"children":992},"Module 6 — Spectral Analysis, Cycles, and Filters","\u002Fen\u002Ftime-series\u002F06-spectral-analysis","en\u002Ftime-series\u002F06-spectral-analysis\u002Findex",[993],{"title":989,"path":990,"stem":991},{"title":995,"path":996,"stem":997,"children":998},"R Matrix Laboratory","\u002Fen\u002Ftime-series\u002F07-r-implementation","en\u002Ftime-series\u002F07-r-implementation\u002Findex",[999,1000,1004,1008,1012],{"title":995,"path":996,"stem":997},{"title":1001,"path":1002,"stem":1003},"R Matrix Lab 1 — Covariance Geometry","\u002Fen\u002Ftime-series\u002F07-r-implementation\u002F01-covariance-matrices","en\u002Ftime-series\u002F07-r-implementation\u002F01-covariance-matrices",{"title":1005,"path":1006,"stem":1007},"R Matrix Lab 2 — AR Recursions and Yule–Walker Equations","\u002Fen\u002Ftime-series\u002F07-r-implementation\u002F02-ar-recursions","en\u002Ftime-series\u002F07-r-implementation\u002F02-ar-recursions",{"title":1009,"path":1010,"stem":1011},"R Matrix Lab 3 — Prediction and Gaussian Likelihood","\u002Fen\u002Ftime-series\u002F07-r-implementation\u002F03-prediction-likelihood","en\u002Ftime-series\u002F07-r-implementation\u002F03-prediction-likelihood",{"title":1013,"path":1014,"stem":1015},"R Matrix Lab 4 — State Space and Kalman Filtering","\u002Fen\u002Ftime-series\u002F07-r-implementation\u002F04-state-space","en\u002Ftime-series\u002F07-r-implementation\u002F04-state-space",{"title":1017,"path":1018,"stem":1019,"children":1020},"Optional Python Appendix — Empirical Forecasting","\u002Fen\u002Ftime-series\u002F08-python-implementation","en\u002Ftime-series\u002F08-python-implementation\u002Findex",[1021,1022,1026,1030,1034],{"title":1017,"path":1018,"stem":1019},{"title":1023,"path":1024,"stem":1025},"Optional Python Lab 1 — Explore Before Modeling","\u002Fen\u002Ftime-series\u002F08-python-implementation\u002F01-exploration","en\u002Ftime-series\u002F08-python-implementation\u002F01-exploration",{"title":1027,"path":1028,"stem":1029},"Optional Python Lab 2 — Diagnose Stationarity","\u002Fen\u002Ftime-series\u002F08-python-implementation\u002F02-stationarity","en\u002Ftime-series\u002F08-python-implementation\u002F02-stationarity",{"title":1031,"path":1032,"stem":1033},"Optional Python Lab 3 — Fit and Audit ARMA Errors","\u002Fen\u002Ftime-series\u002F08-python-implementation\u002F03-arma-fitting","en\u002Ftime-series\u002F08-python-implementation\u002F03-arma-fitting",{"title":1035,"path":1036,"stem":1037},"Optional Python Lab 4 — Forecast, Backtest, and Monitor","\u002Fen\u002Ftime-series\u002F08-python-implementation\u002F04-forecasting","en\u002Ftime-series\u002F08-python-implementation\u002F04-forecasting",{"title":1039,"path":1040,"stem":1041,"children":1042},"Zh","\u002Fzh","zh",[1043,1045,1063,1247,1323,1365,1421,1467,1543,1625,1633,1731],{"title":10,"path":1040,"stem":1044},"zh\u002Findex",{"title":1046,"path":1047,"stem":1048,"children":1049},"学术写作与文献综述","\u002Fzh\u002Facademic-writing","zh\u002Facademic-writing\u002Findex",[1050,1051,1055,1059],{"title":1046,"path":1047,"stem":1048},{"title":1052,"path":1053,"stem":1054},"4. 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. 从分块结构到问题链：文献综述示例","\u002Fzh\u002Facademic-writing\u002F10-从分块结构到问题链-文献综述示例","zh\u002Facademic-writing\u002F10-从分块结构到问题链-文献综述示例",{"title":59,"path":1064,"stem":1065,"children":1066,"page":249},"\u002Fzh\u002Faccounting","zh\u002Faccounting",[1067,1073,1087,1191],{"title":1068,"path":1069,"stem":1070,"children":1071},"会计学学习路线图","\u002Fzh\u002Faccounting\u002F00-index","zh\u002Faccounting\u002F00-index",[1072],{"title":1068,"path":1069,"stem":1070},{"title":1074,"path":1075,"stem":1076,"children":1077},"附录","\u002Fzh\u002Faccounting\u002Fappendix","zh\u002Faccounting\u002Fappendix\u002Findex",[1078,1079,1083],{"title":1074,"path":1075,"stem":1076},{"title":1080,"path":1081,"stem":1082},"综合示例与常见陷阱","\u002Fzh\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls","zh\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls",{"title":1084,"path":1085,"stem":1086},"会计术语速查表","\u002Fzh\u002Faccounting\u002Fappendix\u002F26-glossary","zh\u002Faccounting\u002Fappendix\u002F26-glossary",{"title":1088,"path":1089,"stem":1090,"children":1091},"金融会计","\u002Fzh\u002Faccounting\u002Ffinancial-accounting","zh\u002Faccounting\u002Ffinancial-accounting\u002Findex",[1092,1093,1111,1125,1159,1173],{"title":1088,"path":1089,"stem":1090},{"title":1094,"path":1095,"stem":1096,"children":1097},"1. 基础","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations","zh\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002Findex",[1098,1099,1103,1107],{"title":1094,"path":1095,"stem":1096},{"title":1100,"path":1101,"stem":1102},"会计信息目标与质量特征","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics","zh\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics",{"title":1104,"path":1105,"stem":1106},"会计等式与要素","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements","zh\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements",{"title":1108,"path":1109,"stem":1110},"记账基础与原则","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles","zh\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles",{"title":1112,"path":1113,"stem":1114,"children":1115},"2. 交易记录","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions","zh\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002Findex",[1116,1117,1121],{"title":1112,"path":1113,"stem":1114},{"title":1118,"path":1119,"stem":1120},"复式记账与借贷规则","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr","zh\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr",{"title":1122,"path":1123,"stem":1124},"会计循环","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle","zh\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle",{"title":1126,"path":1127,"stem":1128,"children":1129},"3. 计量与调整","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments","zh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002Findex",[1130,1131,1135,1139,1143,1147,1151,1155],{"title":1126,"path":1127,"stem":1128},{"title":1132,"path":1133,"stem":1134},"收入确认","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition","zh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition",{"title":1136,"path":1137,"stem":1138},"存货","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory","zh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory",{"title":1140,"path":1141,"stem":1142},"应收账款","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables","zh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables",{"title":1144,"path":1145,"stem":1146},"固定资产","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe","zh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe",{"title":1148,"path":1149,"stem":1150},"无形资产","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles","zh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles",{"title":1152,"path":1153,"stem":1154},"租赁","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases","zh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases",{"title":1156,"path":1157,"stem":1158},"所得税","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax","zh\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax",{"title":1160,"path":1161,"stem":1162,"children":1163},"4. 报表与现金","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash","zh\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002Findex",[1164,1165,1169],{"title":1160,"path":1161,"stem":1162},{"title":1166,"path":1167,"stem":1168},"财务报表","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements","zh\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements",{"title":1170,"path":1171,"stem":1172},"现金控制","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control","zh\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control",{"title":1174,"path":1175,"stem":1176,"children":1177},"5. 分析与比较","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison","zh\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002Findex",[1178,1179,1183,1187],{"title":1174,"path":1175,"stem":1176},{"title":1180,"path":1181,"stem":1182},"财务比率分析","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis","zh\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis",{"title":1184,"path":1185,"stem":1186},"IFRS 与 US GAAP 对比","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap","zh\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap",{"title":1188,"path":1189,"stem":1190},"2026 准则更新与报告案例","\u002Fzh\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F27-current-standards-2026","zh\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F27-current-standards-2026",{"title":1192,"path":1193,"stem":1194,"children":1195},"管理会计","\u002Fzh\u002Faccounting\u002Fmanagement-accounting","zh\u002Faccounting\u002Fmanagement-accounting\u002Findex",[1196,1197,1215,1233],{"title":1192,"path":1193,"stem":1194},{"title":1198,"path":1199,"stem":1200,"children":1201},"1. 成本基础","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations","zh\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002Findex",[1202,1203,1207,1211],{"title":1198,"path":1199,"stem":1200},{"title":1204,"path":1205,"stem":1206},"成本概念","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts","zh\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts",{"title":1208,"path":1209,"stem":1210},"成本核算系统","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems","zh\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems",{"title":1212,"path":1213,"stem":1214},"变动成本法 vs. 吸收成本法","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption","zh\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption",{"title":1216,"path":1217,"stem":1218,"children":1219},"2. 计划与控制","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control","zh\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002Findex",[1220,1221,1225,1229],{"title":1216,"path":1217,"stem":1218},{"title":1222,"path":1223,"stem":1224},"本量利分析","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis","zh\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis",{"title":1226,"path":1227,"stem":1228},"预算与差异分析","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances","zh\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances",{"title":1230,"path":1231,"stem":1232},"绩效评价","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement","zh\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement",{"title":1234,"path":1235,"stem":1236,"children":1237},"3. 