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Search, Evaluate, and Read Evidence","\u002Fen\u002Facademic-writing\u002F02-reading-and-literature-matrix","en\u002Facademic-writing\u002F02-reading-and-literature-matrix",{"title":27,"path":28,"stem":29},"3. From Sources to Synthesis and Argument","\u002Fen\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs","en\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs",{"title":31,"path":32,"stem":33},"4. Literature Review and a Defensible Gap","\u002Fen\u002Facademic-writing\u002F04-writing-the-literature-review","en\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":35,"path":36,"stem":37},"5. Research Design, Evidence, and Core Sections","\u002Fen\u002Facademic-writing\u002F05-core-sections-and-evidence","en\u002Facademic-writing\u002F05-core-sections-and-evidence",{"title":39,"path":40,"stem":41},"6. Citation, Paraphrasing, Integrity, and AI","\u002Fen\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity","en\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity",{"title":43,"path":44,"stem":45},"7. Revision, Review, and Submission","\u002Fen\u002Facademic-writing\u002F07-revision-style-and-submission","en\u002Facademic-writing\u002F07-revision-style-and-submission",{"title":47,"path":48,"stem":49},"8. Research Workbook","\u002Fen\u002Facademic-writing\u002F08-literature-review-checklist-and-template","en\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":51,"path":52,"stem":53},"9. Research Workflow and Tools","\u002Fen\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management","en\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management",{"title":55,"path":56,"stem":57},"10. Worked Project — From Question to Defensible Conclusion","\u002Fen\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain","en\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain",{"title":59,"path":60,"stem":61,"children":62,"page":249},"Accounting","\u002Fen\u002Faccounting","en\u002Faccounting",[63,69,87,93,193],{"title":64,"path":65,"stem":66,"children":67},"Accounting — From Evidence to Decisions","\u002Fen\u002Faccounting\u002F00-index","en\u002Faccounting\u002F00-index",[68],{"title":64,"path":65,"stem":66},{"title":70,"path":71,"stem":72,"children":73},"Accounting Appendix","\u002Fen\u002Faccounting\u002Fappendix","en\u002Faccounting\u002Fappendix\u002Findex",[74,75,79,83],{"title":70,"path":71,"stem":72},{"title":76,"path":77,"stem":78},"Worked Examples and Error Diagnosis","\u002Fen\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls","en\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls",{"title":80,"path":81,"stem":82},"Accounting Glossary","\u002Fen\u002Faccounting\u002Fappendix\u002F26-glossary","en\u002Faccounting\u002Fappendix\u002F26-glossary",{"title":84,"path":85,"stem":86},"Reading and Evidence Map","\u002Fen\u002Faccounting\u002Fappendix\u002F27-reading-map","en\u002Faccounting\u002Fappendix\u002F27-reading-map",{"title":88,"path":89,"stem":90,"children":91},"Northstar Record-to-Decision Capstone","\u002Fen\u002Faccounting\u002Fcapstone","en\u002Faccounting\u002Fcapstone\u002Findex",[92],{"title":88,"path":89,"stem":90},{"title":94,"path":95,"stem":96,"children":97},"Financial Accounting","\u002Fen\u002Faccounting\u002Ffinancial-accounting","en\u002Faccounting\u002Ffinancial-accounting\u002Findex",[98,99,117,131,165,179],{"title":94,"path":95,"stem":96},{"title":100,"path":101,"stem":102,"children":103},"1. Foundations","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002Findex",[104,105,109,113],{"title":100,"path":101,"stem":102},{"title":106,"path":107,"stem":108},"Objectives and Qualitative Characteristics","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics",{"title":110,"path":111,"stem":112},"Equation and Elements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements",{"title":114,"path":115,"stem":116},"Accrual Basis, Estimates and Periods","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles",{"title":118,"path":119,"stem":120,"children":121},"2. Recording Transactions","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002Findex",[122,123,127],{"title":118,"path":119,"stem":120},{"title":124,"path":125,"stem":126},"Double-Entry and Debit\u002FCredit","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr",{"title":128,"path":129,"stem":130},"Accounting Cycle","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle",{"title":132,"path":133,"stem":134,"children":135},"3. Measurement and Adjustments","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002Findex",[136,137,141,145,149,153,157,161],{"title":132,"path":133,"stem":134},{"title":138,"path":139,"stem":140},"Revenue Recognition","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition",{"title":142,"path":143,"stem":144},"Inventory","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory",{"title":146,"path":147,"stem":148},"Receivables and Expected Credit Losses","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables",{"title":150,"path":151,"stem":152},"Property, Plant and Equipment","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe",{"title":154,"path":155,"stem":156},"Intangible Assets","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles",{"title":158,"path":159,"stem":160},"Leases","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases",{"title":162,"path":163,"stem":164},"Current and Deferred Income Tax","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax",{"title":166,"path":167,"stem":168,"children":169},"4. Reporting and Cash","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002Findex",[170,171,175],{"title":166,"path":167,"stem":168},{"title":172,"path":173,"stem":174},"Linked Financial Statements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements",{"title":176,"path":177,"stem":178},"Cash, Reconciliation and Internal Control","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control",{"title":180,"path":181,"stem":182,"children":183},"5. Analysis and Comparison","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002Findex",[184,185,189],{"title":180,"path":181,"stem":182},{"title":186,"path":187,"stem":188},"Ratio Analysis","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis",{"title":190,"path":191,"stem":192},"IFRS versus US GAAP","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap",{"title":194,"path":195,"stem":196,"children":197},"Management Accounting","\u002Fen\u002Faccounting\u002Fmanagement-accounting","en\u002Faccounting\u002Fmanagement-accounting\u002Findex",[198,199,217,235],{"title":194,"path":195,"stem":196},{"title":200,"path":201,"stem":202,"children":203},"1. Cost Foundations","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002Findex",[204,205,209,213],{"title":200,"path":201,"stem":202},{"title":206,"path":207,"stem":208},"Cost Concepts and Behaviour","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts",{"title":210,"path":211,"stem":212},"Costing Systems","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems",{"title":214,"path":215,"stem":216},"Variable and Absorption Costing","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption",{"title":218,"path":219,"stem":220,"children":221},"2. Planning and Control","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002Findex",[222,223,227,231],{"title":218,"path":219,"stem":220},{"title":224,"path":225,"stem":226},"Cost–Volume–Profit Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis",{"title":228,"path":229,"stem":230},"Budgeting and Variance Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances",{"title":232,"path":233,"stem":234},"Performance Measurement","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement",{"title":236,"path":237,"stem":238,"children":239},"3. Decisions and Investment","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002Findex",[240,241,245],{"title":236,"path":237,"stem":238},{"title":242,"path":243,"stem":244},"Short-Term Decisions","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions",{"title":246,"path":247,"stem":248},"Capital Budgeting","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting",false,{"title":251,"path":252,"stem":253,"children":254,"page":249},"Business Analytics","\u002Fen\u002Fbusiness-analytics","en\u002Fbusiness-analytics",[255,261,279,305,335,365,383,400],{"title":256,"path":257,"stem":258,"children":259},"Business Analytics — From Data to Defensible Action","\u002Fen\u002Fbusiness-analytics\u002F00-index","en\u002Fbusiness-analytics\u002F00-index",[260],{"title":256,"path":257,"stem":258},{"title":262,"path":263,"stem":264,"children":265},"0. Decision and Data Foundations","\u002Fen\u002Fbusiness-analytics\u002F00-intro","en\u002Fbusiness-analytics\u002F00-intro\u002Findex",[266,267,271,275],{"title":262,"path":263,"stem":264},{"title":268,"path":269,"stem":270},"Decision Framing","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing","en\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing",{"title":272,"path":273,"stem":274},"Data