决策与投资","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment","zh\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002Findex",[1238,1239,1243],{"title":1234,"path":1235,"stem":1236},{"title":1240,"path":1241,"stem":1242},"短期决策","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions","zh\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions",{"title":1244,"path":1245,"stem":1246},"资本预算","\u002Fzh\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting","zh\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting",{"title":1248,"path":1249,"stem":1250,"children":1251},"资产定价理论","\u002Fzh\u002Fasset-pricing","zh\u002Fasset-pricing\u002Findex",[1252,1253,1259,1265,1271,1277,1283,1289,1295,1301,1307,1313,1319],{"title":1248,"path":1249,"stem":1250},{"title":1254,"path":1255,"stem":1256,"children":1257},"第一章：引言与基础","\u002Fzh\u002Fasset-pricing\u002F01-intro","zh\u002Fasset-pricing\u002F01-intro\u002Findex",[1258],{"title":1254,"path":1255,"stem":1256},{"title":1260,"path":1261,"stem":1262,"children":1263},"第二章：效用理论与风险偏好","\u002Fzh\u002Fasset-pricing\u002F02-utility","zh\u002Fasset-pricing\u002F02-utility\u002Findex",[1264],{"title":1260,"path":1261,"stem":1262},{"title":1266,"path":1267,"stem":1268,"children":1269},"第三章：均值-方差分析","\u002Fzh\u002Fasset-pricing\u002F03-mean-variance","zh\u002Fasset-pricing\u002F03-mean-variance\u002Findex",[1270],{"title":1266,"path":1267,"stem":1268},{"title":1272,"path":1273,"stem":1274,"children":1275},"第四章：资本资产定价模型(CAPM)","\u002Fzh\u002Fasset-pricing\u002F04-capm","zh\u002Fasset-pricing\u002F04-capm\u002Findex",[1276],{"title":1272,"path":1273,"stem":1274},{"title":1278,"path":1279,"stem":1280,"children":1281},"第五章：因子模型","\u002Fzh\u002Fasset-pricing\u002F05-factor-models","zh\u002Fasset-pricing\u002F05-factor-models\u002Findex",[1282],{"title":1278,"path":1279,"stem":1280},{"title":1284,"path":1285,"stem":1286,"children":1287},"第六章：跨期资产定价","\u002Fzh\u002Fasset-pricing\u002F06-intertemporal","zh\u002Fasset-pricing\u002F06-intertemporal\u002Findex",[1288],{"title":1284,"path":1285,"stem":1286},{"title":1290,"path":1291,"stem":1292,"children":1293},"第七章：期权定价理论","\u002Fzh\u002Fasset-pricing\u002F07-options","zh\u002Fasset-pricing\u002F07-options\u002Findex",[1294],{"title":1290,"path":1291,"stem":1292},{"title":1296,"path":1297,"stem":1298,"children":1299},"第八章：固定收益证券","\u002Fzh\u002Fasset-pricing\u002F08-fixed-income","zh\u002Fasset-pricing\u002F08-fixed-income\u002Findex",[1300],{"title":1296,"path":1297,"stem":1298},{"title":1302,"path":1303,"stem":1304,"children":1305},"第九章：市场有效性与异象","\u002Fzh\u002Fasset-pricing\u002F09-efficiency","zh\u002Fasset-pricing\u002F09-efficiency\u002Findex",[1306],{"title":1302,"path":1303,"stem":1304},{"title":1308,"path":1309,"stem":1310,"children":1311},"第十章：数值方法与实证应用","\u002Fzh\u002Fasset-pricing\u002F10-empirical","zh\u002Fasset-pricing\u002F10-empirical\u002Findex",[1312],{"title":1308,"path":1309,"stem":1310},{"title":1314,"path":1315,"stem":1316,"children":1317},"第十一章：资产定价浏览器交互实验","\u002Fzh\u002Fasset-pricing\u002F11-interactive-labs","zh\u002Fasset-pricing\u002F11-interactive-labs\u002Findex",[1318],{"title":1314,"path":1315,"stem":1316},{"title":1320,"path":1321,"stem":1322},"第十二章：前沿文献与现代资产定价案例（2023—2026）","\u002Fzh\u002Fasset-pricing\u002F12-frontier-literature-2026","zh\u002Fasset-pricing\u002F12-frontier-literature-2026",{"title":1324,"path":1325,"stem":1326,"children":1327},"计量经济学","\u002Fzh\u002Feconometrics","zh\u002Feconometrics\u002Findex",[1328,1329,1333,1337,1341,1345,1349,1353,1357,1361],{"title":1324,"path":1325,"stem":1326},{"title":1330,"path":1331,"stem":1332},"第一章：数据、概率与回归对象","\u002Fzh\u002Feconometrics\u002F01-data-and-regression","zh\u002Feconometrics\u002F01-data-and-regression",{"title":1334,"path":1335,"stem":1336},"第二章：OLS、矩阵与几何解释","\u002Fzh\u002Feconometrics\u002F02-ols-and-geometry","zh\u002Feconometrics\u002F02-ols-and-geometry",{"title":1338,"path":1339,"stem":1340},"第三章：统计推断与稳健标准误","\u002Fzh\u002Feconometrics\u002F03-inference-and-robustness","zh\u002Feconometrics\u002F03-inference-and-robustness",{"title":1342,"path":1343,"stem":1344},"第四章：内生性、工具变量与两阶段最小二乘","\u002Fzh\u002Feconometrics\u002F04-endogeneity-and-iv","zh\u002Feconometrics\u002F04-endogeneity-and-iv",{"title":1346,"path":1347,"stem":1348},"第五章：面板数据与时间序列","\u002Fzh\u002Feconometrics\u002F05-panel-and-time-series","zh\u002Feconometrics\u002F05-panel-and-time-series",{"title":1350,"path":1351,"stem":1352},"第六章：估计方法与因果设计的共同基础","\u002Fzh\u002Feconometrics\u002F06-estimation-and-causal-design","zh\u002Feconometrics\u002F06-estimation-and-causal-design",{"title":1354,"path":1355,"stem":1356},"第七章：可重复计量实证项目","\u002Fzh\u002Feconometrics\u002F07-reproducible-project","zh\u002Feconometrics\u002F07-reproducible-project",{"title":1358,"path":1359,"stem":1360},"第八章：计量经济学浏览器回归实验","\u002Fzh\u002Feconometrics\u002F08-interactive-regression-labs","zh\u002Feconometrics\u002F08-interactive-regression-labs",{"title":1362,"path":1363,"stem":1364},"第九章：前沿文献与现代计量案例（2023—2026）","\u002Fzh\u002Feconometrics\u002F09-frontier-literature-2026","zh\u002Feconometrics\u002F09-frontier-literature-2026",{"title":478,"path":1366,"stem":1367,"children":1368,"page":249},"\u002Fzh\u002Fintro-to-economics","zh\u002Fintro-to-economics",[1369,1373,1377,1381,1385,1389,1393,1397,1401,1405,1409,1413,1417],{"title":1370,"path":1371,"stem":1372},"经济学导论 (微观与宏观)","\u002Fzh\u002Fintro-to-economics\u002F00-intro","zh\u002Fintro-to-economics\u002F00-intro",{"title":1374,"path":1375,"stem":1376},"第1章：经济学基础原理","\u002Fzh\u002Fintro-to-economics\u002F01-foundations","zh\u002Fintro-to-economics\u002F01-foundations",{"title":1378,"path":1379,"stem":1380},"第2章：需求与供给","\u002Fzh\u002Fintro-to-economics\u002F02-demand-and-supply","zh\u002Fintro-to-economics\u002F02-demand-and-supply",{"title":1382,"path":1383,"stem":1384},"第3章：弹性","\u002Fzh\u002Fintro-to-economics\u002F03-elasticity","zh\u002Fintro-to-economics\u002F03-elasticity",{"title":1386,"path":1387,"stem":1388},"第4章：市场结构","\u002Fzh\u002Fintro-to-economics\u002F04-market-structures","zh\u002Fintro-to-economics\u002F04-market-structures",{"title":1390,"path":1391,"stem":1392},"第5章：GDP 与财富","\u002Fzh\u002Fintro-to-economics\u002F05-gdp-and-wealth","zh\u002Fintro-to-economics\u002F05-gdp-and-wealth",{"title":1394,"path":1395,"stem":1396},"第6章：通货膨胀与失业","\u002Fzh\u002Fintro-to-economics\u002F06-inflation-and-unemployment","zh\u002Fintro-to-economics\u002F06-inflation-and-unemployment",{"title":1398,"path":1399,"stem":1400},"第7章：经济增长","\u002Fzh\u002Fintro-to-economics\u002F07-economic-growth","zh\u002Fintro-to-economics\u002F07-economic-growth",{"title":1402,"path":1403,"stem":1404},"第8章：货币与银行","\u002Fzh\u002Fintro-to-economics\u002F08-money-and-banking","zh\u002Fintro-to-economics\u002F08-money-and-banking",{"title":1406,"path":1407,"stem":1408},"第9章：货币政策与 AD-AS 模型","\u002Fzh\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as","zh\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as",{"title":1410,"path":1411,"stem":1412},"第10章：财政政策","\u002Fzh\u002Fintro-to-economics\u002F10-fiscal-policy","zh\u002Fintro-to-economics\u002F10-fiscal-policy",{"title":1414,"path":1415,"stem":1416},"第11章：开放经济与汇率","\u002Fzh\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates","zh\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates",{"title":1418,"path":1419,"stem":1420},"第12章：综合复习与案例分析","\u002Fzh\u002Fintro-to-economics\u002F12-review-and-case-studies","zh\u002Fintro-to-economics\u002F12-review-and-case-studies",{"title":1422,"path":1423,"stem":1424,"children":1425},"宏观经济学","\u002Fzh\u002Fmacroeconomics","zh\u002Fmacroeconomics\u002Findex",[1426,1427,1431,1435,1439,1443,1447,1451,1455,1459,1463],{"title":1422,"path":1423,"stem":1424},{"title":1428,"path":1429,"stem":1430},"第一章：国民账户与宏观指标","\u002Fzh\u002Fmacroeconomics\u002F01-national-accounts","zh\u002Fmacroeconomics\u002F01-national-accounts",{"title":1432,"path":1433,"stem":1434},"第二章：消费、投资与凯恩斯交叉","\u002Fzh\u002Fmacroeconomics\u002F02-consumption-investment","zh\u002Fmacroeconomics\u002F02-consumption-investment",{"title":1436,"path":1437,"stem":1438},"第三章：货币、银行与货币政策","\u002Fzh\u002Fmacroeconomics\u002F03-money-and-monetary-policy","zh\u002Fmacroeconomics\u002F03-money-and-monetary-policy",{"title":1440,"path":1441,"stem":1442},"第四章：AD–AS、通货膨胀与失业","\u002Fzh\u002Fmacroeconomics\u002F04-ad-as-inflation-unemployment","zh\u002Fmacroeconomics\u002F04-ad-as-inflation-unemployment",{"title":1444,"path":1445,"stem":1446},"第五章：财政政策、债务与稳定化","\u002Fzh\u002Fmacroeconomics\u002F05-fiscal-policy-and-debt","zh\u002Fmacroeconomics\u002F05-fiscal-policy-and-debt",{"title":1448,"path":1449,"stem":1450},"第六章：经济增长、生产率与发展","\u002Fzh\u002Fmacroeconomics\u002F06-growth-productivity","zh\u002Fmacroeconomics\u002F06-growth-productivity",{"title":1452,"path":1453,"stem":1454},"第七章：开放经济、汇率与国际收支","\u002Fzh\u002Fmacroeconomics\u002F07-open-economy","zh\u002Fmacroeconomics\u002F07-open-economy",{"title":1456,"path":1457,"stem":1458},"第八章：宏观研究项目与政策分析","\u002Fzh\u002Fmacroeconomics\u002F08-macro-research-project","zh\u002Fmacroeconomics\u002F08-macro-research-project",{"title":1460,"path":1461,"stem":1462},"第九章：宏观经济学浏览器交互实验","\u002Fzh\u002Fmacroeconomics\u002F09-interactive-policy-labs","zh\u002Fmacroeconomics\u002F09-interactive-policy-labs",{"title":1464,"path":1465,"stem":1466},"第十章：前沿文献与当代宏观案例（2023—2026）","\u002Fzh\u002Fmacroeconomics\u002F10-frontier-literature-2026","zh\u002Fmacroeconomics\u002F10-frontier-literature-2026",{"title":1468,"path":1469,"stem":1470,"children":1471},"微观计量经济学","\u002Fzh\u002Fmicroeconometrics","zh\u002Fmicroeconometrics\u002Findex",[1472,1473,1479,1485,1491,1497,1503,1509,1515,1521,1527,1533,1539],{"title":1468,"path":1469,"stem":1470},{"title":1474,"path":1475,"stem":1476,"children":1477},"第一章：微观计量与因果推断导论","\u002Fzh\u002Fmicroeconometrics\u002F01-intro","zh\u002Fmicroeconometrics\u002F01-intro\u002Findex",[1478],{"title":1474,"path":1475,"stem":1476},{"title":1480,"path":1481,"stem":1482,"children":1483},"第二章：线性回归与 