Contracts","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts","en\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts",{"title":276,"path":277,"stem":278},"Reproducible Analytics Workflow","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow","en\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow",{"title":280,"path":281,"stem":282,"children":283},"1. Descriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive","en\u002Fbusiness-analytics\u002F01-descriptive\u002Findex",[284,285,289,293,297,301],{"title":280,"path":281,"stem":282},{"title":286,"path":287,"stem":288},"Distributions and Exploratory Data Analysis","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda","en\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda",{"title":290,"path":291,"stem":292},"KPIs and Denominators","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators","en\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators",{"title":294,"path":295,"stem":296},"Segments, Cohorts and Funnels","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels","en\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels",{"title":298,"path":299,"stem":300},"Visual Evidence and Data Stories","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story","en\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story",{"title":302,"path":303,"stem":304},"Experiments and Causal Boundaries","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary","en\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary",{"title":306,"path":307,"stem":308,"children":309},"2. Predictive Analytics","\u002Fen\u002Fbusiness-analytics\u002F02-predictive","en\u002Fbusiness-analytics\u002F02-predictive\u002Findex",[310,311,315,319,323,327,331],{"title":306,"path":307,"stem":308},{"title":312,"path":313,"stem":314},"Validation, Baselines and Leakage","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage","en\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage",{"title":316,"path":317,"stem":318},"Regression and Forecasting","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting","en\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting",{"title":320,"path":321,"stem":322},"Classification and Calibration","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration","en\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration",{"title":324,"path":325,"stem":326},"Trees and Ensembles","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles","en\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles",{"title":328,"path":329,"stem":330},"Decision Metrics and Thresholds","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds","en\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds",{"title":332,"path":333,"stem":334},"Explainability, Monitoring and Drift","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift","en\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift",{"title":336,"path":337,"stem":338,"children":339},"3. Prescriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive","en\u002Fbusiness-analytics\u002F03-prescriptive\u002Findex",[340,341,345,349,353,357,361],{"title":336,"path":337,"stem":338},{"title":342,"path":343,"stem":344},"Decision-Making under Uncertainty","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty",{"title":346,"path":347,"stem":348},"Linear Optimisation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization",{"title":350,"path":351,"stem":352},"Inventory and Allocation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation",{"title":354,"path":355,"stem":356},"Queueing and Simulation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation",{"title":358,"path":359,"stem":360},"Pricing and Revenue Management","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation",{"title":362,"path":363,"stem":364},"Causal Targeting and Policy Learning","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting",{"title":366,"path":367,"stem":368,"children":369},"4. Deployment and Governance","\u002Fen\u002Fbusiness-analytics\u002F04-deployment","en\u002Fbusiness-analytics\u002F04-deployment\u002Findex",[370,371,375,379],{"title":366,"path":367,"stem":368},{"title":372,"path":373,"stem":374},"Data Products and Monitoring","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring","en\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring",{"title":376,"path":377,"stem":378},"Governance, Fairness and Privacy","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy","en\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy",{"title":380,"path":381,"stem":382},"Adoption and Business Value","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value","en\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value",{"title":384,"path":385,"stem":386,"children":387},"Appendix and Revision Tools","\u002Fen\u002Fbusiness-analytics\u002Fappendix","en\u002Fbusiness-analytics\u002Fappendix\u002Findex",[388,389,393,397],{"title":384,"path":385,"stem":386},{"title":390,"path":391,"stem":392},"Formula and Decision Map","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map","en\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map",{"title":394,"path":395,"stem":396},"Worked Examples and Pitfalls","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls","en\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls",{"title":84,"path":398,"stem":399},"\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map","en\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map",{"title":401,"path":402,"stem":403,"children":404},"Capstone — The Evening Delivery Promise","\u002Fen\u002Fbusiness-analytics\u002Fcapstone","en\u002Fbusiness-analytics\u002Fcapstone\u002Findex",[405],{"title":401,"path":402,"stem":403},{"title":407,"path":408,"stem":409,"children":410,"page":249},"Financial Economic Time Series","\u002Fen\u002Ffinancial-economic-time-series","en\u002Ffinancial-economic-time-series",[411,417,423,429,435,441,447,453,459,465,471],{"title":412,"path":413,"stem":414,"children":415},"Financial and Economic Time Series — Course Guide","\u002Fen\u002Ffinancial-economic-time-series\u002F00-intro","en\u002Ffinancial-economic-time-series\u002F00-intro\u002Findex",[416],{"title":412,"path":413,"stem":414},{"title":418,"path":419,"stem":420,"children":421},"Three Versions of Time Series","\u002Fen\u002Ffinancial-economic-time-series\u002F01-bridge","en\u002Ffinancial-economic-time-series\u002F01-bridge\u002Findex",[422],{"title":418,"path":419,"stem":420},{"title":424,"path":425,"stem":426,"children":427},"Data, Clocks, and Transformations","\u002Fen\u002Ffinancial-economic-time-series\u002F02-data-transformations","en\u002Ffinancial-economic-time-series\u002F02-data-transformations\u002Findex",[428],{"title":424,"path":425,"stem":426},{"title":430,"path":431,"stem":432,"children":433},"Predictive Regressions and Persistent Predictors","\u002Fen\u002Ffinancial-economic-time-series\u002F03-predictive-regressions","en\u002Ffinancial-economic-time-series\u002F03-predictive-regressions\u002Findex",[434],{"title":430,"path":431,"stem":432},{"title":436,"path":437,"stem":438,"children":439},"Volatility, Tails, and Financial Risk","\u002Fen\u002Ffinancial-economic-time-series\u002F04-volatility-risk","en\u002Ffinancial-economic-time-series\u002F04-volatility-risk\u002Findex",[440],{"title":436,"path":437,"stem":438},{"title":442,"path":443,"stem":444,"children":445},"Unit Roots, Cointegration, and Error Correction","\u002Fen\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration","en\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration\u002Findex",[446],{"title":442,"path":443,"stem":444},{"title":448,"path":449,"stem":450,"children":451},"VAR, Structural Identification, and Local Projections","\u002Fen\u002Ffinancial-economic-time-series\u002F06-var-identification","en\u002Ffinancial-economic-time-series\u002F06-var-identification\u002Findex",[452],{"title":448,"path":449,"stem":450},{"title":454,"path":455,"stem":456,"children":457},"State Space, Mixed Frequency, and Nowcasting","\u002Fen\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting","en\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting\u002Findex",[458],{"title":454,"path":455,"stem":456},{"title":460,"path":461,"stem":462,"children":463},"Forecast Evaluation for Decisions","\u002Fen\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation","en\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation\u002Findex",[464],{"title":460,"path":461,"stem":462},{"title":466,"path":467,"stem":468,"children":469},"Integrated R Laboratory","\u002Fen\u002Ffinancial-economic-time-series\u002F09-r-laboratory","en\u002Ffinancial-economic-time-series\u002F09-r-laboratory\u002Findex",[470],{"title":466,"path":467,"stem":468},{"title":472,"path":473,"stem":474,"children":475},"Capstones, Data, and Reading Ladder","\u002Fen\u002Ffinancial-economic-time-series\u002F10-capstone-readings","en\u002Ffinancial-economic-time-series\u002F10-capstone-readings\u002Findex",[476],{"title":472,"path":473,"stem":474},{"title":478,"path":479,"stem":480,"children":481,"page":249},"Intro To