OLS","\u002Fzh\u002Fmicroeconometrics\u002F02-ols","zh\u002Fmicroeconometrics\u002F02-ols\u002Findex",[1484],{"title":1480,"path":1481,"stem":1482},{"title":1486,"path":1487,"stem":1488,"children":1489},"第三章：工具变量法","\u002Fzh\u002Fmicroeconometrics\u002F03-iv","zh\u002Fmicroeconometrics\u002F03-iv\u002Findex",[1490],{"title":1486,"path":1487,"stem":1488},{"title":1492,"path":1493,"stem":1494,"children":1495},"第四章：面板数据方法","\u002Fzh\u002Fmicroeconometrics\u002F04-panel","zh\u002Fmicroeconometrics\u002F04-panel\u002Findex",[1496],{"title":1492,"path":1493,"stem":1494},{"title":1498,"path":1499,"stem":1500,"children":1501},"第五章：双重差分法","\u002Fzh\u002Fmicroeconometrics\u002F05-did","zh\u002Fmicroeconometrics\u002F05-did\u002Findex",[1502],{"title":1498,"path":1499,"stem":1500},{"title":1504,"path":1505,"stem":1506,"children":1507},"第六章：断点回归设计","\u002Fzh\u002Fmicroeconometrics\u002F06-rdd","zh\u002Fmicroeconometrics\u002F06-rdd\u002Findex",[1508],{"title":1504,"path":1505,"stem":1506},{"title":1510,"path":1511,"stem":1512,"children":1513},"第七章：匹配、倾向得分与加权","\u002Fzh\u002Fmicroeconometrics\u002F07-matching","zh\u002Fmicroeconometrics\u002F07-matching\u002Findex",[1514],{"title":1510,"path":1511,"stem":1512},{"title":1516,"path":1517,"stem":1518,"children":1519},"第八章：离散选择模型","\u002Fzh\u002Fmicroeconometrics\u002F08-discrete-choice","zh\u002Fmicroeconometrics\u002F08-discrete-choice\u002Findex",[1520],{"title":1516,"path":1517,"stem":1518},{"title":1522,"path":1523,"stem":1524,"children":1525},"第九章：计数数据与受限因变量","\u002Fzh\u002Fmicroeconometrics\u002F09-count-limited","zh\u002Fmicroeconometrics\u002F09-count-limited\u002Findex",[1526],{"title":1522,"path":1523,"stem":1524},{"title":1528,"path":1529,"stem":1530,"children":1531},"第十章：合成控制法","\u002Fzh\u002Fmicroeconometrics\u002F10-synthetic-control","zh\u002Fmicroeconometrics\u002F10-synthetic-control\u002Findex",[1532],{"title":1528,"path":1529,"stem":1530},{"title":1534,"path":1535,"stem":1536,"children":1537},"第十一章：机器学习与因果推断","\u002Fzh\u002Fmicroeconometrics\u002F11-ml-causal","zh\u002Fmicroeconometrics\u002F11-ml-causal\u002Findex",[1538],{"title":1534,"path":1535,"stem":1536},{"title":1540,"path":1541,"stem":1542},"第十二章：前沿文献与现代微观案例（2024—2026）","\u002Fzh\u002Fmicroeconometrics\u002F12-frontier-literature-2026","zh\u002Fmicroeconometrics\u002F12-frontier-literature-2026",{"title":716,"path":1544,"stem":1545,"children":1546,"page":249},"\u002Fzh\u002Fmicroeconomics","zh\u002Fmicroeconomics",[1547,1553,1559,1565,1571,1577,1583,1589,1595,1601,1607,1613,1619],{"title":1548,"path":1549,"stem":1550,"children":1551},"微观经济学 III","\u002Fzh\u002Fmicroeconomics\u002F00-intro","zh\u002Fmicroeconomics\u002F00-intro\u002Findex",[1552],{"title":1548,"path":1549,"stem":1550},{"title":1554,"path":1555,"stem":1556,"children":1557},"消费者理论与分析基础","\u002Fzh\u002Fmicroeconomics\u002F01-fundations","zh\u002Fmicroeconomics\u002F01-fundations\u002Findex",[1558],{"title":1554,"path":1555,"stem":1556},{"title":1560,"path":1561,"stem":1562,"children":1563},"比较静态分析与福利测度","\u002Fzh\u002Fmicroeconomics\u002F02-comparative-statics","zh\u002Fmicroeconomics\u002F02-comparative-statics\u002Findex",[1564],{"title":1560,"path":1561,"stem":1562},{"title":1566,"path":1567,"stem":1568,"children":1569},"不确定性下的决策","\u002Fzh\u002Fmicroeconomics\u002F03-uncertainty","zh\u002Fmicroeconomics\u002F03-uncertainty\u002Findex",[1570],{"title":1566,"path":1567,"stem":1568},{"title":1572,"path":1573,"stem":1574,"children":1575},"一般均衡与福利经济学","\u002Fzh\u002Fmicroeconomics\u002F04-general-equilibrium","zh\u002Fmicroeconomics\u002F04-general-equilibrium\u002Findex",[1576],{"title":1572,"path":1573,"stem":1574},{"title":1578,"path":1579,"stem":1580,"children":1581},"博弈论：静态与动态博弈","\u002Fzh\u002Fmicroeconomics\u002F05-game-theory","zh\u002Fmicroeconomics\u002F05-game-theory\u002Findex",[1582],{"title":1578,"path":1579,"stem":1580},{"title":1584,"path":1585,"stem":1586,"children":1587},"寡头垄断与策略性市场行为","\u002Fzh\u002Fmicroeconomics\u002F06-oligopoly","zh\u002Fmicroeconomics\u002F06-oligopoly\u002Findex",[1588],{"title":1584,"path":1585,"stem":1586},{"title":1590,"path":1591,"stem":1592,"children":1593},"信息经济学：逆向选择与道德风险","\u002Fzh\u002Fmicroeconomics\u002F07-information-economics","zh\u002Fmicroeconomics\u002F07-information-economics\u002Findex",[1594],{"title":1590,"path":1591,"stem":1592},{"title":1596,"path":1597,"stem":1598,"children":1599},"机制设计与拍卖理论","\u002Fzh\u002Fmicroeconomics\u002F08-mechanism-design","zh\u002Fmicroeconomics\u002F08-mechanism-design\u002Findex",[1600],{"title":1596,"path":1597,"stem":1598},{"title":1602,"path":1603,"stem":1604,"children":1605},"行为与实验微观经济学","\u002Fzh\u002Fmicroeconomics\u002F09-behavioural-economics","zh\u002Fmicroeconomics\u002F09-behavioural-economics\u002Findex",[1606],{"title":1602,"path":1603,"stem":1604},{"title":1608,"path":1609,"stem":1610,"children":1611},"外部性、公共物品与机制","\u002Fzh\u002Fmicroeconomics\u002F10-externalities-public-goods","zh\u002Fmicroeconomics\u002F10-externalities-public-goods\u002Findex",[1612],{"title":1608,"path":1609,"stem":1610},{"title":1614,"path":1615,"stem":1616,"children":1617},"市场设计与匹配理论","\u002Fzh\u002Fmicroeconomics\u002F11-market-design","zh\u002Fmicroeconomics\u002F11-market-design\u002Findex",[1618],{"title":1614,"path":1615,"stem":1616},{"title":1620,"path":1621,"stem":1622,"children":1623},"第十二章：微观经济学：回顾与前沿应用","\u002Fzh\u002Fmicroeconomics\u002F12-review","zh\u002Fmicroeconomics\u002F12-review\u002Findex",[1624],{"title":1620,"path":1621,"stem":1622},{"title":799,"path":1626,"stem":1627,"children":1628,"page":249},"\u002Fzh\u002Fplayground","zh\u002Fplayground",[1629],{"title":1630,"path":1631,"stem":1632},"Chart.js 可视化","\u002Fzh\u002Fplayground\u002F05-chartjs","zh\u002Fplayground\u002F05-chartjs",{"title":1634,"path":1635,"stem":1636,"children":1637},"概率论与数理统计","\u002Fzh\u002Fprob-and-stats","zh\u002Fprob-and-stats\u002Findex",[1638,1639,1645,1680,1727],{"title":1634,"path":1635,"stem":1636},{"title":1640,"path":1641,"stem":1642,"children":1643},"第零章：概率统计的对象与学习方法","\u002Fzh\u002Fprob-and-stats\u002F00-intro","zh\u002Fprob-and-stats\u002F00-intro\u002Findex",[1644],{"title":1640,"path":1641,"stem":1642},{"title":1646,"path":1647,"stem":1648,"children":1649,"page":249},"01 Probability","\u002Fzh\u002Fprob-and-stats\u002F01-probability","zh\u002Fprob-and-stats\u002F01-probability",[1650,1656,1662,1668,1674],{"title":1651,"path":1652,"stem":1653,"children":1654},"第一章：概率论基础","\u002Fzh\u002Fprob-and-stats\u002F01-probability\u002F01-prob-theory","zh\u002Fprob-and-stats\u002F01-probability\u002F01-prob-theory\u002Findex",[1655],{"title":1651,"path":1652,"stem":1653},{"title":1657,"path":1658,"stem":1659,"children":1660},"第二章：随机变量与分布","\u002Fzh\u002Fprob-and-stats\u002F01-probability\u002F02-random-variables","zh\u002Fprob-and-stats\u002F01-probability\u002F02-random-variables\u002Findex",[1661],{"title":1657,"path":1658,"stem":1659},{"title":1663,"path":1664,"stem":1665,"children":1666},"第三章：期望、方差与条件矩","\u002Fzh\u002Fprob-and-stats\u002F01-probability\u002F03-moment","zh\u002Fprob-and-stats\u002F01-probability\u002F03-moment\u002Findex",[1667],{"title":1663,"path":1664,"stem":1665},{"title":1669,"path":1670,"stem":1671,"children":1672},"第四章：常见分布族与建模机制","\u002Fzh\u002Fprob-and-stats\u002F01-probability\u002F04-families","zh\u002Fprob-and-stats\u002F01-probability\u002F04-families\u002Findex",[1673],{"title":1669,"path":1670,"stem":1671},{"title":1675,"path":1676,"stem":1677,"children":1678},"第五章：收敛与渐近理论","\u002Fzh\u002Fprob-and-stats\u002F01-probability\u002F05-asymptotics","zh\u002Fprob-and-stats\u002F01-probability\u002F05-asymptotics\u002Findex",[1679],{"title":1675,"path":1676,"stem":1677},{"title":1681,"path":1682,"stem":1683,"children":1684,"page":249},"02 Statistics","\u002Fzh\u002Fprob-and-stats\u002F02-statistics","zh\u002Fprob-and-stats\u002F02-statistics",[1685,1691,1697,1703,1709,1715,1721],{"title":1686,"path":1687,"stem":1688,"children":1689},"第六章：抽样分布","\u002Fzh\u002Fprob-and-stats\u002F02-statistics\u002F01-sampling","zh\u002Fprob-and-stats\u002F02-statistics\u002F01-sampling\u002Findex",[1690],{"title":1686,"path":1687,"stem":1688},{"title":1692,"path":1693,"stem":1694,"children":1695},"第七章：区间估计","\u002Fzh\u002Fprob-and-stats\u002F02-statistics\u002F02-interval-estimation","zh\u002Fprob-and-stats\u002F02-statistics\u002F02-interval-estimation\u002Findex",[1696],{"title":1692,"path":1693,"stem":1694},{"title":1698,"path":1699,"stem":1700,"children":1701},"第八章：点估计理论","\u002Fzh\u002Fprob-and-stats\u002F02-statistics\u002F03-point-estimation","zh\u002Fprob-and-stats\u002F02-statistics\u002F03-point-estimation\u002Findex",[1702],{"title":1698,"path":1699,"stem":1700},{"title":1704,"path":1705,"stem":1706,"children":1707},"第九章：点估计方法","\u002Fzh\u002Fprob-and-stats\u002F02-statistics\u002F04-pe-method","zh\u002Fprob-and-stats\u002F02-statistics\u002F04-pe-method\u002Findex",[1708],{"title":1704,"path":1705,"stem":1706},{"title":1710,"path":1711,"stem":1712,"children":1713},"第十章：假设检验原理","\u002Fzh\u002Fprob-and-stats\u002F02-statistics\u002F05-hypothesis","zh\u002Fprob-and-stats\u002F02-statistics\u002F05-hypothesis\u002Findex",[1714],{"title":1710,"path":1711,"stem":1712},{"title":1716,"path":1717,"stem":1718,"children":1719},"第十一章：常用检验方法与选择","\u002Fzh\u002Fprob-and-stats\u002F02-statistics\u002F06-hypothesis-method","zh\u002Fprob-and-stats\u002F02-statistics\u002F06-hypothesis-method\u002Findex",[1720],{"title":1716,"path":1717,"stem":1718},{"title":1722,"path":1723,"stem":1724,"children":1725},"第十二章：Bootstrap 与重抽样","\u002Fzh\u002Fprob-and-stats\u002F02-statistics\u002F07-bootstrap","zh\u002Fprob-and-stats\u002F02-statistics\u002F07-bootstrap\u002Findex",[1726],{"title":1722,"path":1723,"stem":1724},{"title":1728,"path":1729,"stem":1730},"第十三章：前沿文献与现代统计案例（2023—2026）","\u002Fzh\u002Fprob-and-stats\u002F03-frontier-literature-2026","zh\u002Fprob-and-stats\u002F03-frontier-literature-2026",{"title":1732,"path":1733,"stem":1734,"children":1735,"page":249},"Quant","\u002Fzh\u002Fquant","zh\u002Fquant",[1736,1742,1746,1750,1754,1758,1762,1766,1770,1774,1778],{"title":1737,"path":1738,"stem":1739,"children":1740},"量化投资——从可检验信号到可执行组合","\u002Fzh\u002Fquant\u002F00-index","zh\u002Fquant\u002F00-index",[1741],{"title":1737,"path":1738,"stem":1739},{"title":1743,"path":1744,"stem":1745},"数据获取与预处理","\u002Fzh\u002Fquant\u002F01-research-data","zh\u002Fquant\u002F01-research-data",{"title":1747,"path":1748,"stem":1749},"因子与交易信号","\u002Fzh\u002Fquant\u002F02-factor-and-signals","zh\u002Fquant\u002F02-factor-and-signals",{"title":1751,"path":1752,"stem":1753},"策略建模与回测","\u002Fzh\u002Fquant\u002F03-modeling-and-backtest","zh\u002Fquant\u002F03-modeling-and-backtest",{"title":1755,"path":1756,"stem":1757},"组合构建与风险建模（资产定价视角）","\u002Fzh\u002Fquant\u002F04-portfolio-and-risk","zh\u002Fquant\u002F04-portfolio-and-risk",{"title":1759,"path":1760,"stem":1761},"执行策略与市场微结构概览","\u002Fzh\u002Fquant\u002F05-execution-and-microstructure","zh\u002Fquant\u002F05-execution-and-microstructure",{"title":1763,"path":1764,"stem":1765},"策略上线、监控与迭代","\u002Fzh\u002Fquant\u002F06-production-and-monitoring","zh\u002Fquant\u002F06-production-and-monitoring",{"title":1767,"path":1768,"stem":1769},"量化工程与工具链概览","\u002Fzh\u002Fquant\u002F07-engineering-stack","zh\u002Fquant\u002F07-engineering-stack",{"title":1771,"path":1772,"stem":1773},"计量方法与实证检验","\u002Fzh\u002Fquant\u002F08-econometric-methods","zh\u002Fquant\u002F08-econometric-methods",{"title":1775,"path":1776,"stem":1777},"案例研究：多因子股票 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":1480,"body":1785,"description":4931,"extension":4932,"features":1782,"hero":1782,"layout":1782,"locale":1782,"meta":4933,"navigation":1782,"path":1481,"published":4935,"seo":4936,"stem":1482,"__hash__":4937},"docs\u002Fzh\u002Fmicroeconometrics\u002F02-ols\u002Findex.md",{"type":1786,"value":1787,"toc":4917},"minimark",[1788,1792,1803,1885,1889,1892,1911,1915,1918,2646,2719,3099,3102,3106,3109,3371,3403,3812,3844,3848,3851,4228,4359,4363,4557,4560,4564,4567,4574,4577,4581,4584,4688,4691,4695,4762,4765,4846,4849,4852,4869,4872,4905],[1789,1790,1480],"h1",{"id":1791},"第二章线性回归与-ols",[1793,1794,1795],"blockquote",{},[1796,1797,1798,1802],"p",{},[1799,1800,1801],"strong",{},"案例："," 工资对受教育年限的回归系数，是教育回报，还是教育与能力共同选择的结果？",[1796,1804,1805,1806,1853,1854,1884],{},"OLS 最擅长回答“在指定线性比较下，",[1807,1808,1811,1833],"span",{"className":1809},[1810],"katex",[1807,1812,1815],{"className":1813},[1814],"katex-mathml",[1816,1817,1819],"math",{"xmlns":1818},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML",[1820,1821,1822,1829],"semantics",{},[1823,1824,1825],"mrow",{},[1826,1827,1828],"mi",{},"X",[1830,1831,1828],"annotation",{"encoding":1832},"application\u002Fx-tex",[1807,1834,1838],{"className":1835,"ariaHidden":1837},[1836],"katex-html","true",[1807,1839,1842,1847],{"className":1840},[1841],"base",[1807,1843],{"className":1844,"style":1846},[1845],"strut","height:0.6833em;",[1807,1848,1828],{"className":1849,"style":1852},[1850,1851],"mord","mathnormal","margin-right:0.0785em;"," 与 ",[1807,1855,1857,1871],{"className":1856},[1810],[1807,1858,1860],{"className":1859},[1814],[1816,1861,1862],{"xmlns":1818},[1820,1863,1864,1869],{},[1823,1865,1866],{},[1826,1867,1868],{},"Y",[1830,1870,1868],{"encoding":1832},[1807,1872,1874],{"className":1873,"ariaHidden":1837},[1836],[1807,1875,1877,1880],{"className":1876},[1841],[1807,1878],{"className":1879,"style":1846},[1845],[1807,1881,1868],{"className":1882,"style":1883},[1850,1851],"margin-right:0.2222em;"," 如何共同变化”。只有当研究设计支持时，这个线性投影才有因果含义。",[1886,1887,1888],"h2",{"id":1888},"学习目标",[1796,1890,1891],{},"你应能：",[1893,1894,1895,1899,1902,1905,1908],"ol",{},[1896,1897,1898],"li",{},"区分线性投影、条件均值与因果效应；",[1896,1900,1901],{},"用遗漏变量偏误公式判断偏误方向；",[1896,1903,1904],{},"用 FWL 定理解释“控制变量”做了什么；",[1896,1906,1907],{},"识别混淆变量、精度变量、坏控制和碰撞点；",[1896,1909,1910],{},"报告稳健标准误、置信区间与经济量级。",[1886,1912,1914],{"id":1913},"_1-ols-究竟估计什么","1. OLS 究竟估计什么",[1796,1916,1917],{},"总体线性投影写为",[1807,1919,1922],{"className":1920},[1921],"katex-display",[1807,1923,1925,2061],{"className":1924},[1810],[1807,1926,1928],{"className":1927},[1814],[1816,1929,1931],{"xmlns":1818,"display":1930},"block",[1820,1932,1933,2058],{},[1823,1934,1935,1943,1947,1956,1959,1966,1973,1975,1987,1990,1992,1999,2002,2006,2009,2013,2016,2018,2020,2026,2028,2036,2044,2050,2053,2055],{},[1936,1937,1938,1940],"msub",{},[1826,1939,1868],{},[1826,1941,1942],{},"i",[1944,1945,1946],"mo",{},"=",[1936,1948,1949,1952],{},[1826,1950,1951],{},"β",[1953,1954,1955],"mn",{},"0",[1944,1957,1958],{},"+",[1936,1960,1961,1963],{},[1826,1962,1951],{},[1953,1964,1965],{},"1",[1936,1967,1968,1971],{},[1826,1969,1970],{},"D",[1826,1972,1942],{},[1944,1974,1958],{},[1976,1977,1978,1980,1982],"msubsup",{},[1826,1979,1828],{},[1826,1981,1942],{},[1944,1983,1986],{"mathvariant":1984,"lspace":1985,"rspace":1985},"normal","0em","′",[1826,1988,1989],{},"γ",[1944,1991,1958],{},[1936,1993,1994,1997],{},[1826,1995,1996],{},"u",[1826,1998,1942],{},[1944,2000,2001],{"separator":1837},",",[2003,2004],"mspace",{"width":2005},"2em",[1826,2007,2008],{},"E",[1944,2010,2012],{"stretchy":2011},"false","[",[1944,2014,2015],{"stretchy":2011},"(",[1953,2017,1965],{},[1944,2019,2001],{"separator":1837},[1936,2021,2022,2024],{},[1826,2023,1970],{},[1826,2025,1942],{},[1944,2027,2001],{"separator":1837},[1976,2029,2030,2032,2034],{},[1826,2031,1828],{},[1826,2033,1942],{},[1944,2035,1986],{"mathvariant":1984,"lspace":1985,"rspace":1985},[2037,2038,2039,2042],"msup",{},[1944,2040,2041],{"stretchy":2011},")",[1944,2043,1986],{"mathvariant":1984,"lspace":1985,"rspace":1985},[1936,2045,2046,2048],{},[1826,2047,1996],{},[1826,2049,1942],{},[1944,2051,2052],{"stretchy":2011},"]",[1944,2054,1946],{},[1953,2056,2057],{},"0.",[1830,2059,2060],{"encoding":1832},"Y_i=\\beta_0+\\beta_1D_i+X_i'\\gamma+u_i,\n\\qquad E[(1,D_i,X_i')'u_i]=0.",[1807,2062,2064,2138,2198,2295,2373,2636],{"className":2063,"ariaHidden":1837},[1836],[1807,2065,2067,2071,2127,2131,2135],{"className":2066},[1841],[1807,2068],{"className":2069,"style":2070},[1845],"height:0.8333em;vertical-align:-0.15em;",[1807,2072,2074,2077],{"className":2073},[1850],[1807,2075,1868],{"className":2076,"style":1883},[1850,1851],[1807,2078,2081],{"className":2079},[2080],"msupsub",[1807,2082,2086,2118],{"className":2083},[2084,2085],"vlist-t","vlist-t2",[1807,2087,2090,2113],{"className":2088},[2089],"vlist-r",[1807,2091,2095],{"className":2092,"style":2094},[2093],"vlist","height:0.3117em;",[1807,2096,2098,2103],{"style":2097},"top:-2.55em;margin-left:-0.2222em;margin-right:0.05em;",[1807,2099],{"className":2100,"style":2102},[2101],"pstrut","height:2.7em;",[1807,2104,2110],{"className":2105},[2106,2107,2108,2109],"sizing","reset-size6","size3","mtight",[1807,2111,1942],{"className":2112},[1850,1851,2109],[1807,2114,2117],{"className":2115},[2116],"vlist-s","​",[1807,2119,2121],{"className":2120},[2089],[1807,2122,2125],{"className":2123,"style":2124},[2093],"height:0.15em;",[1807,2126],{},[1807,2128],{"className":2129,"style":2130},[2003],"margin-right:0.2778em;",[1807,2132,1946],{"className":2133},[2134],"mrel",[1807,2136],{"className":2137,"style":2130},[2003],[1807,2139,2141,2145,2188,2191,2195],{"className":2140},[1841],[1807,2142],{"className":2143,"style":2144},[1845],"height:0.8889em;vertical-align:-0.1944em;",[1807,2146,2148,2152],{"className":2147},[1850],[1807,2149,1951],{"className":2150,"style":2151},[1850,1851],"margin-right:0.0528em;",[1807,2153,2155],{"className":2154},[2080],[1807,2156,2158,2180],{"className":2157},[2084,2085],[1807,2159,2161,2177],{"className":2160},[2089],[1807,2162,2165],{"className":2163,"style":2164},[2093],"height:0.3011em;",[1807,2166,2168,2171],{"style":2167},"top:-2.55em;margin-left:-0.0528em;margin-right:0.05em;",[1807,2169],{"className":2170,"style":2102},[2101],[1807,2172,2174],{"className":2173},[2106,2107,2108,2109],[1807,2175,1955],{"className":2176},[1850,2109],[1807,2178,2117],{"className":2179},[2116],[1807,2181,2183],{"className":2182},[2089],[1807,2184,2186],{"className":2185,"style":2124},[2093],[1807,2187],{},[1807,2189],{"className":2190,"style":1883},[2003],[1807,2192,1958],{"className":2193},[2194],"mbin",[1807,2196],{"className":2197,"style":1883},[2003],[1807,2199,2201,2204,2244,2286,2289,2292],{"className":2200},[1841],[1807,2202],{"className":2203,"style":2144},[1845],[1807,2205,2207,2210],{"className":2206},[1850],[1807,2208,1951],{"className":2209,"style":2151},[1850,1851],[1807,2211,2213],{"className":2212},[2080],[1807,2214,2216,2236],{"className":2215},[2084,2085],[1807,2217,2219,2233],{"className":2218},[2089],[1807,2220,2222],{"className":2221,"style":2164},[2093],[1807,2223,2224,2227],{"style":2167},[1807,2225],{"className":2226,"style":2102},[2101],[1807,2228,2230],{"className":2229},[2106,2107,2108,2109],[1807,2231,1965],{"className":2232},[1850,2109],[1807,2234,2117],{"className":2235},[2116],[1807,2237,2239],{"className":2238},[2089],[1807,2240,2242],{"className":2241,"style":2124},[2093],[1807,2243],{},[1807,2245,2247,2251],{"className":2246},[1850],[1807,2248,1970],{"className":2249,"style":2250},[1850,1851],"margin-right:0.0278em;",[1807,2252,2254],{"className":2253},[2080],[1807,2255,2257,2278],{"className":2256},[2084,2085],[1807,2258,2260,2275],{"className":2259},[2089],[1807,2261,2263],{"className":2262,"style":2094},[2093],[1807,2264,2266,2269],{"style":2265},"top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;",[1807,2267],{"className":2268,"style":2102},[2101],[1807,2270,2272],{"className":2271},[2106,2107,2108,2109],[1807,2273,1942],{"className":2274},[1850,1851,2109],[1807,2276,2117],{"className":2277},[2116],[1807,2279,2281],{"className":2280},[2089],[1807,2282,2284],{"className":2283,"style":2124},[2093],[1807,2285],{},[1807,2287],{"className":2288,"style":1883},[2003],[1807,2290,1958],{"className":2291},[2194],[1807,2293],{"className":2294,"style":1883},[2003],[1807,2296,2298,2302,2360,2364,2367,2370],{"className":2297},[1841],[1807,2299],{"className":2300,"style":2301},[1845],"height:1.0489em;vertical-align:-0.247em;",[1807,2303,2305,2308],{"className":2304},[1850],[1807,2306,1828],{"className":2307,"style":1852},[1850,1851],[1807,2309,2311],{"className":2310},[2080],[1807,2312,2314,2351],{"className":2313},[2084,2085],[1807,2315,2317,2348],{"className":2316},[2089],[1807,2318,2321,2333],{"className":2319,"style":2320},[2093],"height:0.8019em;",[1807,2322,2324,2327],{"style":2323},"top:-2.453em;margin-left:-0.0785em;margin-right:0.05em;",[1807,2325],{"className":2326,"style":2102},[2101],[1807,2328,2330],{"className":2329},[2106,2107,2108,2109],[1807,2331,1942],{"className":2332},[1850,1851,2109],[1807,2334,2336,2339],{"style":2335},"top:-3.113em;margin-right:0.05em;",[1807,2337],{"className":2338,"style":2102},[2101],[1807,2340,2342],{"className":2341},[2106,2107,2108,2109],[1807,2343,2345],{"className":2344},[1850,2109],[1807,2346,1986],{"className":2347},[1850,2109],[1807,2349,2117],{"className":2350},[2116],[1807,2352,2354],{"className":2353},[2089],[1807,2355,2358],{"className":2356,"style":2357},[2093],"height:0.247em;",[1807,2359],{},[1807,2361,1989],{"className":2362,"style":2363},[1850,1851],"margin-right:0.0556em;",[1807,2365],{"className":2366,"style":1883},[2003],[1807,2368,1958],{"className":2369},[2194],[1807,2371],{"className":2372,"style":1883},[2003],[1807,2374,2376,2380,2421,2425,2429,2433,2437,2442,2445,2448,2451,2491,2494,2497,2551,2584,2624,2627,2630,2633],{"className":2375},[1841],[1807,2377],{"className":2378,"style":2379},[1845],"height:1.0519em;vertical-align:-0.25em;",[1807,2381,2383,2386],{"className":2382},[1850],[1807,2384,1996],{"className":2385},[1850,1851],[1807,2387,2389],{"className":2388},[2080],[1807,2390,2392,2413],{"className":2391},[2084,2085],[1807,2393,