Economics","\u002Fen\u002Fintro-to-economics","en\u002Fintro-to-economics",[482,486,490,494,498,502,506,510,514,518,522,526,530],{"title":483,"path":484,"stem":485},"Introduction to Economics — Decisions, Markets, and the Macroeconomy","\u002Fen\u002Fintro-to-economics\u002F00-intro","en\u002Fintro-to-economics\u002F00-intro",{"title":487,"path":488,"stem":489},"Chapter 1 — Choice, Opportunity Cost, and Trade","\u002Fen\u002Fintro-to-economics\u002F01-foundations","en\u002Fintro-to-economics\u002F01-foundations",{"title":491,"path":492,"stem":493},"Chapter 2 — Demand, Supply, Equilibrium, and Welfare","\u002Fen\u002Fintro-to-economics\u002F02-demand-and-supply","en\u002Fintro-to-economics\u002F02-demand-and-supply",{"title":495,"path":496,"stem":497},"Chapter 3 — Elasticity, Revenue, and Tax Incidence","\u002Fen\u002Fintro-to-economics\u002F03-elasticity","en\u002Fintro-to-economics\u002F03-elasticity",{"title":499,"path":500,"stem":501},"Chapter 4 — Firms, Market Power, and Market Failure","\u002Fen\u002Fintro-to-economics\u002F04-market-structures","en\u002Fintro-to-economics\u002F04-market-structures",{"title":503,"path":504,"stem":505},"Chapter 5 — GDP, Income, Wealth, and Welfare","\u002Fen\u002Fintro-to-economics\u002F05-gdp-and-wealth","en\u002Fintro-to-economics\u002F05-gdp-and-wealth",{"title":507,"path":508,"stem":509},"Chapter 6 — Inflation, Purchasing Power, and Labour Markets","\u002Fen\u002Fintro-to-economics\u002F06-inflation-and-unemployment","en\u002Fintro-to-economics\u002F06-inflation-and-unemployment",{"title":511,"path":512,"stem":513},"Chapter 7 — Productivity, Technology, and Economic Growth","\u002Fen\u002Fintro-to-economics\u002F07-economic-growth","en\u002Fintro-to-economics\u002F07-economic-growth",{"title":515,"path":516,"stem":517},"Chapter 8 — Money, Credit, and Banking","\u002Fen\u002Fintro-to-economics\u002F08-money-and-banking","en\u002Fintro-to-economics\u002F08-money-and-banking",{"title":519,"path":520,"stem":521},"Chapter 9 — Business Cycles, AD–AS, and Monetary Policy","\u002Fen\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as","en\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as",{"title":523,"path":524,"stem":525},"Chapter 10 — Fiscal Policy, Distribution, and Public Debt","\u002Fen\u002Fintro-to-economics\u002F10-fiscal-policy","en\u002Fintro-to-economics\u002F10-fiscal-policy",{"title":527,"path":528,"stem":529},"Chapter 11 — Trade, Capital Flows, and Exchange Rates","\u002Fen\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates","en\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates",{"title":531,"path":532,"stem":533},"Chapter 12 — Integrated Economic Analysis Studio","\u002Fen\u002Fintro-to-economics\u002F12-review-and-case-studies","en\u002Fintro-to-economics\u002F12-review-and-case-studies",{"title":535,"path":536,"stem":537,"children":538,"page":249},"Microeconometrics","\u002Fen\u002Fmicroeconometrics","en\u002Fmicroeconometrics",[539,557,563,581,603,629,651,673,691,709],{"title":540,"path":541,"stem":542,"children":543},"0. Causal Questions and Designs","\u002Fen\u002Fmicroeconometrics\u002F00-foundations","en\u002Fmicroeconometrics\u002F00-foundations\u002Findex",[544,545,549,553],{"title":540,"path":541,"stem":542},{"title":546,"path":547,"stem":548},"Causal Questions and Estimands","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands","en\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands",{"title":550,"path":551,"stem":552},"Potential Outcomes and Experiments","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments","en\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments",{"title":554,"path":555,"stem":556},"Causal Diagrams and Controls","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls","en\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls",{"title":558,"path":559,"stem":560,"children":561},"Microeconometrics — Designing Credible Counterfactuals","\u002Fen\u002Fmicroeconometrics\u002F00-index","en\u002Fmicroeconometrics\u002F00-index",[562],{"title":558,"path":559,"stem":560},{"title":564,"path":565,"stem":566,"children":567},"1. Regression and Inference","\u002Fen\u002Fmicroeconometrics\u002F01-regression","en\u002Fmicroeconometrics\u002F01-regression\u002Findex",[568,569,573,577],{"title":564,"path":565,"stem":566},{"title":570,"path":571,"stem":572},"OLS as a Projection","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection","en\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection",{"title":574,"path":575,"stem":576},"FWL, Selection and Controls","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls","en\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls",{"title":578,"path":579,"stem":580},"Inference and Clustering","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering","en\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering",{"title":582,"path":583,"stem":584,"children":585},"2. Instruments and Panel Data","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002Findex",[586,587,591,595,599],{"title":582,"path":583,"stem":584},{"title":588,"path":589,"stem":590},"Instrumental Variables","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification",{"title":592,"path":593,"stem":594},"Weak Instruments and LATE","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late",{"title":596,"path":597,"stem":598},"Panel Fixed Effects","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects",{"title":600,"path":601,"stem":602},"Dynamic Panels and Limits","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel",{"title":604,"path":605,"stem":606,"children":607},"3. Policy Evaluation Designs","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs","en\u002Fmicroeconometrics\u002F03-policy-designs\u002Findex",[608,609,613,617,621,625],{"title":604,"path":605,"stem":606},{"title":610,"path":611,"stem":612},"Difference-in-Differences","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core",{"title":614,"path":615,"stem":616},"Staggered DID and Event Studies","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies",{"title":618,"path":619,"stem":620},"Regression Discontinuity","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd",{"title":622,"path":623,"stem":624},"Matching, Weighting and Overlap","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting",{"title":626,"path":627,"stem":628},"Synthetic Control","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control",{"title":630,"path":631,"stem":632,"children":633},"Module 4 — Choice and Limited Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002Findex",[634,635,639,643,647],{"title":630,"path":631,"stem":632},{"title":636,"path":637,"stem":638},"Binary Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice",{"title":640,"path":641,"stem":642},"Multinomial and Ordered Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice",{"title":644,"path":645,"stem":646},"Count Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes",{"title":648,"path":649,"stem":650},"Censoring, Truncation and Selection","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection",{"title":652,"path":653,"stem":654,"children":655},"Module 5 — Modern Causal Analysis","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal","en\u002Fmicroeconometrics\u002F05-modern-causal\u002Findex",[656,657,661,665,669],{"title":652,"path":653,"stem":654},{"title":658,"path":659,"stem":660},"Double Machine Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml",{"title":662,"path":663,"stem":664},"Heterogeneity and Policy Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy",{"title":666,"path":667,"stem":668},"Sensitivity and Partial Identification","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id",{"title":670,"path":671,"stem":672},"External Validity and Transport","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity",{"title":674,"path":675,"stem":676,"children":677},"Module 6 — From Data to Auditable Evidence","\u002Fen\u002Fmicroeconometrics\u002F06-workflow","en\u002Fmicroeconometrics\u002F06-workflow\u002Findex",[678,679,683,687],{"title":674,"path":675,"stem":676},{"title":680,"path":681,"stem":682},"Data Provenance","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance","en\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance",{"title":684,"path":685,"stem":686},"Reproducibility and Reporting","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting","en\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting",{"title":688,"path":689,"stem":690},"Paper and Evidence Audit","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit","en\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit",{"title":692,"path":693,"stem":694,"children":695},"Appendix — Revision and Evidence Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix","en\u002Fmicroeconometrics\u002Fappendix\u002Findex",[696,697,701,705],{"title":692,"path":693,"stem":694},{"title":698,"path":699,"stem":700},"Formula and Design Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map","en\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map",{"title":702,"path":703,"stem":704},"Ten