2395,2410],{"className":2394},[2089],[1807,2396,2398],{"className":2397,"style":2094},[2093],[1807,2399,2401,2404],{"style":2400},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[1807,2402],{"className":2403,"style":2102},[2101],[1807,2405,2407],{"className":2406},[2106,2107,2108,2109],[1807,2408,1942],{"className":2409},[1850,1851,2109],[1807,2411,2117],{"className":2412},[2116],[1807,2414,2416],{"className":2415},[2089],[1807,2417,2419],{"className":2418,"style":2124},[2093],[1807,2420],{},[1807,2422,2001],{"className":2423},[2424],"mpunct",[1807,2426],{"className":2427,"style":2428},[2003],"margin-right:2em;",[1807,2430],{"className":2431,"style":2432},[2003],"margin-right:0.1667em;",[1807,2434,2008],{"className":2435,"style":2436},[1850,1851],"margin-right:0.0576em;",[1807,2438,2441],{"className":2439},[2440],"mopen","[(",[1807,2443,1965],{"className":2444},[1850],[1807,2446,2001],{"className":2447},[2424],[1807,2449],{"className":2450,"style":2432},[2003],[1807,2452,2454,2457],{"className":2453},[1850],[1807,2455,1970],{"className":2456,"style":2250},[1850,1851],[1807,2458,2460],{"className":2459},[2080],[1807,2461,2463,2483],{"className":2462},[2084,2085],[1807,2464,2466,2480],{"className":2465},[2089],[1807,2467,2469],{"className":2468,"style":2094},[2093],[1807,2470,2471,2474],{"style":2265},[1807,2472],{"className":2473,"style":2102},[2101],[1807,2475,2477],{"className":2476},[2106,2107,2108,2109],[1807,2478,1942],{"className":2479},[1850,1851,2109],[1807,2481,2117],{"className":2482},[2116],[1807,2484,2486],{"className":2485},[2089],[1807,2487,2489],{"className":2488,"style":2124},[2093],[1807,2490],{},[1807,2492,2001],{"className":2493},[2424],[1807,2495],{"className":2496,"style":2432},[2003],[1807,2498,2500,2503],{"className":2499},[1850],[1807,2501,1828],{"className":2502,"style":1852},[1850,1851],[1807,2504,2506],{"className":2505},[2080],[1807,2507,2509,2543],{"className":2508},[2084,2085],[1807,2510,2512,2540],{"className":2511},[2089],[1807,2513,2515,2526],{"className":2514,"style":2320},[2093],[1807,2516,2517,2520],{"style":2323},[1807,2518],{"className":2519,"style":2102},[2101],[1807,2521,2523],{"className":2522},[2106,2107,2108,2109],[1807,2524,1942],{"className":2525},[1850,1851,2109],[1807,2527,2528,2531],{"style":2335},[1807,2529],{"className":2530,"style":2102},[2101],[1807,2532,2534],{"className":2533},[2106,2107,2108,2109],[1807,2535,2537],{"className":2536},[1850,2109],[1807,2538,1986],{"className":2539},[1850,2109],[1807,2541,2117],{"className":2542},[2116],[1807,2544,2546],{"className":2545},[2089],[1807,2547,2549],{"className":2548,"style":2357},[2093],[1807,2550],{},[1807,2552,2555,2558],{"className":2553},[2554],"mclose",[1807,2556,2041],{"className":2557},[2554],[1807,2559,2561],{"className":2560},[2080],[1807,2562,2564],{"className":2563},[2084],[1807,2565,2567],{"className":2566},[2089],[1807,2568,2570],{"className":2569,"style":2320},[2093],[1807,2571,2572,2575],{"style":2335},[1807,2573],{"className":2574,"style":2102},[2101],[1807,2576,2578],{"className":2577},[2106,2107,2108,2109],[1807,2579,2581],{"className":2580},[1850,2109],[1807,2582,1986],{"className":2583},[1850,2109],[1807,2585,2587,2590],{"className":2586},[1850],[1807,2588,1996],{"className":2589},[1850,1851],[1807,2591,2593],{"className":2592},[2080],[1807,2594,2596,2616],{"className":2595},[2084,2085],[1807,2597,2599,2613],{"className":2598},[2089],[1807,2600,2602],{"className":2601,"style":2094},[2093],[1807,2603,2604,2607],{"style":2400},[1807,2605],{"className":2606,"style":2102},[2101],[1807,2608,2610],{"className":2609},[2106,2107,2108,2109],[1807,2611,1942],{"className":2612},[1850,1851,2109],[1807,2614,2117],{"className":2615},[2116],[1807,2617,2619],{"className":2618},[2089],[1807,2620,2622],{"className":2621,"style":2124},[2093],[1807,2623],{},[1807,2625,2052],{"className":2626},[2554],[1807,2628],{"className":2629,"style":2130},[2003],[1807,2631,1946],{"className":2632},[2134],[1807,2634],{"className":2635,"style":2130},[2003],[1807,2637,2639,2643],{"className":2638},[1841],[1807,2640],{"className":2641,"style":2642},[1845],"height:0.6444em;",[1807,2644,2057],{"className":2645},[1850],[1796,2647,2648,2718],{},[1807,2649,2651,2669],{"className":2650},[1810],[1807,2652,2654],{"className":2653},[1814],[1816,2655,2656],{"xmlns":1818},[1820,2657,2658,2666],{},[1823,2659,2660],{},[1936,2661,2662,2664],{},[1826,2663,1951],{},[1953,2665,1965],{},[1830,2667,2668],{"encoding":1832},"\\beta_1",[1807,2670,2672],{"className":2671,"ariaHidden":1837},[1836],[1807,2673,2675,2678],{"className":2674},[1841],[1807,2676],{"className":2677,"style":2144},[1845],[1807,2679,2681,2684],{"className":2680},[1850],[1807,2682,1951],{"className":2683,"style":2151},[1850,1851],[1807,2685,2687],{"className":2686},[2080],[1807,2688,2690,2710],{"className":2689},[2084,2085],[1807,2691,2693,2707],{"className":2692},[2089],[1807,2694,2696],{"className":2695,"style":2164},[2093],[1807,2697,2698,2701],{"style":2167},[1807,2699],{"className":2700,"style":2102},[2101],[1807,2702,2704],{"className":2703},[2106,2107,2108,2109],[1807,2705,1965],{"className":2706},[1850,2109],[1807,2708,2117],{"className":2709},[2116],[1807,2711,2713],{"className":2712},[2089],[1807,2714,2716],{"className":2715,"style":2124},[2093],[1807,2717],{}," 是在所有线性函数中使均方误差最小的系数。它自动具有描述意义；要解释为处理效应，还需类似",[1807,2720,2722],{"className":2721},[1921],[1807,2723,2725,2801],{"className":2724},[1810],[1807,2726,2728],{"className":2727},[1814],[1816,2729,2730],{"xmlns":1818,"display":1930},[1820,2731,2732,2798],{},[1823,2733,2734,2736,2738,2744,2746,2749,2751,2754,2760,2762,2768,2770,2772,2774,2776,2782,2784,2786,2788,2790,2796],{},[1826,2735,2008],{},[1944,2737,2012],{"stretchy":2011},[1936,2739,2740,2742],{},[1826,2741,1868],{},[1826,2743,1942],{},[1944,2745,2015],{"stretchy":2011},[1826,2747,2748],{},"d",[1944,2750,2041],{"stretchy":2011},[1944,2752,2753],{},"∣",[1936,2755,2756,2758],{},[1826,2757,1970],{},[1826,2759,1942],{},[1944,2761,2001],{"separator":1837},[1936,2763,2764,2766],{},[1826,2765,1828],{},[1826,2767,1942],{},[1944,2769,2052],{"stretchy":2011},[1944,2771,1946],{},[1826,2773,2008],{},[1944,2775,2012],{"stretchy":2011},[1936,2777,2778,2780],{},[1826,2779,1868],{},[1826,2781,1942],{},[1944,2783,2015],{"stretchy":2011},[1826,2785,2748],{},[1944,2787,2041],{"stretchy":2011},[1944,2789,2753],{},[1936,2791,2792,2794],{},[1826,2793,1828],{},[1826,2795,1942],{},[1944,2797,2052],{"stretchy":2011},[1830,2799,2800],{"encoding":1832},"E[Y_i(d)\\mid D_i,X_i]=E[Y_i(d)\\mid X_i]",[1807,2802,2804,2875,2980,3050],{"className":2803,"ariaHidden":1837},[1836],[1807,2805,2807,2811,2814,2817,2857,2860,2863,2866,2869,2872],{"className":2806},[1841],[1807,2808],{"className":2809,"style":2810},[1845],"height:1em;vertical-align:-0.25em;",[1807,2812,2008],{"className":2813,"style":2436},[1850,1851],[1807,2815,2012],{"className":2816},[2440],[1807,2818,2820,2823],{"className":2819},[1850],[1807,2821,1868],{"className":2822,"style":1883},[1850,1851],[1807,2824,2826],{"className":2825},[2080],[1807,2827,2829,2849],{"className":2828},[2084,2085],[1807,2830,2832,2846],{"className":2831},[2089],[1807,2833,2835],{"className":2834,"style":2094},[2093],[1807,2836,2837,2840],{"style":2097},[1807,2838],{"className":2839,"style":2102},[2101],[1807,2841,2843],{"className":2842},[2106,2107,2108,2109],[1807,2844,1942],{"className":2845},[1850,1851,2109],[1807,2847,2117],{"className":2848},[2116],[1807,2850,2852],{"className":2851},[2089],[1807,2853,2855],{"className":2854,"style":2124},[2093],[1807,2856],{},[1807,2858,2015],{"className":2859},[2440],[1807,2861,2748],{"className":2862},[1850,1851],[1807,2864,2041],{"className":2865},[2554],[1807,2867],{"className":2868,"style":2130},[2003],[1807,2870,2753],{"className":2871},[2134],[1807,2873],{"className":2874,"style":2130},[2003],[1807,2876,2878,2881,2921,2924,2927,2968,2971,2974,2977],{"className":2877},[1841],[1807,2879],{"className":2880,"style":2810},[1845],[1807,2882,2884,2887],{"className":2883},[1850],[1807,2885,1970],{"className":2886,"style":2250},[1850,1851],[1807,2888,2890],{"className":2889},[2080],[1807,2891,2893,2913],{"className":2892},[2084,2085],[1807,2894,2896,2910],{"className":2895},[2089],[1807,2897,2899],{"className":2898,"style":2094},[2093],[1807,2900,2901,2904],{"style":2265},[1807,2902],{"className":2903,"style":2102},[2101],[1807,2905,2907],{"className":2906},[2106,2107,2108,2109],[1807,2908,1942],{"className":2909},[1850,1851,2109],[1807,2911,2117],{"className":2912},[2116],[1807,2914,2916],{"className":2915},[2089],[1807,2917,2919],{"className":2918,"style":2124},[2093],[1807,2920],{},[1807,2922,2001],{"className":2923},[2424],[1807,2925],{"className":2926,"style":2432},[2003],[1807,2928,2930,2933],{"className":2929},[1850],[1807,2931,1828],{"className":2932,"style":1852},[1850,1851],[1807,2934,2936],{"className":2935},[2080],[1807,2937,2939,2960],{"className":2938},[2084,2085],[1807,2940,2942,2957],{"className":2941},[2089],[1807,2943,2945],{"className":2944,"style":2094},[2093],[1807,2946,2948,2951],{"style":2947},"top:-2.55em;margin-left:-0.0785em;margin-right:0.05em;",[1807,2949],{"className":2950,"style":2102},[2101],[1807,2952,2954],{"className":2953},[2106,2107,2108,2109],[1807,2955,1942],{"className":2956},[1850,1851,2109],[1807,2958,2117],{"className":2959},[2116],[1807,2961,2963],{"className":2962},[2089],[1807,2964,2966],{"className":2965,"style":2124},[2093],[1807,2967],{},[1807,2969,2052],{"className":2970},[2554],[1807,2972],{"className":2973,"style":2130},[2003],[1807,2975,1946],{"className":2976},[2134],[1807,2978],{"className":2979,"style":2130},[2003],[1807,2981,2983,2986,2989,2992,3032,3035,3038,3041,3044,3047],{"className":2982},[1841],[1807,2984],{"className":2985,"style":2810},[1845],[1807,2987,2008],{"className":2988,"style":2436},[1850,1851],[1807,2990,2012],{"className":2991},[2440],[1807,2993,2995,2998],{"className":2994},[1850],[1807,2996,1868],{"className":2997,"style":1883},[1850,1851],[1807,2999,3001],{"className":3000},[2080],[1807,3002,3004,3024],{"className":3003},[2084,2085],[1807,3005,3007,3021],{"className":3006},[2089],[1807,3008,3010],{"className":3009,"style":2094},[2093],[1807,3011,3012,3015],{"style":2097},[1807,3013],{"className":3014,"style":2102},[2101],[1807,3016,3018],{"className":3017},[2106,2107,2108,2109],[1807,3019,1942],{"className":3020},[1850,1851,2109],[1807,3022,2117],{"className":3023},[2116],[1807,3025,3027],{"className":3026},[2089],[1807,3028,3030],{"className":3029,"style":2124},[2093],[1807,3031],{},[1807,3033,2015],{"className":3034},[2440],[1807,3036,2748],{"className":3037},[1850,1851],[1807,3039,2041],{"className":3040},[2554],[1807,3042],{"className":3043,"style":2130},[2003],[1807,3045,2753],{"className":3046},[2134],[1807,3048],{"className":3049,"style":2130},[2003],[1807,3051,3053,3056,3096],{"className":3052},[1841],[1807,3054],{"className":3055,"style":2810},[1845],[1807,3057,3059,3062],{"className":3058},[1850],[1807,3060,1828],{"className":3061,"style":1852},[1850,1851],[1807,3063,3065],{"className":3064},[2080],[1807,3066,3068,3088],{"className":3067},[2084,2085],[1807,3069,3071,3085],{"className":3070},[2089],[1807,3072,3074],{"className":3073,"style":2094},[2093],[1807,3075,3076,3079],{"style":2947},[1807,3077],{"className":3078,"style":2102},[2101],[1807,3080,3082],{"className":3081},[2106,2107,2108,2109],[1807,3083,1942],{"className":3084},[1850,1851,2109],[1807,3086,2117],{"className":3087},[2116],[1807,3089,3091],{"className":3090},[2089],[1807,3092,3094],{"className":3093,"style":2124},[2093],[1807,3095],{},[1807,3097,2052],{"className":3098},[2554],[1796,3100,3101],{},"的条件可交换性，以及一致性、重叠和正确的比较范围。",[1886,3103,3105],{"id":3104},"_2-遗漏变量偏误先判断方向","2. 遗漏变量偏误：先判断方向",[1796,3107,3108],{},"真实模型若为",[1807,3110,3112],{"className":3111},[1921],[1807,3113,3115,3166],{"className":3114},[1810],[1807,3116,3118],{"className":3117},[1814],[1816,3119,3120],{"xmlns":1818,"display":1930},[1820,3121,3122,3163],{},[1823,3123,3124,3126,3128,3134,3136,3142,3144,3146,3153,3156,3158,3161],{},[1826,3125,1868],{},[1944,3127,1946],{},[1936,3129,3130,3132],{},[1826,3131,1951],{},[1953,3133,1955],{},[1944,3135,1958],{},[1936,3137,3138,3140],{},[1826,3139,1951],{},[1953,3141,1965],{},[1826,3143,1970],{},[1944,3145,1958],{},[1936,3147,3148,3150],{},[1826,3149,1951],{},[1953,3151,3152],{},"2",[1826,3154,3155],{},"A",[1944,3157,1958],{},[1826,3159,3160],{},"ε",[1944,3162,2001],{"separator":1837},[1830,3164,3165],{"encoding":1832},"Y=\\beta_0+\\beta_1D+\\beta_2A+\\varepsilon,",[1807,3167,3169,3187,3242,3300,3358],{"className":3168,"ariaHidden":1837},[1836],[1807,3170,3172,3175,3178,3181,3184],{"className":3171},[1841],[1807,3173],{"className":3174,"style":1846},[1845],[1807,3176,1868],{"className":3177,"style":1883},[1850,1851],[1807,3179],{"className":3180,"style":2130},[2003],[1807,3182,1946],{"className":3183},[2134],[1807,3185],{"className":3186,"style":2130},[2003],[1807,3188,3190,3193,3233,3236,3239],{"className":3189},[1841],[1807,3191],{"className":3192,"style":2144},[1845],[1807,3194,3196,3199],{"className":3195},[1850],[1807,3197,1951],{"className":3198,"style":2151},[1850,1851],[1807,3200,3202],{"className":3201},[2080],[1807,3203,3205,3225],{"className":3204},[2084,2085],[1807,3206,3208,3222],{"className":3207},[2089],[1807,3209,3211],{"className":3210,"style":2164},[2093],[1807,3212,3213,3216],{"style":2167},[1807,3214],{"className":3215,"style":2102},[2101],[1807,3217,3219],{"className":3218},[2106,2107,2108,2109],[1807,3220,1955],{"className":3221},[1850,2109],[1807,3223,2117],{"className":3224},[2116],[1807,3226,3228],{"className":3227},[2089],[1807,3229,3231],{"className":3230,"style":2124},[2093],[1807,3232],{},[1807,3234],{"className":3235,"style":1883},[2003],[1807,3237,1958],{"className":3238},[2194],[1807,3240],{"className":3241,"style":1883},[2003],[1807,3243,3245,3248,3288,3291,3294,3297],{"className":3244},[1841],[1807,3246],{"className":3247,"style":2144},[1845],[1807,3249,3251,3254],{"className":3250},[1850],[1807,3252,1951],{"className":3253,"style":2151},[1850,1851],[1807,3255,3257],{"className":3256},[2080],[1807,3258,3260,3280],{"className":3259},[2084,2085],[1807,3261,3263,3277],{"className":3262},[2089],[1807,3264,3266],{"className":3265,"style":2164},[2093],[1807,3267,3268,3271],{"style":2167},[1807,3269],{"className":3270,"style":2102},[2101],[1807,3272,3274],{"className":3273},[2106,2107,2108,2109],[1807,3275,1965],{"className":3276},[1850,2109],[1807,3278,2117],{"className":3279},[2116],[1807,3281,3283],{"className":3282},[2089],[1807,3284,3286],{"className":3285,"style":2124},[2093],[1807,3287],{},[1807,3289,1970],{"className":3290,"style":2250},[1850,1851],[1807,3292],{"className":3293,"style":1883},[2003],[1807,3295,1958],{"className":3296},[2194],[1807,3298],{"className":3299,"style":1883},[2003],[1807,3301,3303,3306,3346,3349,3352,3355],{"className":3302},[1841],[1807,3304],{"className":3305,"style":2144},[1845],[1807,3307,3309,3312],{"className":3308},[1850],[1807,3310,1951],{"className":3311,"style":2151},[1850,1851],[1807,3313,3315],{"className":3314},[2080],[1807,3316,3318,3338],{"className":3317},[2084,2085],[1807,3319,3321,3335],{"className":3320},[2089],[1807,3322,3324],{"className":3323,"style":2164},[2093],[1807,3325,3326,3329],{"style":2167},[1807,3327],{"className":3328,"style":2102},[2101],[1807,3330,3332],{"className":3331},[2106,2107,2108,2109],[1807,3333,3152],{"className":3334},[1850,2109],[1807,3336,2117],{"className":3337},[2116],[1807,3339,3341],{"className":3340},[2089],[1807,3342,3344],{"className":3343,"style":2124},[2093],[1807,3345],{},[1807,3347,3155],{"className":3348},[1850,1851],[1807,3350],{"className":3351,"style":1883},[2003],[1807,3353,1958],{"className":3354},[2194],[1807,3356],{"className":3357,"style":1883},[2003],[1807,3359,3361,3365,3368],{"className":3360},[1841],[1807,3362],{"className":3363,"style":3364},[1845],"height:0.625em;vertical-align:-0.1944em;",[1807,3366,3160],{"className":3367},[1850,1851],[1807,3369,2001],{"className":3370},[2424],[1796,3372,3373,3374,3402],{},"却遗漏 ",[1807,3375,3377,3390],{"className":3376},[1810],[1807,3378,3380],{"className":3379},[1814],[1816,3381,3382],{"xmlns":1818},[1820,3383,3384,3388],{},[1823,3385,3386],{},[1826,3387,3155],{},[1830,3389,3155],{"encoding":1832},[1807,3391,3393],{"className":3392,"ariaHidden":1837},[1836],[1807,3394,3396,3399],{"className":3395},[1841],[1807,3397],{"className":3398,"style":1846},[1845],[1807,3400,3155],{"className":3401},[1850,1851],"，简单回归系数满足",[1807,3404,3406],{"className":3405},[1921],[1807,3407,3409,3486],{"className":3408},[1810],[1807,3410,3412],{"className":3411},[1814],[1816,3413,3414],{"xmlns":1818,"display":1930},[1820,3415,3416,3483],{},[1823,3417,3418,3430,3432,3438,3440,3446,3480],{},[1936,3419,3420,3428],{},[3421,3422,3423,3425],"mover",{"accent":1837},[1826,3424,1951],{},[1944,3426,3427],{"stretchy":1837},"~",[1953,3429,1965],{},[1944,3431,1946],{},[1936,3433,3434,3436],{},[1826,3435,1951],{},[1953,3437,1965],{},[1944,3439,1958],{},[1936,3441,3442,3444],{},[1826,3443,1951],{},[1953,3445,3152],{},[3447,3448,3449,3467],"mfrac",{},[1823,3450,3451,3454,3457,3459,3461,3463,3465],{},[1826,3452,3453],{"mathvariant":1984},"Cov",[1944,3455,3456],{},"⁡",[1944,3458,2015],{"stretchy":2011},[1826,3460,1970],{},[1944,3462,2001],{"separator":1837},[1826,3464,3155],{},[1944,3466,2041],{"stretchy":2011},[1823,3468,3469,3472,3474,3476,3478],{},[1826,3470,3471],{"mathvariant":1984},"Var",[1944,3473,3456],{},[1944,3475,2015],{"stretchy":2011},[1826,3477,1970],{},[1944,3479,2041],{"stretchy":2011},[1826,3481,3482],{"mathvariant":1984},".",[1830,3484,3485],{"encoding":1832},"\\widetilde\\beta_1\n=\\beta_1+\\beta_2\\frac{\\operatorname{Cov}(D,A)}{\\operatorname{Var}(D)}.",[1807,3487,3489,3601,3656],{"className":3488,"ariaHidden":1837},[1836],[1807,3490,3492,3496,3592,3595,3598],{"className":3491},[1841],[1807,3493],{"className":3494,"style":3495},[1845],"height:1.1489em;vertical-align:-0.1944em;",[1807,3497,3499,3558],{"className":3498},[1850],[1807,3500,3503],{"className":3501},[1850,3502],"accent",[1807,3504,3506,3549],{"className":3505},[2084,2085],[1807,3507,3509,3546],{"className":3508},[2089],[1807,3510,3513,3523],{"className":3511,"style":3512},[2093],"height:0.9544em;",[1807,3514,3516,3520],{"style":3515},"top:-3em;",[1807,3517],{"className":3518,"style":3519},[2101],"height:3em;",[1807,3521,1951],{"className":3522,"style":2151},[1850,1851],[1807,3524,3528,3531],{"className":3525,"style":3527},[3526],"svg-align","width:calc(100% - 0.1667em);margin-left:0.1667em;top:-3.6944em;",[1807,3529],{"className":3530,"style":3519},[2101],[1807,3532,3534],{"style":3533},"height:0.26em;",[3535,3536,3542],"svg",{"xmlns":3537,"width":3538,"height":3539,"viewBox":3540,"preserveAspectRatio":3541},"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg","100%","0.26em","0 0 600 260","none",[3543,3544],"path",{"d":3545},"M200 55.538c-77 0-168 73.953-177 73.953-3 0-7\n-2.175-9-5.437L2 97c-1-2-2-4-2-6 0-4 2-7 5-9l20-12C116 12 171 0 207 0c86 0\n 114 68 191 68 78 0 168-68 177-68 4 0 7 2 9 5l12 19c1 2.175 2 4.35 2 6.525 0\n 4.35-2 7.613-5 9.788l-19 13.05c-92 63.077-116.937 75.308-183 76.128\n-68.267.847-113-73.952-191-73.952z",[1807,3547,2117],{"className":3548},[2116],[1807,3550,3552],{"className":3551},[2089],[1807,3553,3556],{"className":3554,"style":3555},[2093],"height:0.1944em;",[1807,3557],{},[1807,3559,3561],{"className":3560},[2080],[1807,3562,3564,3584],{"className":3563},[2084,2085],[1807,3565,3567,3581],{"className":3566},[2089],[1807,3568,3570],{"className":3569,"style":2164},[2093],[1807,3571,3572,3575],{"style":2167},[1807,3573],{"className":3574,"style":2102},[2101],[1807,3576,3578],{"className":3577},[2106,2107,2108,2109],[1807,3579,1965],{"className":3580},[1850,2109],[1807,3582,2117],{"className":3583},[2116],[1807,3585,3587],{"className":3586},[2089],[1807,3588,3590],{"className":3589,"style":2124},[2093],[1807,3591],{},[1807,3593],{"className":3594,"style":2130},[2003],[1807,3596,1946],{"className":3597},[2134],[1807,3599],{"className":3600,"style":2130},[2003],[1807,3602,3604,3607,3647,3650,3653],{"className":3603},[1841],[1807,3605],{"className":3606,"style":2144},[1845],[1807,3608,3610,3613],{"className":3609},[1850],[1807,3611,1951],{"className":3612,"style":2151},[1850,1851],[1807,3614,3616],{"className":3615},[2080],[1807,3617,3619,3639],{"className":3618},[2084,2085],[1807,3620,3622,3636],{"className":3621},[2089],[1807,3623,3625],{"className":3624,"style":2164},[2093],[1807,3626,3627,3630],{"style":2167},[1807,3628],{"className":3629,"style":2102},[2101],[1807,3631,3633],{"className":3632},[2106,2107,2108,2109],[1807,3634,1965],{"className":3635},[1850,2109],[1807,3637,2117],{"className":3638},[2116],[1807,3640,3642],{"className":3641},[2089],[1807,3643,3645],{"className":3644,"style":2124},[2093],[1807,3646],{},[1807,3648],{"className":3649,"style":1883},[2003],[1807,3651,1958],{"className":3652},[2194],[1807,3654],{"className":3655,"style":1883},[2003],[1807,3657,3659,3663,3703,3809],{"className":3658},[1841],[1807,3660],{"className":3661,"style":3662},[1845],