Worked Microeconometric Pitfalls","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls","en\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls",{"title":706,"path":707,"stem":708},"Reading and Software Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map","en\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map",{"title":710,"path":711,"stem":712,"children":713},"Capstone — Should Northbridge Expand Pathways?","\u002Fen\u002Fmicroeconometrics\u002Fcapstone","en\u002Fmicroeconometrics\u002Fcapstone\u002Findex",[714],{"title":710,"path":711,"stem":712},{"title":716,"path":717,"stem":718,"children":719,"page":249},"Microeconomics","\u002Fen\u002Fmicroeconomics","en\u002Fmicroeconomics",[720,726,732,738,744,750,756,762,768,774,780,786,792],{"title":721,"path":722,"stem":723,"children":724},"Advanced Microeconomics — Course Guide","\u002Fen\u002Fmicroeconomics\u002F00-intro","en\u002Fmicroeconomics\u002F00-intro\u002Findex",[725],{"title":721,"path":722,"stem":723},{"title":727,"path":728,"stem":729,"children":730},"Module 1 — Choice, Duality, and Revealed Preference","\u002Fen\u002Fmicroeconomics\u002F01-fundations","en\u002Fmicroeconomics\u002F01-fundations\u002Findex",[731],{"title":727,"path":728,"stem":729},{"title":733,"path":734,"stem":735,"children":736},"Module 2 — Comparative Statics and Welfare Measurement","\u002Fen\u002Fmicroeconomics\u002F02-comparative-statics","en\u002Fmicroeconomics\u002F02-comparative-statics\u002Findex",[737],{"title":733,"path":734,"stem":735},{"title":739,"path":740,"stem":741,"children":742},"Module 3 — Choice under Risk and Insurance","\u002Fen\u002Fmicroeconomics\u002F03-uncertainty","en\u002Fmicroeconomics\u002F03-uncertainty\u002Findex",[743],{"title":739,"path":740,"stem":741},{"title":745,"path":746,"stem":747,"children":748},"Module 4 — General Equilibrium and Welfare","\u002Fen\u002Fmicroeconomics\u002F04-general-equilibrium","en\u002Fmicroeconomics\u002F04-general-equilibrium\u002Findex",[749],{"title":745,"path":746,"stem":747},{"title":751,"path":752,"stem":753,"children":754},"Module 5 — Static, Dynamic, and Repeated Games","\u002Fen\u002Fmicroeconomics\u002F05-game-theory","en\u002Fmicroeconomics\u002F05-game-theory\u002Findex",[755],{"title":751,"path":752,"stem":753},{"title":757,"path":758,"stem":759,"children":760},"Module 6 — Oligopoly, Entry, and Algorithmic Pricing","\u002Fen\u002Fmicroeconomics\u002F06-oligopoly","en\u002Fmicroeconomics\u002F06-oligopoly\u002Findex",[761],{"title":757,"path":758,"stem":759},{"title":763,"path":764,"stem":765,"children":766},"Module 7 — Information Economics and Contracts","\u002Fen\u002Fmicroeconomics\u002F07-information-economics","en\u002Fmicroeconomics\u002F07-information-economics\u002Findex",[767],{"title":763,"path":764,"stem":765},{"title":769,"path":770,"stem":771,"children":772},"Module 8 — Mechanism Design and Auctions","\u002Fen\u002Fmicroeconomics\u002F08-mechanism-design","en\u002Fmicroeconomics\u002F08-mechanism-design\u002Findex",[773],{"title":769,"path":770,"stem":771},{"title":775,"path":776,"stem":777,"children":778},"Module 9 — Behavioural and Experimental Microeconomics","\u002Fen\u002Fmicroeconomics\u002F09-behavioural-economics","en\u002Fmicroeconomics\u002F09-behavioural-economics\u002Findex",[779],{"title":775,"path":776,"stem":777},{"title":781,"path":782,"stem":783,"children":784},"Module 10 — Externalities, Public Goods, and Collective Action","\u002Fen\u002Fmicroeconomics\u002F10-externalities-public-goods","en\u002Fmicroeconomics\u002F10-externalities-public-goods\u002Findex",[785],{"title":781,"path":782,"stem":783},{"title":787,"path":788,"stem":789,"children":790},"Module 11 — Matching and Market Design","\u002Fen\u002Fmicroeconomics\u002F11-market-design","en\u002Fmicroeconomics\u002F11-market-design\u002Findex",[791],{"title":787,"path":788,"stem":789},{"title":793,"path":794,"stem":795,"children":796},"Module 12 — Integrated Microeconomic Design Studio","\u002Fen\u002Fmicroeconomics\u002F12-review","en\u002Fmicroeconomics\u002F12-review\u002Findex",[797],{"title":793,"path":794,"stem":795},{"title":799,"path":800,"stem":801,"children":802},"Playground","\u002Fen\u002Fplayground","en\u002Fplayground\u002Findex",[803,804,808,812,816,820],{"title":799,"path":800,"stem":801},{"title":805,"path":806,"stem":807},"Typst Plugin Playground","\u002Fen\u002Fplayground\u002F01-typstex","en\u002Fplayground\u002F01-typstex",{"title":809,"path":810,"stem":811},"Citation Plugin Test","\u002Fen\u002Fplayground\u002F02-citation","en\u002Fplayground\u002F02-citation",{"title":813,"path":814,"stem":815},"Pyodide Playground","\u002Fen\u002Fplayground\u002F03-pyodide","en\u002Fplayground\u002F03-pyodide",{"title":817,"path":818,"stem":819},"WebR Playground","\u002Fen\u002Fplayground\u002F04-webr","en\u002Fplayground\u002F04-webr",{"title":821,"path":822,"stem":823},"Chart.js 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文献综述的构建与写作","\u002Fzh\u002Facademic-writing\u002F04-writing-the-literature-review","zh\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":1056,"path":1057,"stem":1058},"8. 文献综述清单与模板","\u002Fzh\u002Facademic-writing\u002F08-literature-review-checklist-and-template","zh\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":1060,"path":1061,"stem":1062},"10. 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Alpha 策略全流程","\u002Fzh\u002Fquant\u002F09-case-study","zh\u002Fquant\u002F09-case-study",{"title":1779,"path":1780,"stem":1781},"量化投资文献与软件图谱","\u002Fzh\u002Fquant\u002F10-reading-software-map","zh\u002Fquant\u002F10-reading-software-map",null,{"id":1784,"title":1330,"body":1785,"description":4238,"extension":4239,"features":1782,"hero":1782,"layout":1782,"locale":1782,"meta":4240,"navigation":1782,"path":1331,"published":4241,"seo":4242,"stem":1332,"__hash__":4243},"docs\u002Fzh\u002Feconometrics\u002F01-data-and-regression.md",{"type":1786,"value":1787,"toc":4226},"minimark",[1788,1792,1797,1801,1804,1817,1820,1823,1841,1845,2261,2264,2268,2271,2417,2525,2897,2975,2979,3071,3241,3585,3589,4170,4173,4177,4180,4183,4197,4200,4203,4220,4223],[1789,1790,1330],"h1",{"id":1791},"第一章数据概率与回归对象",[1793,1794,1796],"h2",{"id":1795},"_1-计量问题从对象开始","1. 计量问题从“对象”开始",[1798,1799,1800],"p",{},"在回归软件中输入变量之前，先说明：研究对象是谁、观察单位是什么、结果变量如何定义、解释变量如何测量，以及你想描述还是想识别因果效应。",[1798,1802,1803],{},"例如，“教育影响工资”至少有三种不同问题：",[1805,1806,1807,1811,1814],"ol",{},[1808,1809,1810],"li",{},"受教育年限与工资的条件相关是什么？",[1808,1812,1813],{},"对一个随机选择的个体，额外教育一年的潜在工资变化是什么？",[1808,1815,1816],{},"某项教育政策使受影响者的工资改变多少？",[1798,1818,1819],{},"三者可能使用相似数据和回归形式，但目标参数与识别条件不同。",[1793,1821,1822],{"id":1822},"学习目标",[1824,1825,1826,1829,1832,1835,1838],"ul",{},[1808,1827,1828],{},"区分总体、样本、参数、统计量和估计量；",[1808,1830,1831],{},"用条件期望函数描述预测与因果问题；",[1808,1833,1834],{},"解释抽样误差、测量误差和选择进入数据的差异；",[1808,1836,1837],{},"理解线性投影不要求真实条件期望函数完全线性；",[1808,1839,1840],{},"为 OLS 推导准备矩阵和概率语言。",[1793,1842,1844],{"id":1843},"_2-随机变量与数据生成过程","2. 随机变量与数据生成过程",[1798,1846,1847,1848,2102,2103,2260],{},"把一个观测写成 ",[1849,1850,1853,1911],"span",{"className":1851},[1852],"katex",[1849,1854,1857],{"className":1855},[1856],"katex-mathml",[1858,1859,1861],"math",{"xmlns":1860},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML",[1862,1863,1864,1906],"semantics",{},[1865,1866,1867,1877,1881,1885,1892,1896,1903],"mrow",{},[1868,1869,1870,1874],"msub",{},[1871,1872,1873],"mi",{},"W",[1871,1875,1876],{},"i",[1878,1879,1880],"mo",{},"=",[1878,1882,1884],{"stretchy":1883},"false","(",[1868,1886,1887,1890],{},[1871,1888,1889],{},"Y",[1871,1891,1876],{},[1878,1893,1895],{"separator":1894},"true",",",[1868,1897,1898,1901],{},[1871,1899,1900],{},"X",[1871,1902,1876],{},[1878,1904,1905],{"stretchy":1883},")",[1907,1908,1910],"annotation",{"encoding":1909},"application\u002Fx-tex","W_i=(Y_i,X_i)",[1849,1912,1915,1995],{"className":1913,"ariaHidden":1894},[1914],"katex-html",[1849,1916,1919,1924,1983,1988,1992],{"className":1917},[1918],"base",[1849,1920],{"className":1921,"style":1923},[1922],"strut","height:0.8333em;vertical-align:-0.15em;",[1849,1925,1928,1933],{"className":1926},[1927],"mord",[1849,1929,1873],{"className":1930,"style":1932},[1927,1931],"mathnormal","margin-right:0.1389em;",[1849,1934,1937],{"className":1935},[1936],"msupsub",[1849,1938,1942,1974],{"className":1939},[1940,1941],"vlist-t","vlist-t2",[1849,1943,1946,1969],{"className":1944},[1945],"vlist-r",[1849,1947,1951],{"className":1948,"style":1950},[1949],"vlist","height:0.3117em;",[1849,1952,1954,1959],{"style":1953},"top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;",[1849,1955],{"className":1956,"style":1958},[1957],"pstrut","height:2.7em;",[1849,1960,1966],{"className":1961},[1962,1963,1964,1965],"sizing","reset-size6","size3","mtight",[1849,1967,1876],{"className":1968},[1927,1931,1965],[1849,1970,1973],{"className":1971},[1972],"vlist-s","​",[1849,1975,1977],{"className":1976},[1945],[1849,1978,1981],{"className":1979,"style":1980},[1949],"height:0.15em;",[1849,1982],{},[1849,1984],{"className":1985,"style":1987},[1986],"mspace","margin-right:0.2778em;",[1849,1989,1880],{"className":1990},[1991],"mrel",[1849,1993],{"className":1994,"style":1987},[1986],[1849,1996,1998,2002,2006,2048,2052,2056,2098],{"className":1997},[1918],[1849,1999],{"className":2000,"style":2001},[1922],"height:1em;vertical-align:-0.25em;",[1849,2003,1884],{"className":2004},[2005],"mopen",[1849,2007,2009,2013],{"className":2008},[1927],[1849,2010,1889],{"className":2011,"style":2012},[1927,1931],"margin-right:0.2222em;",[1849,2014,2016],{"className":2015},[1936],[1849,2017,2019,2040],{"className":2018},[1940,1941],[1849,2020,2022,2037],{"className":2021},[1945],[1849,2023,2025],{"className":2024,"style":1950},[1949],[1849,2026,2028,2031],{"style":2027},"top:-2.55em;margin-left:-0.2222em;margin-right:0.05em;",[1849,2029],{"className":2030,"style":1958},[1957],[1849,2032,2034],{"className":2033},[1962,1963,1964,1965],[1849,2035,1876],{"className":2036},[1927,1931,1965],[1849,2038,1973],{"className":2039},[1972],[1849,2041,2043],{"className":2042},[1945],[1849,2044,2046],{"className":2045,"style":1980},[1949],[1849,2047],{},[1849,2049,1895],{"className":2050},[2051],"mpunct",[1849,2053],{"className":2054,"style":2055},[1986],"margin-right:0.1667em;",[1849,2057,2059,2063],{"className":2058},[1927],[1849,2060,1900],{"className":2061,"style":2062},[1927,1931],"margin-right:0.0785em;",[1849,2064,2066],{"className":2065},[1936],[1849,2067,2069,2090],{"className":2068},[1940,1941],[1849,2070,2072,2087],{"className":2071},[1945],[1849,2073,2075],{"className":2074,"style":1950},[1949],[1849,2076,2078,2081],{"style":2077},"top:-2.55em;margin-left:-0.0785em;margin-right:0.05em;",[1849,2079],{"className":2080,"style":1958},[1957],[1849,2082,2084],{"className":2083},[1962,1963,1964,1965],[1849,2085,1876],{"className":2086},[1927,1931,1965],[1849,2088,1973],{"className":2089},[1972],[1849,2091,2093],{"className":2092},[1945],[1849,2094,2096],{"className":2095,"style":1980},[1949],[1849,2097],{},[1849,2099,1905],{"className":2100},[2101],"mclose","。样本 ",[1849,2104,2106,2144],{"className":2105},[1852],[1849,2107,2109],{"className":2108},[1856],[1858,2110,2111],{"xmlns":1860},[1862,2112,2113,2141],{},[1865,2114,2115,2117,2125,2127,2130,2132,2139],{},[1878,2116,1884],{"stretchy":1883},[1868,2118,2119,2121],{},[1871,2120,1873],{},[2122,2123,2124],"mn",{},"1",[1878,2126,1895],{"separator":1894},[1878,2128,2129],{},"…",[1878,2131,1895],{"separator":1894},[1868,2133,2134,2136],{},[1871,2135,1873],{},[1871,2137,2138],{},"n",[1878,2140,1905],{"stretchy":1883},[1907,2142,2143],{"encoding":1909},"(W_1,\\ldots,W_n)",[1849,2145,2147],{"className":2146,"ariaHidden":1894},[1914],[1849,2148,2150,2153,2156,2197,2200,2203,2207,2210,2213,2216,2257],{"className":2149},[1918],[1849,2151],{"className":2152,"style":2001},[1922],[1849,2154,1884],{"className":2155},[2005],[1849,2157,2159,2162],{"className":2158},[1927],[1849,2160,1873],{"className":2161,"style":1932},[1927,1931],[1849,2163,2165],{"className":2164},[1936],[1849,2166,2168,2189],{"className":2167},[1940,1941],[1849,2169,2171,2186],{"className":2170},[1945],[1849,2172,2175],{"className":2173,"style":2174},[1949],"height:0.3011em;",[1849,2176,2177,2180],{"style":1953},[1849,2178],{"className":2179,"style":1958},[1957],[1849,2181,2183],{"className":2182},[1962,1963,1964,1965],[1849,2184,2124],{"className":2185},[1927,1965],[1849,2187,1973],{"className":2188},[1972],[1849,2190,2192],{"className":2191},[1945],[1849,2193,2195],{"className":2194,"style":1980},[1949],[1849,2196],{},[1849,2198,1895],{"className":2199},[2051],[1849,2201],{"className":2202,"style":2055},[1986],[1849,2204,2129],{"className":2205},[2206],"minner",[1849,2208],{"className":2209,"style":2055},[1986],[1849,2211,1895],{"className":2212},[2051],[1849,2214],{"className":2215,"style":2055},[1986],[1849,2217,2219,2222],{"className":2218},[1927],[1849,2220,1873],{"className":2221,"style":1932},[1927,1931],[1849,2223,2225],{"className":2224},[1936],[1849,2226,2228,2249],{"className":2227},[1940,1941],[1849,2229,2231,2246],{"className":2230},[1945],[1849,2232,2235],{"className":2233,"style":2234},[1949],"height:0.1514em;",[1849,2236,2237,2240],{"style":1953},[1849,2238],{"className":2239,"style":1958},[1957],[1849,2241,2243],{"className":2242},[1962,1963,1964,1965],[1849,2244,2138],{"className":2245},[1927,1931,1965],[1849,2247,1973],{"className":2248},[1972],[1849,2250,2252],{"className":2251},[1945],[1849,2253,2255],{"className":2254,"style":1980},[1949],[1849,2256],{},[1849,2258,1905],{"className":2259},[2101]," 来自某种数据生成过程。i.i.d. 是常见基准，但面板、时间序列、空间数据和聚类抽样会破坏简单独立性。",[1798,2262,2263],{},"数据生成过程不是“软件看不到的误差项”，而是描述数据如何被产生、观测和选择进入样本的理论对象。若样本只包含就业者，工资回归的误差可能同时包含劳动参与选择；若问卷非随机缺失，缺失机制会影响目标参数。",[1793,2265,2267],{"id":2266},"_3-条件期望函数","3. 条件期望函数",[1798,2269,2270],{},"条件期望函数为：",[1849,2272,2275],{"className":2273},[2274],"katex-display",[1849,2276,2278,2328],{"className":2277},[1852],[1849,2279,2281],{"className":2280},[1856],[1858,2282,2284],{"xmlns":1860,"display":2283},"block",[1862,2285,2286,2325],{},[1865,2287,2288,2291,2293,2296,2298,2300,2304,2307,2309,2312,2314,2316,2318,2321],{},[1871,2289,2290],{},"m",[1878,2292,1884],{"stretchy":1883},[1871,2294,2295],{},"x",[1878,2297,1905],{"stretchy":1883},[1878,2299,1880],{},[1871,2301,2303],{"mathvariant":2302},"double-struck","E",[1878,2305,2306],{"stretchy":1883},"[",[1871,2308,1889],{},[1878,2310,2311],{},"∣",[1871,2313,1900],{},[1878,2315,1880],{},[1871,2317,2295],{},[1878,2319,2320],{"stretchy":1883},"]",[1871,2322,2324],{"mathvariant":2323},"normal",".",[1907,2326,2327],{"encoding":1909},"m(x)=\\mathbb{E}[Y\\mid X=x].",[1849,2329,2331,2358,2383,2402],{"className":2330,"ariaHidden":1894},[1914],[1849,2332,2334,2337,2340,2343,2346,2349,2352,2355],{"className":2333},[1918],[1849,2335],{"className":2336,"style":2001},[1922],[1849,2338,2290],{"className":2339},[1927,1931],[1849,2341,1884],{"className":2342},[2005],[1849,2344,2295],{"className":2345},[1927,1931],[1849,2347,1905],{"className":2348},[2101],[1849,2350],{"className":2351,"style":1987},[1986],[1849,2353,1880],{"className":2354},[1991],[1849,2356],{"className":2357,"style":1987},[1986],[1849,2359,2361,2364,2368,2371,2374,2377,2380],{"className":2360},[1918],[1849,2362],{"className":2363,"style":2001},[1922],[1849,2365,2303],{"className":2366},[1927,2367],"mathbb",[1849,2369,2306],{"className":2370},[2005],[1849,2372,1889],{"className":2373,"style":2012},[1927,1931],[1849,2375],{"className":2376,"style":1987},[1986],[1849,2378,2311],{"className":2379},[1991],[1849,2381],{"className":2382,"style":1987},[1986],[1849,2384,2386,2390,2393,2396,2399],{"className":2385},[1918],[1849,2387],{"className":2388,"style":2389},[1922],"height:0.6833em;",[1849,2391,1900],{"className":2392,"style":2062},[1927,1931],[1849,2394],{"className":2395,"style":1987},[1986],[1849,2397,1880],{"className":2398},[1991],[1849,2400],{"className":2401,"style":1987},[1986],[1849,2403,2405,2408,2411,2414],{"className":2404},[1918],[1849,2406],{"className":2407,"style":2001},[1922],[1849,2409,2295],{"className":2410},[1927,1931],[1849,2412,2320],{"className":2413},[2101],[1849,2415,2324],{"className":2416},[1927],[1798,2418,2419,2420,2448,2449,2477,2478,2524],{},"它是给定 ",[1849,2421,2423,2436],{"className":2422},[1852],[1849,2424,2426],{"className":2425},[1856],[1858,2427,2428],{"xmlns":1860},[1862,2429,2430,2434],{},[1865,2431,2432],{},[1871,2433,1900],{},[1907,2435,1900],{"encoding":1909},[1849,2437,2439],{"className":2438,"ariaHidden":1894},[1914],[1849,2440,2442,2445],{"className":2441},[1918],[1849,2443],{"className":2444,"style":2389},[1922],[1849,2446,1900],{"className":2447,"style":2062},[1927,1931]," 时 ",[1849,2450,2452,2465],{"className":2451},[1852],[1849,2453,2455],{"className":2454},[1856],[1858,2456,2457],{"xmlns":1860},[1862,2458,2459,2463],{},[1865,2460,2461],{},[1871,2462,1889],{},[1907,2464,1889],{"encoding":1909},[1849,2466,2468],{"className":2467,"ariaHidden":1894},[1914],[1849,2469,2471,2474],{"className":2470},[1918],[1849,2472],{"className":2473,"style":2389},[1922],[1849,2475,1889],{"className":2476,"style":2012},[1927,1931]," 的最佳平方损失预测器，因为对任意函数 ",[1849,2479,2481,2502],{"className":2480},[1852],[1849,2482,2484],{"className":2483},[1856],[1858,2485,2486],{"xmlns":1860},[1862,2487,2488,2499],{},[1865,2489,2490,2493,2495,2497],{},[1871,2491,2492],{},"g",[1878,2494,1884],{"stretchy":1883},[1871,2496,1900],{},[1878,2498,1905],{"stretchy":1883},[1907,2500,2501],{"encoding":1909},"g(X)",[1849,2503,2505],{"className":2504,"ariaHidden":1894},[1914],[1849,2506,2508,2511,2515,2518,2521],{"className":2507},[1918],[1849,2509],{"className":2510,"style":2001},[1922],[1849,2512,2492],{"className":2513,"style":2514},[1927,1931],"margin-right:0.0359em;",[1849,2516,1884],{"className":2517},[2005],[1849,2519,1900],{"className":2520,"style":2062},[1927,1931],[1849,2522,1905],{"className":2523},[2101],"：",[1849,2526,2528],{"className":2527},[2274],[1849,2529,2531,2637],{"className":2530},[1852],[1849,2532,2534],{"className":2533},[1856],[1858,2535,2536],{"xmlns":1860,"display":2283},[1862,2537,2538,2634],{},[1865,2539,2540,2542,2544,2546,2548,2551,2553,2555,2557,2559,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2595,2597,2600,2602,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2630,2632],{},[1871,2541,2303],{"mathvariant":2302},[1878,2543,2306],{"stretchy":1883},[1878,2545,1884],{"stretchy":1883},[1871,2547,1889],{},[1878,2549,2550],{},"−",[1871,2552,2492],{},[1878,2554,1884],{"stretchy":1883},[1871,2556,1900],{},[1878,2558,1905],{"stretchy":1883},[2560,2561,2562,2564],"msup",{},[1878,2563,1905],{"stretchy":1883},[2122,2565,2566],{},"2",[1878,2568,2320],{"stretchy":1883},[1878,2570,1880],{},[1871,2572,2303],{"mathvariant":2302},[1878,2574,2306],{"stretchy":1883},[1878,2576,1884],{"stretchy":1883},[1871,2578,1889],{},[1878,2580,2550],{},[1871,2582,2290],{},[1878,2584,1884],{"stretchy":1883},[1871,2586,1900],{},[1878,2588,1905],{"stretchy":1883},[2560,2590,2591,2593],{},[1878,2592,1905],{"stretchy":1883},[2122,2594,2566],{},[1878,2596,2320],{"stretchy":1883},[1878,2598,2599],{},"+",[1871,2601,2303],{"mathvariant":2302},[1878,2603,2306],{"stretchy":1883},[1878,2605,1884],{"stretchy":1883},[1871,2607,2290],{},[1878,2609,1884],{"stretchy":1883},[1871,2611,1900],{},[1878,2613,1905],{"stretchy":1883},[1878,2615,2550],{},[1871,2617,2492],{},[1878,2619,1884],{"stretchy":1883},[1871,2621,1900],{},[1878,2623,1905],{"stretchy":1883},[2560,2625,2626,2628],{},[1878,2627,1905],{"stretchy":1883},[2122,2629,2566],{},[1878,2631,2320],{"stretchy":1883},[1871,2633,2324],{"mathvariant":2323},[1907,2635,2636],{"encoding":1909},"\\mathbb{E}[(Y-g(X))^2]\n=\\mathbb{E}[(Y-m(X))^2]+\\mathbb{E}[(m(X)-g(X))^2].",[1849,2638,2640,2666,2728,2752,2811,2844],{"className":2639,"ariaHidden":1894},[1914],[1849,2641,2643,2646,2649,2653,2656,2659,2663],{"className":2642},[1918],[1849,2644],{"className":2645,"style":2001},[1922],[1849,2647,2303],{"className":2648},[1927,2367],[1849,2650,2652],{"className":2651},[2005],"[(",[1849,2654,1889],{"className":2655,"style":2012},[1927,1931],[1849,2657],{"className":2658,"style":2012},[1986],[1849,2660,2550],{"className":2661},[2662],"mbin",[1849,2664],{"className":2665,"style":2012},[1986],[1849,2667,2669,2673,2676,2679,2682,2685,2716,2719,2722,2725],{"className":2668},[1918],[1849,2670],{"className":2671,"style":2672},[1922],"height:1.1141em;vertical-align:-0.25em;",[1849,2674,2492],{"className":2675,"style":2514},[1927,1931],[1849,2677,1884],{"className":2678},[2005],[1849,2680,1900],{"className":2681,"style":2062},[1927,1931],[1849,2683,1905],{"className":2684},[2101],[1849,2686,2688,2691],{"className":2687},[2101],[1849,2689,1905],{"className":2690},[2101],[1849,2692,2694],{"className":2693},[1936],[1849,2695,2697],{"className":2696},[1940],[1849,2698,2700],{"className":2699},[1945],[1849,2701,2704],{"className":2702,"style":2703},[1949],"height:0.8641em;",[1849,2705,2707,2710],{"style":2706},"top:-3.113em;margin-right:0.05em;",[1849,2708],{"className":2709,"style":1958},[1957],[1849,2711,2713],{"className":2712},[1962,1963,1964,1965],[1849,2714,2566],{"className":2715},[1927,1965],[1849,2717,2320],{"className":2718},[2101],[1849,2720],{"className":2721,"style":1987},[1986],[1849,2723,1880],{"className":2724},[1991],[1849,2726],{"className":2727,"style":1987},[1986],[1849,2729,2731,2734,2737,2740,2743,2746,2749],{"className":2730},[1918],[1849,2732],{"className":2733,"style":2001},[1922],[1849,2735,2303],{"className":2736},[1927,2367],[1849,2738,2652],{"className":2739},[2005],[1849,2741,1889],{"className":2742,"style":2012},[1927,1931],[1849,2744],{"className":2745,"style":2012},[1986],[1849,2747,2550],{"className":2748},[2662],[1849,2750],{"className":2751,"style":2012},[1986],[1849,2753,2755,2758,2761,2764,2767,2770,2799,2802,2805,2808],{"className":2754},[1918],[1849,2756],{"className":2757,"style":2672},[1922],[1849,2759,2290],{"className":2760},[1927,1931],[1849,2762,1884],{"className":2763},[2005],[1849,2765,1900],{"className":2766,"style":2062},[1927,1931],[1849,2768,1905],{"className":2769},[2101],[1849,2771,2773,2776],{"className":2772},[2101],[1849,2774,1905],{"className":2775},[2101],[1849,2777,2779],{"className":2778},[1936],[1849,2780,2782],{"className":2781},[1940],[1849,2783,2785],{"className":2784},[1945],[1849,2786,2788],{"className":2787,"style":2703},[1949],[1849,2789,2790,2793],{"style":2706},[1849,2791],{"className":2792,"style":1958},[1957],[1849,2794,2796],{"className":2795},[1962,1963,1964,1965],[1849,2797,2566],{"className":2798},[1927,1965],[1849,2800,2320],{"className":2801},[2101],[1849,2803],{"className":2804,"style":2012},[1986],[1849,2806,2599],{"className":2807},[2662],[1849,2809],{"className":2810,"style":2012},[1986],[1849,2812,2814,2817,2820,2823,2826,2829,2832,2835,2838,2841],{"className":2813},[1918],[1849,2815],{"className":2816,"style":2001},[1922],[1849,2818,2303],{"className":2819},[1927,2367],[1849,2821,2652],{"className":2822},[2005],[1849,2824,2290],{"className":2825},[1927,1931],[1849,2827,1884],{"className":2828},[2005],[1849,2830,1900],{"className":2831,"style":2062},[1927,1931],[1849,2833,1905],{"className":2834},[2101],[1849,2836],{"className":2837,"style":2012},[1986],[1849,2839,2550],{"className":2840},[2662],[1849,2842],{"className":2843,"style":2012},[1986],[1849,2845,2847,2850,2853,2856,2859,2862,2891,2894],{"className":2846},[1918],[1849,2848],{"className":2849,"style":2672},[1922],[1849,2851,2492],{"className":2852,"style":2514},[1927,1931],[1849,2854,1884],{"className":2855},[2005],[1849,2857,1900],{"className":2858,"style":2062},[1927,1931],[1849,2860,1905],{"className":2861},[2101],[1849,2863,2865,2868],{"className":2864},[2101],[1849,2866,1905],{"className":2867},[2101],[1849,2869,2871],{"className":2870},[1936],[1849,2872,2874],{"className":2873},[1940],[1849,2875,2877],{"className":2876},[1945],[1849,2878,2880],{"className":2879,"style":2703},[1949],[1849,2881,2882,2885],{"style":2706},[1849,2883],{"className":2884,"style":1958},[1957],[1849,2886,2888],{"className":2887},[1962,1963,1964,1965],[1849,2889,2566],{"className":2890},[1927,1965],[1849,2892,2320],{"className":2893},[2101],[1849,2895,2324],{"className":2896},[1927],[1798,2898,2899,2900,2974],{},"线性回归进一步限制预测函数为 ",[1849,2901,2903,2926],{"className":2902},[1852],[1849,2904,2906],{"className":2905},[1856],[1858,2907,2908],{"xmlns":1860},[1862,2909,2910,2923],{},[1865,2911,2912,2920],{},[2560,2913,2914,2916],{},[1871,2915,1900],{},[1878,2917,2919],{"mathvariant":2323,"lspace":2918,"rspace":2918},"0em","′",[1871,2921,2922],{},"β",[1907,2924,2925],{"encoding":1909},"X'\\beta",[1849,2927,2929],{"className":2928,"ariaHidden":1894},[1914],[1849,2930,2932,2936,2970],{"className":2931},[1918],[1849,2933],{"className":2934,"style":2935},[1922],"height:0.9463em;vertical-align:-0.1944em;",[1849,2937,2939,2942],{"className":2938},[1927],[1849,2940,1900],{"className":2941,"style":2062},[1927,1931],[1849,2943,2945],{"className":2944},[1936],[1849,2946,2948],{"className":2947},[1940],[1849,2949,2951],{"className":2950},[1945],[1849,2952,2955],{"className":2953,"style":2954},[1949],"height:0.7519em;",[1849,2956,2958,2961],{"style":2957},"top:-3.063em;margin-right:0.05em;",[1849,2959],{"className":2960,"style":1958},[1957],[1849,2962,2964],{"className":2963},[1962,1963,1964,1965],[1849,2965,2967],{"className":2966},[1927,1965],[1849,2968,2919],{"className":2969},[1927,1965],[1849,2971,2922],{"className":2972,"style":2973},[1927,1931],"margin-right:0.0528em;","。当真实 CEF 非线性时，OLS 仍然估计总体线性投影系数，而不一定估计某个结构参数。",[1793,2976,2978],{"id":2977},"_4-相关条件相关与因果","4. 相关、条件相关与因果",[1798,2980,2981,2982,3010,3011,3041,3042,3070],{},"条件相关描述在控制 ",[1849,2983,2985,2998],{"className":2984},[1852],[1849,2986,2988],{"className":2987},[1856],[1858,2989,2990],{"xmlns":1860},[1862,2991,2992,2996],{},[1865,2993,2994],{},[1871,2995,1900],{},[1907,2997,1900],{"encoding":1909},[1849,2999,3001],{"className":3000,"ariaHidden":1894},[1914],[1849,3002,3004,3007],{"className":3003},[1918],[1849,3005],{"className":3006,"style":2389},[1922],[1849,3008,1900],{"className":3009,"style":2062},[1927,1931]," 后 ",[1849,3012,3014,3028],{"className":3013},[1852],[1849,3015,3017],{"className":3016},[1856],[1858,3018,3019],{"xmlns":1860},[1862,3020,3021,3026],{},[1865,3022,3023],{},[1871,3024,3025],{},"D",[1907,3027,3025],{"encoding":1909},[1849,3029,3031],{"className":3030,"ariaHidden":1894},[1914],[1849,3032,3034,3037],{"className":3033},[1918],[1849,3035],{"className":3036,"style":2389},[1922],[1849,3038,3025],{"className":3039,"style":3040},[1927,1931],"margin-right:0.0278em;"," 和 ",[1849,3043,3045,3058],{"className":3044},[1852],[1849,3046,3048],{"className":3047},[1856],[1858,3049,3050],{"xmlns":1860},[1862,3051,3052,3056],{},[1865,3053,3054],{},[1871,3055,1889],{},[1907,3057,1889],{"encoding":1909},[1849,3059,3061],{"className":3060,"ariaHidden":1894},[1914],[1849,3062,3064,3067],{"className":3063},[1918],[1849,3065],{"className":3066,"style":2389},[1922],[1849,3068,1889],{"className":3069,"style":2012},[1927,1931]," 的关系。