"height:2.363em;vertical-align:-0.936em;",[1807,3664,3666,3669],{"className":3665},[1850],[1807,3667,1951],{"className":3668,"style":2151},[1850,1851],[1807,3670,3672],{"className":3671},[2080],[1807,3673,3675,3695],{"className":3674},[2084,2085],[1807,3676,3678,3692],{"className":3677},[2089],[1807,3679,3681],{"className":3680,"style":2164},[2093],[1807,3682,3683,3686],{"style":2167},[1807,3684],{"className":3685,"style":2102},[2101],[1807,3687,3689],{"className":3688},[2106,2107,2108,2109],[1807,3690,3152],{"className":3691},[1850,2109],[1807,3693,2117],{"className":3694},[2116],[1807,3696,3698],{"className":3697},[2089],[1807,3699,3701],{"className":3700,"style":2124},[2093],[1807,3702],{},[1807,3704,3706,3710,3806],{"className":3705},[1850],[1807,3707],{"className":3708},[2440,3709],"nulldelimiter",[1807,3711,3713],{"className":3712},[3447],[1807,3714,3716,3797],{"className":3715},[2084,2085],[1807,3717,3719,3794],{"className":3718},[2089],[1807,3720,3723,3749,3760],{"className":3721,"style":3722},[2093],"height:1.427em;",[1807,3724,3726,3729],{"style":3725},"top:-2.314em;",[1807,3727],{"className":3728,"style":3519},[2101],[1807,3730,3732,3740,3743,3746],{"className":3731},[1850],[1807,3733,3736],{"className":3734},[3735],"mop",[1807,3737,3471],{"className":3738},[1850,3739],"mathrm",[1807,3741,2015],{"className":3742},[2440],[1807,3744,1970],{"className":3745,"style":2250},[1850,1851],[1807,3747,2041],{"className":3748},[2554],[1807,3750,3752,3755],{"style":3751},"top:-3.23em;",[1807,3753],{"className":3754,"style":3519},[2101],[1807,3756],{"className":3757,"style":3759},[3758],"frac-line","border-bottom-width:0.04em;",[1807,3761,3763,3766],{"style":3762},"top:-3.677em;",[1807,3764],{"className":3765,"style":3519},[2101],[1807,3767,3769,3776,3779,3782,3785,3788,3791],{"className":3768},[1850],[1807,3770,3772],{"className":3771},[3735],[1807,3773,3453],{"className":3774,"style":3775},[1850,3739],"margin-right:0.0139em;",[1807,3777,2015],{"className":3778},[2440],[1807,3780,1970],{"className":3781,"style":2250},[1850,1851],[1807,3783,2001],{"className":3784},[2424],[1807,3786],{"className":3787,"style":2432},[2003],[1807,3789,3155],{"className":3790},[1850,1851],[1807,3792,2041],{"className":3793},[2554],[1807,3795,2117],{"className":3796},[2116],[1807,3798,3800],{"className":3799},[2089],[1807,3801,3804],{"className":3802,"style":3803},[2093],"height:0.936em;",[1807,3805],{},[1807,3807],{"className":3808},[2554,3709],[1807,3810,3482],{"className":3811},[1850],[1796,3813,3814,3815,3843],{},"若能力 ",[1807,3816,3818,3831],{"className":3817},[1810],[1807,3819,3821],{"className":3820},[1814],[1816,3822,3823],{"xmlns":1818},[1820,3824,3825,3829],{},[1823,3826,3827],{},[1826,3828,3155],{},[1830,3830,3155],{"encoding":1832},[1807,3832,3834],{"className":3833,"ariaHidden":1837},[1836],[1807,3835,3837,3840],{"className":3836},[1841],[1807,3838],{"className":3839,"style":1846},[1845],[1807,3841,3155],{"className":3842},[1850,1851]," 同时提高受教育年限和工资，则两项都为正，教育系数向上偏。这个公式不能证明真实世界偏误大小，但能迫使研究者写出方向逻辑。",[1886,3845,3847],{"id":3846},"_3-fwl控制就是先剥离可预测部分","3. FWL：控制就是先剥离可预测部分",[1796,3849,3850],{},"Frisch–Waugh–Lovell 定理给出三步：",[1893,3852,3853,3979,4101],{},[1896,3854,3855,3856,3884,3885,3913,3914,3978],{},"用控制变量 ",[1807,3857,3859,3872],{"className":3858},[1810],[1807,3860,3862],{"className":3861},[1814],[1816,3863,3864],{"xmlns":1818},[1820,3865,3866,3870],{},[1823,3867,3868],{},[1826,3869,1828],{},[1830,3871,1828],{"encoding":1832},[1807,3873,3875],{"className":3874,"ariaHidden":1837},[1836],[1807,3876,3878,3881],{"className":3877},[1841],[1807,3879],{"className":3880,"style":1846},[1845],[1807,3882,1828],{"className":3883,"style":1852},[1850,1851]," 回归 ",[1807,3886,3888,3901],{"className":3887},[1810],[1807,3889,3891],{"className":3890},[1814],[1816,3892,3893],{"xmlns":1818},[1820,3894,3895,3899],{},[1823,3896,3897],{},[1826,3898,1970],{},[1830,3900,1970],{"encoding":1832},[1807,3902,3904],{"className":3903,"ariaHidden":1837},[1836],[1807,3905,3907,3910],{"className":3906},[1841],[1807,3908],{"className":3909,"style":1846},[1845],[1807,3911,1970],{"className":3912,"style":2250},[1850,1851],"，取得残差 ",[1807,3915,3917,3935],{"className":3916},[1810],[1807,3918,3920],{"className":3919},[1814],[1816,3921,3922],{"xmlns":1818},[1820,3923,3924,3932],{},[1823,3925,3926],{},[3421,3927,3928,3930],{"accent":1837},[1826,3929,1970],{},[1944,3931,3427],{"stretchy":1837},[1830,3933,3934],{"encoding":1832},"\\widetilde D",[1807,3936,3938],{"className":3937,"ariaHidden":1837},[1836],[1807,3939,3941,3945],{"className":3940},[1841],[1807,3942],{"className":3943,"style":3944},[1845],"height:0.9433em;",[1807,3946,3948],{"className":3947},[1850,3502],[1807,3949,3951],{"className":3950},[2084],[1807,3952,3954],{"className":3953},[2089],[1807,3955,3957,3965],{"className":3956,"style":3944},[2093],[1807,3958,3959,3962],{"style":3515},[1807,3960],{"className":3961,"style":3519},[2101],[1807,3963,1970],{"className":3964,"style":2250},[1850,1851],[1807,3966,3969,3972],{"className":3967,"style":3968},[3526],"width:calc(100% - 0.1111em);margin-left:0.1111em;top:-3.6833em;",[1807,3970],{"className":3971,"style":3519},[2101],[1807,3973,3974],{"style":3533},[3535,3975,3976],{"xmlns":3537,"width":3538,"height":3539,"viewBox":3540,"preserveAspectRatio":3541},[3543,3977],{"d":3545},"；",[1896,3980,3981,3982,3884,4010,3913,4038,3978],{},"用 ",[1807,3983,3985,3998],{"className":3984},[1810],[1807,3986,3988],{"className":3987},[1814],[1816,3989,3990],{"xmlns":1818},[1820,3991,3992,3996],{},[1823,3993,3994],{},[1826,3995,1828],{},[1830,3997,1828],{"encoding":1832},[1807,3999,4001],{"className":4000,"ariaHidden":1837},[1836],[1807,4002,4004,4007],{"className":4003},[1841],[1807,4005],{"className":4006,"style":1846},[1845],[1807,4008,1828],{"className":4009,"style":1852},[1850,1851],[1807,4011,4013,4026],{"className":4012},[1810],[1807,4014,4016],{"className":4015},[1814],[1816,4017,4018],{"xmlns":1818},[1820,4019,4020,4024],{},[1823,4021,4022],{},[1826,4023,1868],{},[1830,4025,1868],{"encoding":1832},[1807,4027,4029],{"className":4028,"ariaHidden":1837},[1836],[1807,4030,4032,4035],{"className":4031},[1841],[1807,4033],{"className":4034,"style":1846},[1845],[1807,4036,1868],{"className":4037,"style":1883},[1850,1851],[1807,4039,4041,4059],{"className":4040},[1810],[1807,4042,4044],{"className":4043},[1814],[1816,4045,4046],{"xmlns":1818},[1820,4047,4048,4056],{},[1823,4049,4050],{},[3421,4051,4052,4054],{"accent":1837},[1826,4053,1868],{},[1944,4055,3427],{"stretchy":1837},[1830,4057,4058],{"encoding":1832},"\\widetilde Y",[1807,4060,4062],{"className":4061,"ariaHidden":1837},[1836],[1807,4063,4065,4068],{"className":4064},[1841],[1807,4066],{"className":4067,"style":3944},[1845],[1807,4069,4071],{"className":4070},[1850,3502],[1807,4072,4074],{"className":4073},[2084],[1807,4075,4077],{"className":4076},[2089],[1807,4078,4080,4088],{"className":4079,"style":3944},[2093],[1807,4081,4082,4085],{"style":3515},[1807,4083],{"className":4084,"style":3519},[2101],[1807,4086,1868],{"className":4087,"style":1883},[1850,1851],[1807,4089,4092,4095],{"className":4090,"style":4091},[3526],"top:-3.6833em;",[1807,4093],{"className":4094,"style":3519},[2101],[1807,4096,4097],{"style":3533},[3535,4098,4099],{"xmlns":3537,"width":3538,"height":3539,"viewBox":3540,"preserveAspectRatio":3541},[3543,4100],{"d":3545},[1896,4102,4103,4104,4165,4166,4227],{},"回归 ",[1807,4105,4107,4124],{"className":4106},[1810],[1807,4108,4110],{"className":4109},[1814],[1816,4111,4112],{"xmlns":1818},[1820,4113,4114,4122],{},[1823,4115,4116],{},[3421,4117,4118,4120],{"accent":1837},[1826,4119,1868],{},[1944,4121,3427],{"stretchy":1837},[1830,4123,4058],{"encoding":1832},[1807,4125,4127],{"className":4126,"ariaHidden":1837},[1836],[1807,4128,4130,4133],{"className":4129},[1841],[1807,4131],{"className":4132,"style":3944},[1845],[1807,4134,4136],{"className":4135},[1850,3502],[1807,4137,4139],{"className":4138},[2084],[1807,4140,4142],{"className":4141},[2089],[1807,4143,4145,4153],{"className":4144,"style":3944},[2093],[1807,4146,4147,4150],{"style":3515},[1807,4148],{"className":4149,"style":3519},[2101],[1807,4151,1868],{"className":4152,"style":1883},[1850,1851],[1807,4154,4156,4159],{"className":4155,"style":4091},[3526],[1807,4157],{"className":4158,"style":3519},[2101],[1807,4160,4161],{"style":3533},[3535,4162,4163],{"xmlns":3537,"width":3538,"height":3539,"viewBox":3540,"preserveAspectRatio":3541},[3543,4164],{"d":3545}," 对 ",[1807,4167,4169,4186],{"className":4168},[1810],[1807,4170,4172],{"className":4171},[1814],[1816,4173,4174],{"xmlns":1818},[1820,4175,4176,4184],{},[1823,4177,4178],{},[3421,4179,4180,4182],{"accent":1837},[1826,4181,1970],{},[1944,4183,3427],{"stretchy":1837},[1830,4185,3934],{"encoding":1832},[1807,4187,4189],{"className":4188,"ariaHidden":1837},[1836],[1807,4190,4192,4195],{"className":4191},[1841],[1807,4193],{"className":4194,"style":3944},[1845],[1807,4196,4198],{"className":4197},[1850,3502],[1807,4199,4201],{"className":4200},[2084],[1807,4202,4204],{"className":4203},[2089],[1807,4205,4207,4215],{"className":4206,"style":3944},[2093],[1807,4208,4209,4212],{"style":3515},[1807,4210],{"className":4211,"style":3519},[2101],[1807,4213,1970],{"className":4214,"style":2250},[1850,1851],[1807,4216,4218,4221],{"className":4217,"style":3968},[3526],[1807,4219],{"className":4220,"style":3519},[2101],[1807,4222,4223],{"style":3533},[3535,4224,4225],{"xmlns":3537,"width":3538,"height":3539,"viewBox":3540,"preserveAspectRatio":3541},[3543,4226],{"d":3545},"。",[1796,4229,4230,4231,4259,4260,4329,4330,4358],{},"所得斜率与完整多元回归中的 ",[1807,4232,4234,4247],{"className":4233},[1810],[1807,4235,4237],{"className":4236},[1814],[1816,4238,4239],{"xmlns":1818},[1820,4240,4241,4245],{},[1823,4242,4243],{},[1826,4244,1970],{},[1830,4246,1970],{"encoding":1832},[1807,4248,4250],{"className":4249,"ariaHidden":1837},[1836],[1807,4251,4253,4256],{"className":4252},[1841],[1807,4254],{"className":4255,"style":1846},[1845],[1807,4257,1970],{"className":4258,"style":2250},[1850,1851]," 系数相同。