因果效应要求比较同一单位在不同处理状态下的潜在结果：",[1849,3072,3074],{"className":3073},[2274],[1849,3075,3077,3121],{"className":3076},[1852],[1849,3078,3080],{"className":3079},[1856],[1858,3081,3082],{"xmlns":1860,"display":2283},[1862,3083,3084,3118],{},[1865,3085,3086,3092,3094,3096,3098,3100,3103,3109,3111,3114,3116],{},[1868,3087,3088,3090],{},[1871,3089,1889],{},[1871,3091,1876],{},[1878,3093,1884],{"stretchy":1883},[2122,3095,2124],{},[1878,3097,1905],{"stretchy":1883},[1878,3099,1895],{"separator":1894},[1986,3101],{"width":3102},"2em",[1868,3104,3105,3107],{},[1871,3106,1889],{},[1871,3108,1876],{},[1878,3110,1884],{"stretchy":1883},[2122,3112,3113],{},"0",[1878,3115,1905],{"stretchy":1883},[1871,3117,2324],{"mathvariant":2323},[1907,3119,3120],{"encoding":1909},"Y_i(1),\\qquad Y_i(0).",[1849,3122,3124],{"className":3123,"ariaHidden":1894},[1914],[1849,3125,3127,3130,3170,3173,3176,3179,3182,3186,3189,3229,3232,3235,3238],{"className":3126},[1918],[1849,3128],{"className":3129,"style":2001},[1922],[1849,3131,3133,3136],{"className":3132},[1927],[1849,3134,1889],{"className":3135,"style":2012},[1927,1931],[1849,3137,3139],{"className":3138},[1936],[1849,3140,3142,3162],{"className":3141},[1940,1941],[1849,3143,3145,3159],{"className":3144},[1945],[1849,3146,3148],{"className":3147,"style":1950},[1949],[1849,3149,3150,3153],{"style":2027},[1849,3151],{"className":3152,"style":1958},[1957],[1849,3154,3156],{"className":3155},[1962,1963,1964,1965],[1849,3157,1876],{"className":3158},[1927,1931,1965],[1849,3160,1973],{"className":3161},[1972],[1849,3163,3165],{"className":3164},[1945],[1849,3166,3168],{"className":3167,"style":1980},[1949],[1849,3169],{},[1849,3171,1884],{"className":3172},[2005],[1849,3174,2124],{"className":3175},[1927],[1849,3177,1905],{"className":3178},[2101],[1849,3180,1895],{"className":3181},[2051],[1849,3183],{"className":3184,"style":3185},[1986],"margin-right:2em;",[1849,3187],{"className":3188,"style":2055},[1986],[1849,3190,3192,3195],{"className":3191},[1927],[1849,3193,1889],{"className":3194,"style":2012},[1927,1931],[1849,3196,3198],{"className":3197},[1936],[1849,3199,3201,3221],{"className":3200},[1940,1941],[1849,3202,3204,3218],{"className":3203},[1945],[1849,3205,3207],{"className":3206,"style":1950},[1949],[1849,3208,3209,3212],{"style":2027},[1849,3210],{"className":3211,"style":1958},[1957],[1849,3213,3215],{"className":3214},[1962,1963,1964,1965],[1849,3216,1876],{"className":3217},[1927,1931,1965],[1849,3219,1973],{"className":3220},[1972],[1849,3222,3224],{"className":3223},[1945],[1849,3225,3227],{"className":3226,"style":1980},[1949],[1849,3228],{},[1849,3230,1884],{"className":3231},[2005],[1849,3233,3113],{"className":3234},[1927],[1849,3236,1905],{"className":3237},[2101],[1849,3239,2324],{"className":3240},[1927],[1798,3242,3243,3244,3407,3408,3584],{},"个体处理效应为 ",[1849,3245,3247,3285],{"className":3246},[1852],[1849,3248,3250],{"className":3249},[1856],[1858,3251,3252],{"xmlns":1860},[1862,3253,3254,3282],{},[1865,3255,3256,3262,3264,3266,3268,3270,3276,3278,3280],{},[1868,3257,3258,3260],{},[1871,3259,1889],{},[1871,3261,1876],{},[1878,3263,1884],{"stretchy":1883},[2122,3265,2124],{},[1878,3267,1905],{"stretchy":1883},[1878,3269,2550],{},[1868,3271,3272,3274],{},[1871,3273,1889],{},[1871,3275,1876],{},[1878,3277,1884],{"stretchy":1883},[2122,3279,3113],{},[1878,3281,1905],{"stretchy":1883},[1907,3283,3284],{"encoding":1909},"Y_i(1)-Y_i(0)",[1849,3286,3288,3352],{"className":3287,"ariaHidden":1894},[1914],[1849,3289,3291,3294,3334,3337,3340,3343,3346,3349],{"className":3290},[1918],[1849,3292],{"className":3293,"style":2001},[1922],[1849,3295,3297,3300],{"className":3296},[1927],[1849,3298,1889],{"className":3299,"style":2012},[1927,1931],[1849,3301,3303],{"className":3302},[1936],[1849,3304,3306,3326],{"className":3305},[1940,1941],[1849,3307,3309,3323],{"className":3308},[1945],[1849,3310,3312],{"className":3311,"style":1950},[1949],[1849,3313,3314,3317],{"style":2027},[1849,3315],{"className":3316,"style":1958},[1957],[1849,3318,3320],{"className":3319},[1962,1963,1964,1965],[1849,3321,1876],{"className":3322},[1927,1931,1965],[1849,3324,1973],{"className":3325},[1972],[1849,3327,3329],{"className":3328},[1945],[1849,3330,3332],{"className":3331,"style":1980},[1949],[1849,3333],{},[1849,3335,1884],{"className":3336},[2005],[1849,3338,2124],{"className":3339},[1927],[1849,3341,1905],{"className":3342},[2101],[1849,3344],{"className":3345,"style":2012},[1986],[1849,3347,2550],{"className":3348},[2662],[1849,3350],{"className":3351,"style":2012},[1986],[1849,3353,3355,3358,3398,3401,3404],{"className":3354},[1918],[1849,3356],{"className":3357,"style":2001},[1922],[1849,3359,3361,3364],{"className":3360},[1927],[1849,3362,1889],{"className":3363,"style":2012},[1927,1931],[1849,3365,3367],{"className":3366},[1936],[1849,3368,3370,3390],{"className":3369},[1940,1941],[1849,3371,3373,3387],{"className":3372},[1945],[1849,3374,3376],{"className":3375,"style":1950},[1949],[1849,3377,3378,3381],{"style":2027},[1849,3379],{"className":3380,"style":1958},[1957],[1849,3382,3384],{"className":3383},[1962,1963,1964,1965],[1849,3385,1876],{"className":3386},[1927,1931,1965],[1849,3388,1973],{"className":3389},[1972],[1849,3391,3393],{"className":3392},[1945],[1849,3394,3396],{"className":3395,"style":1980},[1949],[1849,3397],{},[1849,3399,1884],{"className":3400},[2005],[1849,3402,3113],{"className":3403},[1927],[1849,3405,1905],{"className":3406},[2101],"，平均处理效应为 ",[1849,3409,3411,3455],{"className":3410},[1852],[1849,3412,3414],{"className":3413},[1856],[1858,3415,3416],{"xmlns":1860},[1862,3417,3418,3452],{},[1865,3419,3420,3422,3424,3430,3432,3434,3436,3438,3444,3446,3448,3450],{},[1871,3421,2303],{"mathvariant":2302},[1878,3423,2306],{"stretchy":1883},[1868,3425,3426,3428],{},[1871,3427,1889],{},[1871,3429,1876],{},[1878,3431,1884],{"stretchy":1883},[2122,3433,2124],{},[1878,3435,1905],{"stretchy":1883},[1878,3437,2550],{},[1868,3439,3440,3442],{},[1871,3441,1889],{},[1871,3443,1876],{},[1878,3445,1884],{"stretchy":1883},[2122,3447,3113],{},[1878,3449,1905],{"stretchy":1883},[1878,3451,2320],{"stretchy":1883},[1907,3453,3454],{"encoding":1909},"\\mathbb{E}[Y_i(1)-Y_i(0)]",[1849,3456,3458,3528],{"className":3457,"ariaHidden":1894},[1914],[1849,3459,3461,3464,3467,3470,3510,3513,3516,3519,3522,3525],{"className":3460},[1918],[1849,3462],{"className":3463,"style":2001},[1922],[1849,3465,2303],{"className":3466},[1927,2367],[1849,3468,2306],{"className":3469},[2005],[1849,3471,3473,3476],{"className":3472},[1927],[1849,3474,1889],{"className":3475,"style":2012},[1927,1931],[1849,3477,3479],{"className":3478},[1936],[1849,3480,3482,3502],{"className":3481},[1940,1941],[1849,3483,3485,3499],{"className":3484},[1945],[1849,3486,3488],{"className":3487,"style":1950},[1949],[1849,3489,3490,3493],{"style":2027},[1849,3491],{"className":3492,"style":1958},[1957],[1849,3494,3496],{"className":3495},[1962,1963,1964,1965],[1849,3497,1876],{"className":3498},[1927,1931,1965],[1849,3500,1973],{"className":3501},[1972],[1849,3503,3505],{"className":3504},[1945],[1849,3506,3508],{"className":3507,"style":1980},[1949],[1849,3509],{},[1849,3511,1884],{"className":3512},[2005],[1849,3514,2124],{"className":3515},[1927],[1849,3517,1905],{"className":3518},[2101],[1849,3520],{"className":3521,"style":2012},[1986],[1849,3523,2550],{"className":3524},[2662],[1849,3526],{"className":3527,"style":2012},[1986],[1849,3529,3531,3534,3574,3577,3580],{"className":3530},[1918],[1849,3532],{"className":3533,"style":2001},[1922],[1849,3535,3537,3540],{"className":3536},[1927],[1849,3538,1889],{"className":3539,"style":2012},[1927,1931],[1849,3541,3543],{"className":3542},[1936],[1849,3544,3546,3566],{"className":3545},[1940,1941],[1849,3547,3549,3563],{"className":3548},[1945],[1849,3550,3552],{"className":3551,"style":1950},[1949],[1849,3553,3554,3557],{"style":2027},[1849,3555],{"className":3556,"style":1958},[1957],[1849,3558,3560],{"className":3559},[1962,1963,1964,1965],[1849,3561,1876],{"className":3562},[1927,1931,1965],[1849,3564,1973],{"className":3565},[1972],[1849,3567,3569],{"className":3568},[1945],[1849,3570,3572],{"className":3571,"style":1980},[1949],[1849,3573],{},[1849,3575,1884],{"className":3576},[2005],[1849,3578,3113],{"className":3579},[1927],[1849,3581,3583],{"className":3582},[2101],")]","。问题在于同一单位不能同时观察两种状态。回归控制、随机化、工具变量和准实验设计都是构造或逼近反事实的方法，但依赖不同假设。",[1793,3586,3588],{"id":3587},"_5-目标参数表","5. 