因此 ",[1807,4261,4263,4280],{"className":4262},[1810],[1807,4264,4266],{"className":4265},[1814],[1816,4267,4268],{"xmlns":1818},[1820,4269,4270,4278],{},[1823,4271,4272],{},[1936,4273,4274,4276],{},[1826,4275,1951],{},[1953,4277,1965],{},[1830,4279,2668],{"encoding":1832},[1807,4281,4283],{"className":4282,"ariaHidden":1837},[1836],[1807,4284,4286,4289],{"className":4285},[1841],[1807,4287],{"className":4288,"style":2144},[1845],[1807,4290,4292,4295],{"className":4291},[1850],[1807,4293,1951],{"className":4294,"style":2151},[1850,1851],[1807,4296,4298],{"className":4297},[2080],[1807,4299,4301,4321],{"className":4300},[2084,2085],[1807,4302,4304,4318],{"className":4303},[2089],[1807,4305,4307],{"className":4306,"style":2164},[2093],[1807,4308,4309,4312],{"style":2167},[1807,4310],{"className":4311,"style":2102},[2101],[1807,4313,4315],{"className":4314},[2106,2107,2108,2109],[1807,4316,1965],{"className":4317},[1850,2109],[1807,4319,2117],{"className":4320},[2116],[1807,4322,4324],{"className":4323},[2089],[1807,4325,4327],{"className":4326,"style":2124},[2093],[1807,4328],{}," 来自“控制变量相同的人之间仍剩下的 ",[1807,4331,4333,4346],{"className":4332},[1810],[1807,4334,4336],{"className":4335},[1814],[1816,4337,4338],{"xmlns":1818},[1820,4339,4340,4344],{},[1823,4341,4342],{},[1826,4343,1970],{},[1830,4345,1970],{"encoding":1832},[1807,4347,4349],{"className":4348,"ariaHidden":1837},[1836],[1807,4350,4352,4355],{"className":4351},[1841],[1807,4353],{"className":4354,"style":1846},[1845],[1807,4356,1970],{"className":4357,"style":2250},[1850,1851]," 差异”。如果剩余差异很少，重叠差、标准误大，模型也会依赖外推。",[1886,4360,4362],{"id":4361},"_4-哪些变量该控制","4. 哪些变量该控制",[4364,4365,4366,4382],"table",{},[4367,4368,4369],"thead",{},[4370,4371,4372,4376,4379],"tr",{},[4373,4374,4375],"th",{},"变量角色",[4373,4377,4378],{},"是否通常控制",[4373,4380,4381],{},"原因",[4383,4384,4385,4455,4495,4506,4546],"tbody",{},[4370,4386,4387,4449,4452],{},[4388,4389,4390,4391,4419,4420,4448],"td",{},"同时影响 ",[1807,4392,4394,4407],{"className":4393},[1810],[1807,4395,4397],{"className":4396},[1814],[1816,4398,4399],{"xmlns":1818},[1820,4400,4401,4405],{},[1823,4402,4403],{},[1826,4404,1970],{},[1830,4406,1970],{"encoding":1832},[1807,4408,4410],{"className":4409,"ariaHidden":1837},[1836],[1807,4411,4413,4416],{"className":4412},[1841],[1807,4414],{"className":4415,"style":1846},[1845],[1807,4417,1970],{"className":4418,"style":2250},[1850,1851]," 和 ",[1807,4421,4423,4436],{"className":4422},[1810],[1807,4424,4426],{"className":4425},[1814],[1816,4427,4428],{"xmlns":1818},[1820,4429,4430,4434],{},[1823,4431,4432],{},[1826,4433,1868],{},[1830,4435,1868],{"encoding":1832},[1807,4437,4439],{"className":4438,"ariaHidden":1837},[1836],[1807,4440,4442,4445],{"className":4441},[1841],[1807,4443],{"className":4444,"style":1846},[1845],[1807,4446,1868],{"className":4447,"style":1883},[1850,1851]," 的处理前混淆变量",[4388,4450,4451],{},"是",[4388,4453,4454],{},"关闭后门路径",[4370,4456,4457,4489,4492],{},[4388,4458,4459,4460,4488],{},"只预测 ",[1807,4461,4463,4476],{"className":4462},[1810],[1807,4464,4466],{"className":4465},[1814],[1816,4467,4468],{"xmlns":1818},[1820,4469,4470,4474],{},[1823,4471,4472],{},[1826,4473,1868],{},[1830,4475,1868],{"encoding":1832},[1807,4477,4479],{"className":4478,"ariaHidden":1837},[1836],[1807,4480,4482,4485],{"className":4481},[1841],[1807,4483],{"className":4484,"style":1846},[1845],[1807,4486,1868],{"className":4487,"style":1883},[1850,1851]," 的处理前变量",[4388,4490,4491],{},"可选",[4388,4493,4494],{},"常提高精度",[4370,4496,4497,4500,4503],{},[4388,4498,4499],{},"处理后的中介变量",[4388,4501,4502],{},"否，若目标是总效应",[4388,4504,4505],{},"会截断部分效应",[4370,4507,4508,4540,4543],{},[4388,4509,4510,4511,4539],{},"由 ",[1807,4512,4514,4527],{"className":4513},[1810],[1807,4515,4517],{"className":4516},[1814],[1816,4518,4519],{"xmlns":1818},[1820,4520,4521,4525],{},[1823,4522,4523],{},[1826,4524,1970],{},[1830,4526,1970],{"encoding":1832},[1807,4528,4530],{"className":4529,"ariaHidden":1837},[1836],[1807,4531,4533,4536],{"className":4532},[1841],[1807,4534],{"className":4535,"style":1846},[1845],[1807,4537,1970],{"className":4538,"style":2250},[1850,1851]," 与未观测因素共同造成的碰撞点",[4388,4541,4542],{},"否",[4388,4544,4545],{},"条件化会打开伪路径",[4370,4547,4548,4551,4554],{},[4388,4549,4550],{},"与研究人群几乎无重叠的变量组合",[4388,4552,4553],{},"不能靠回归解决",[4388,4555,4556],{},"系数来自函数形式外推",[1796,4558,4559],{},"例如评估培训对工资的总效应时，培训后取得的证书可能是机制，不应作为普通“控制变量”放入主回归。",[1886,4561,4563],{"id":4562},"_5-可运行案例能力遗漏与-fwl","5. 可运行案例：能力遗漏与 FWL",[1796,4565,4566],{},"代码生成一个真实教育效应为 1.5 的世界。能力同时影响教育与工资；研究者先看朴素回归，再加入能力，并用 FWL 复核。",[4568,4569],"pyodide",{"code64":4570,"layout":4571,"locale":7,"packages":4572,"title":4573},"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","vertical","numpy","Python：遗漏变量偏误、稳健标准误与 FWL",[1796,4575,4576],{},"这里“加入能力后接近真值”是因为模拟中能力被完整测量且模型设定正确。真实数据里的测量误差、未观测动机和学校选择仍可能存在。",[1886,4578,4580],{"id":4579},"_6-推断与报告","6. 推断与报告",[1796,4582,4583],{},"最少报告：",[4585,4586,4587,4590,4650,4653,4656],"ul",{},[1896,4588,4589],{},"系数、稳健或聚类标准误、95% 置信区间；",[1896,4591,4592,4620,4621,4649],{},[1807,4593,4595,4608],{"className":4594},[1810],[1807,4596,4598],{"className":4597},[1814],[1816,4599,4600],{"xmlns":1818},[1820,4601,4602,4606],{},[1823,4603,4604],{},[1826,4605,1868],{},[1830,4607,1868],{"encoding":1832},[1807,4609,4611],{"className":4610,"ariaHidden":1837},[1836],[1807,4612,4614,4617],{"className":4613},[1841],[1807,4615],{"className":4616,"style":1846},[1845],[1807,4618,1868],{"className":4619,"style":1883},[1850,1851],"、",[1807,4622,4624,4637],{"className":4623},[1810],[1807,4625,4627],{"className":4626},[1814],[1816,4628,4629],{"xmlns":1818},[1820,4630,4631,4635],{},[1823,4632,4633],{},[1826,4634,1970],{},[1830,4636,1970],{"encoding":1832},[1807,4638,4640],{"className":4639,"ariaHidden":1837},[1836],[1807,4641,4643,4646],{"className":4642},[1841],[1807,4644],{"className":4645,"style":1846},[1845],[1807,4647,1970],{"className":4648,"style":2250},[1850,1851]," 的单位和变换；",[1896,4651,4652],{},"样本量、缺失值处理与控制变量选择依据；",[1896,4654,4655],{},"处理分配层级与误差相关层级；",[1896,4657,4658,4659,4687],{},"经济量级，例如增加一年教育对应工资百分比变化，而不只给 ",[1807,4660,4662,4675],{"className":4661},[1810],[1807,4663,4665],{"className":4664},[1814],[1816,4666,4667],{"xmlns":1818},[1820,4668,4669,4673],{},[1823,4670,4671],{},[1826,4672,1796],{},[1830,4674,1796],{"encoding":1832},[1807,4676,4678],{"className":4677,"ariaHidden":1837},[1836],[1807,4679,4681,4684],{"className":4680},[1841],[1807,4682],{"className":4683,"style":3364},[1845],[1807,4685,1796],{"className":4686},[1850,1851]," 值。",[1796,4689,4690],{},"异方差稳健标准误修正的是方差估计，不会修复遗漏变量、反向因果或测量误差。",[1886,4692,4694],{"id":4693},"_7-快速诊断","7. 快速诊断",[1893,4696,4697,4732,4738,4744,4750,4756],{},[1896,4698,4699,4702,4703,4731],{},[1799,4700,4701],{},"支持集："," 处理组和比较组在 ",[1807,4704,4706,4719],{"className":4705},[1810],[1807,4707,4709],{"className":4708},[1814],[1816,4710,4711],{"xmlns":1818},[1820,4712,4713,4717],{},[1823,4714,4715],{},[1826,4716,1828],{},[1830,4718,1828],{"encoding":1832},[1807,4720,4722],{"className":4721,"ariaHidden":1837},[1836],[1807,4723,4725,4728],{"className":4724},[1841],[1807,4726],{"className":4727,"style":1846},[1845],[1807,4729,1828],{"className":4730,"style":1852},[1850,1851]," 上是否重叠？",[1896,4733,4734,4737],{},[1799,4735,4736],{},"函数形式："," 连续变量是否需要非线性或分段关系？",[1896,4739,4740,4743],{},[1799,4741,4742],{},"影响点："," 结论是否由少数极端观测驱动？",[1896,4745,4746,4749],{},[1799,4747,4748],{},"标准误："," 处理是否按学校、企业或地区分配？",[1896,4751,4752,4755],{},[1799,4753,4754],{},"控制顺序："," 控制是否在处理前测量？",[1896,4757,4758,4761],{},[1799,4759,4760],{},"敏感性："," 未观测混淆需要多强才能解释结果？",[1886,4763,4764],{"id":4764},"常见误区",[4585,4766,4767,4834,4837,4840,4843],{},[1896,4768,4769,4770,4833],{},"把高 ",[1807,4771,4773,4792],{"className":4772},[1810],[1807,4774,4776],{"className":4775},[1814],[1816,4777,4778],{"xmlns":1818},[1820,4779,4780,4789],{},[1823,4781,4782],{},[2037,4783,4784,4787],{},[1826,4785,4786],{},"R",[1953,4788,3152],{},[1830,4790,4791],{"encoding":1832},"R^2",[1807,4793,4795],{"className":4794,"ariaHidden":1837},[1836],[1807,4796,4798,4802],{"className":4797},[1841],[1807,4799],{"className":4800,"style":4801},[1845],"height:0.8141em;",[1807,4803,4805,4809],{"className":4804},[1850],[1807,4806,4786],{"className":4807,"style":4808},[1850,1851],"margin-right:0.0077em;",[1807,4810,4812],{"className":4811},[2080],[1807,4813,4815],{"className":4814},[2084],[1807,4816,4818],{"className":4817},[2089],[1807,4819,4821],{"className":4820,"style":4801},[2093],[1807,4822,4824,4827],{"style":4823},"top:-3.063em;margin-right:0.05em;",[1807,4825],{"className":4826,"style":2102},[2101],[1807,4828,4830],{"className":4829},[2106,2107,2108,2109],[1807,4831,3152],{"className":4832},[1850,2109]," 当作因果可信度；",[1896,4835,4836],{},"逐步回归后只保留显著控制变量；",[1896,4838,4839],{},"控制处理后结果，仍把系数称为总效应；",[1896,4841,4842],{},"对数因变量的系数直接说成精确百分比，忽略近似条件；",[1896,4844,4845],{},"只换标准误，不重新审查识别设计。",[1886,4847,4848],{"id":4848},"课堂任务",[1796,4850,4851],{},"为“远程办公对生产率的影响”列出：",[1893,4853,4854,4857,4860,4863,4866],{},[1896,4855,4856],{},"两个处理前混淆变量；",[1896,4858,4859],{},"一个可能的坏控制；",[1896,4861,4862],{},"一个需要聚类的层级；",[1896,4864,4865],{},"一个能暴露函数形式问题的图；",[1896,4867,4868],{},"一句话说明 OLS 系数在什么假设下才是因果效应。",[1886,4870,4871],{"id":4871},"核心阅读",[4585,4873,4874,4887,4897],{},[1896,4875,4876,4877,4227],{},"Wooldridge, ",[4878,4879,4883],"a",{"href":4880,"rel":4881},"https:\u002F\u002Fmitpress.mit.edu\u002F9780262232586\u002Feconometric-analysis-of-cross-section-and-panel-data\u002F",[4882],"nofollow",[4884,4885,4886],"em",{},"Econometric Analysis of Cross Section and Panel Data",[1896,4888,4889,4890,4227],{},"Angrist & Pischke, ",[4878,4891,4894],{"href":4892,"rel":4893},"https:\u002F\u002Fwww.mostlyharmlesseconometrics.com\u002F",[4882],[4884,4895,4896],{},"Mostly Harmless Econometrics",[1896,4898,4899,4900,4227],{},"Cinelli, Ferwerda & Hazlett (2020), ",[4878,4901,4904],{"href":4902,"rel":4903},"https:\u002F\u002Fdoi.org\u002F10.1111\u002Frssa.12537",[4882],"“Sensemakr: Sensitivity Analysis Tools for OLS”",[1796,4906,4907,4908,4912,4913,4227],{},"上一章：",[4878,4909,4911],{"href":4910},"..\u002F01-intro\u002F","因果推断导论","｜下一章：",[4878,4914,4916],{"href":4915},"..\u002F03-iv\u002F","工具变量法",{"title":10,"searchDepth":4918,"depth":4918,"links":4919},2,[4920,4921,4922,4923,4924,4925,4926,4927,4928,4929,4930],{"id":1888,"depth":4918,"text":1888},{"id":1913,"depth":4918,"text":1914},{"id":3104,"depth":4918,"text":3105},{"id":3846,"depth":4918,"text":3847},{"id":4361,"depth":4918,"text":4362},{"id":4562,"depth":4918,"text":4563},{"id":4579,"depth":4918,"text":4580},{"id":4693,"depth":4918,"text":4694},{"id":4764,"depth":4918,"text":4764},{"id":4848,"depth":4918,"text":4848},{"id":4871,"depth":4918,"text":4871},"把 OLS 当作线性投影与比较工具，掌握控制变量、遗漏变量、FWL 与稳健推断。","md",{"sidebar":4934},{"order":4918},true,{"title":1480,"description":4931},"drVcQxRFh-v5fSPy4Ct_UQ7vg4BDD6ew0a-CmjOIz-o",[4939,4941],{"title":1474,"path":1475,"stem":1476,"description":4940,"children":-1},"从潜在结果、目标参数与选择偏误出发，建立微观计量研究的完整识别语言。",{"title":1486,"path":1487,"stem":1488,"description":4942,"children":-1},"用鼓励设计理解相关性、排除限制、独立性、单调性、弱工具与 LATE。",1785754751235]