目标参数表",[3590,3591,3592,3608],"table",{},[3593,3594,3595],"thead",{},[3596,3597,3598,3602,3605],"tr",{},[3599,3600,3601],"th",{},"对象",[3599,3603,3604],{},"例子",[3599,3606,3607],{},"是否自动具有因果含义",[3609,3610,3611,3623,3721,3761,3864,4014],"tbody",{},[3596,3612,3613,3617,3620],{},[3614,3615,3616],"td",{},"描述性统计量",[3614,3618,3619],{},"样本平均工资",[3614,3621,3622],{},"否",[3596,3624,3625,3628,3719],{},[3614,3626,3627],{},"条件均值",[3614,3629,3630],{},[1849,3631,3633,3661],{"className":3632},[1852],[1849,3634,3636],{"className":3635},[1856],[1858,3637,3638],{"xmlns":1860},[1862,3639,3640,3658],{},[1865,3641,3642,3644,3646,3648,3650,3652,3654,3656],{},[1871,3643,2303],{},[1878,3645,2306],{"stretchy":1883},[1871,3647,1889],{},[1878,3649,2311],{},[1871,3651,3025],{},[1878,3653,1880],{},[2122,3655,2124],{},[1878,3657,2320],{"stretchy":1883},[1907,3659,3660],{"encoding":1909},"E[Y\\mid D=1]",[1849,3662,3664,3689,3707],{"className":3663,"ariaHidden":1894},[1914],[1849,3665,3667,3670,3674,3677,3680,3683,3686],{"className":3666},[1918],[1849,3668],{"className":3669,"style":2001},[1922],[1849,3671,2303],{"className":3672,"style":3673},[1927,1931],"margin-right:0.0576em;",[1849,3675,2306],{"className":3676},[2005],[1849,3678,1889],{"className":3679,"style":2012},[1927,1931],[1849,3681],{"className":3682,"style":1987},[1986],[1849,3684,2311],{"className":3685},[1991],[1849,3687],{"className":3688,"style":1987},[1986],[1849,3690,3692,3695,3698,3701,3704],{"className":3691},[1918],[1849,3693],{"className":3694,"style":2389},[1922],[1849,3696,3025],{"className":3697,"style":3040},[1927,1931],[1849,3699],{"className":3700,"style":1987},[1986],[1849,3702,1880],{"className":3703},[1991],[1849,3705],{"className":3706,"style":1987},[1986],[1849,3708,3710,3713,3716],{"className":3709},[1918],[1849,3711],{"className":3712,"style":2001},[1922],[1849,3714,2124],{"className":3715},[1927],[1849,3717,2320],{"className":3718},[2101],[3614,3720,3622],{},[3596,3722,3723,3726,3758],{},[3614,3724,3725],{},"线性投影系数",[3614,3727,3728,3729,3757],{},"OLS 中 ",[1849,3730,3732,3745],{"className":3731},[1852],[1849,3733,3735],{"className":3734},[1856],[1858,3736,3737],{"xmlns":1860},[1862,3738,3739,3743],{},[1865,3740,3741],{},[1871,3742,3025],{},[1907,3744,3025],{"encoding":1909},[1849,3746,3748],{"className":3747,"ariaHidden":1894},[1914],[1849,3749,3751,3754],{"className":3750},[1918],[1849,3752],{"className":3753,"style":2389},[1922],[1849,3755,3025],{"className":3756,"style":3040},[1927,1931]," 的系数",[3614,3759,3760],{},"否，除非有识别假设",[3596,3762,3763,3766,3861],{},[3614,3764,3765],{},"ATE",[3614,3767,3768],{},[1849,3769,3771,3807],{"className":3770},[1852],[1849,3772,3774],{"className":3773},[1856],[1858,3775,3776],{"xmlns":1860},[1862,3777,3778,3804],{},[1865,3779,3780,3782,3784,3786,3788,3790,3792,3794,3796,3798,3800,3802],{},[1871,3781,2303],{},[1878,3783,2306],{"stretchy":1883},[1871,3785,1889],{},[1878,3787,1884],{"stretchy":1883},[2122,3789,2124],{},[1878,3791,1905],{"stretchy":1883},[1878,3793,2550],{},[1871,3795,1889],{},[1878,3797,1884],{"stretchy":1883},[2122,3799,3113],{},[1878,3801,1905],{"stretchy":1883},[1878,3803,2320],{"stretchy":1883},[1907,3805,3806],{"encoding":1909},"E[Y(1)-Y(0)]",[1849,3808,3810,3843],{"className":3809,"ariaHidden":1894},[1914],[1849,3811,3813,3816,3819,3822,3825,3828,3831,3834,3837,3840],{"className":3812},[1918],[1849,3814],{"className":3815,"style":2001},[1922],[1849,3817,2303],{"className":3818,"style":3673},[1927,1931],[1849,3820,2306],{"className":3821},[2005],[1849,3823,1889],{"className":3824,"style":2012},[1927,1931],[1849,3826,1884],{"className":3827},[2005],[1849,3829,2124],{"className":3830},[1927],[1849,3832,1905],{"className":3833},[2101],[1849,3835],{"className":3836,"style":2012},[1986],[1849,3838,2550],{"className":3839},[2662],[1849,3841],{"className":3842,"style":2012},[1986],[1849,3844,3846,3849,3852,3855,3858],{"className":3845},[1918],[1849,3847],{"className":3848,"style":2001},[1922],[1849,3850,1889],{"className":3851,"style":2012},[1927,1931],[1849,3853,1884],{"className":3854},[2005],[1849,3856,3113],{"className":3857},[1927],[1849,3859,3583],{"className":3860},[2101],[3614,3862,3863],{},"是，定义本身是因果参数",[3596,3865,3866,3869,4011],{},[3614,3867,3868],{},"ATT",[3614,3870,3871],{},[1849,3872,3874,3918],{"className":3873},[1852],[1849,3875,3877],{"className":3876},[1856],[1858,3878,3879],{"xmlns":1860},[1862,3880,3881,3915],{},[1865,3882,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913],{},[1871,3884,2303],{},[1878,3886,2306],{"stretchy":1883},[1871,3888,1889],{},[1878,3890,1884],{"stretchy":1883},[2122,3892,2124],{},[1878,3894,1905],{"stretchy":1883},[1878,3896,2550],{},[1871,3898,1889],{},[1878,3900,1884],{"stretchy":1883},[2122,3902,3113],{},[1878,3904,1905],{"stretchy":1883},[1878,3906,2311],{},[1871,3908,3025],{},[1878,3910,1880],{},[2122,3912,2124],{},[1878,3914,2320],{"stretchy":1883},[1907,3916,3917],{"encoding":1909},"E[Y(1)-Y(0)\\mid D=1]",[1849,3919,3921,3954,3981,3999],{"className":3920,"ariaHidden":1894},[1914],[1849,3922,3924,3927,3930,3933,3936,3939,3942,3945,3948,3951],{"className":3923},[1918],[1849,3925],{"className":3926,"style":2001},[1922],[1849,3928,2303],{"className":3929,"style":3673},[1927,1931],[1849,3931,2306],{"className":3932},[2005],[1849,3934,1889],{"className":3935,"style":2012},[1927,1931],[1849,3937,1884],{"className":3938},[2005],[1849,3940,2124],{"className":3941},[1927],[1849,3943,1905],{"className":3944},[2101],[1849,3946],{"className":3947,"style":2012},[1986],[1849,3949,2550],{"className":3950},[2662],[1849,3952],{"className":3953,"style":2012},[1986],[1849,3955,3957,3960,3963,3966,3969,3972,3975,3978],{"className":3956},[1918],[1849,3958],{"className":3959,"style":2001},[1922],[1849,3961,1889],{"className":3962,"style":2012},[1927,1931],[1849,3964,1884],{"className":3965},[2005],[1849,3967,3113],{"className":3968},[1927],[1849,3970,1905],{"className":3971},[2101],[1849,3973],{"className":3974,"style":1987},[1986],[1849,3976,2311],{"className":3977},[1991],[1849,3979],{"className":3980,"style":1987},[1986],[1849,3982,3984,3987,3990,3993,3996],{"className":3983},[1918],[1849,3985],{"className":3986,"style":2389},[1922],[1849,3988,3025],{"className":3989,"style":3040},[1927,1931],[1849,3991],{"className":3992,"style":1987},[1986],[1849,3994,1880],{"className":3995},[1991],[1849,3997],{"className":3998,"style":1987},[1986],[1849,4000,4002,4005,4008],{"className":4001},[1918],[1849,4003],{"className":4004,"style":2001},[1922],[1849,4006,2124],{"className":4007},[1927],[1849,4009,2320],{"className":4010},[2101],[3614,4012,4013],{},"是，但需要设计识别",[3596,4015,4016,4019,4167],{},[3614,4017,4018],{},"预测风险",[3614,4020,4021],{},[1849,4022,4024,4062],{"className":4023},[1852],[1849,4025,4027],{"className":4026},[1856],[1858,4028,4029],{"xmlns":1860},[1862,4030,4031,4059],{},[1865,4032,4033,4035,4037,4039,4041,4043,4051,4057],{},[1871,4034,2303],{},[1878,4036,2306],{"stretchy":1883},[1878,4038,1884],{"stretchy":1883},[1871,4040,1889],{},[1878,4042,2550],{},[4044,4045,4046,4048],"mover",{"accent":1894},[1871,4047,1889],{},[1878,4049,4050],{},"^",[2560,4052,4053,4055],{},[1878,4054,1905],{"stretchy":1883},[2122,4056,2566],{},[1878,4058,2320],{"stretchy":1883},[1907,4060,4061],{"encoding":1909},"E[(Y-\\hat Y)^2]",[1849,4063,4065,4089],{"className":4064,"ariaHidden":1894},[1914],[1849,4066,4068,4071,4074,4077,4080,4083,4086],{"className":4067},[1918],[1849,4069],{"className":4070,"style":2001},[1922],[1849,4072,2303],{"className":4073,"style":3673},[1927,1931],[1849,4075,2652],{"className":4076},[2005],[1849,4078,1889],{"className":4079,"style":2012},[1927,1931],[1849,4081],{"className":4082,"style":2012},[1986],[1849,4084,2550],{"className":4085},[2662],[1849,4087],{"className":4088,"style":2012},[1986],[1849,4090,4092,4096,4134,4164],{"className":4091},[1918],[1849,4093],{"className":4094,"style":4095},[1922],"height:1.1968em;vertical-align:-0.25em;",[1849,4097,4100],{"className":4098},[1927,4099],"accent",[1849,4101,4103],{"className":4102},[1940],[1849,4104,4106],{"className":4105},[1945],[1849,4107,4110,4120],{"className":4108,"style":4109},[1949],"height:0.9468em;",[1849,4111,4113,4117],{"style":4112},"top:-3em;",[1849,4114],{"className":4115,"style":4116},[1957],"height:3em;",[1849,4118,1889],{"className":4119,"style":2012},[1927,1931],[1849,4121,4123,4126],{"style":4122},"top:-3.2523em;",[1849,4124],{"className":4125,"style":4116},[1957],[1849,4127,4131],{"className":4128,"style":4130},[4129],"accent-body","left:-0.25em;",[1849,4132,4050],{"className":4133},[1927],[1849,4135,4137,4140],{"className":4136},[2101],[1849,4138,1905],{"className":4139},[2101],[1849,4141,4143],{"className":4142},[1936],[1849,4144,4146],{"className":4145},[1940],[1849,4147,4149],{"className":4148},[1945],[1849,4150,4153],{"className":4151,"style":4152},[1949],"height:0.8141em;",[1849,4154,4155,4158],{"style":2957},[1849,4156],{"className":4157,"style":1958},[1957],[1849,4159,4161],{"className":4160},[1962,1963,1964,1965],[1849,4162,2566],{"className":4163},[1927,1965],[1849,4165,2320],{"className":4166},[2101],[3614,4168,4169],{},"否，评价预测任务",[1798,4171,4172],{},"写实证报告时，把“估计的是什么”放在结果表之前，读者才知道系数应如何解释。",[1793,4174,4176],{"id":4175},"_6-测量与选择","6. 测量与选择",[1798,4178,4179],{},"测量误差包括教育年限报告误差、收入顶格报告、价格指数口径变化和行政数据匹配误差。解释变量测量误差在经典条件下可能造成衰减偏误，但现实测量误差未必满足经典条件。",[1798,4181,4182],{},"选择问题包括：",[1824,4184,4185,4188,4191,4194],{},[1808,4186,4187],{},"只有就业者才有观察到的工资；",[1808,4189,4190],{},"只有申请者才进入政策样本；",[1808,4192,4193],{},"只有留下来接受追踪的个体有后续结果；",[1808,4195,4196],{},"变量缺失与结果或处理状态相关。",[1798,4198,4199],{},"样本量大不能自动修复选择偏误。大样本会让估计更精确地估计错误的对象。",[1793,4201,4202],{"id":4202},"自测题",[1805,4204,4205,4208,4211,4214,4217],{},[1808,4206,4207],{},"为什么条件期望是最佳平方预测器？",[1808,4209,4210],{},"OLS 估计线性投影系数时，真实 CEF 非线性会产生什么含义？",[1808,4212,4213],{},"描述性相关何时可能接近因果效应？需要什么假设？",[1808,4215,4216],{},"解释变量测量误差为什么可能使系数向零偏？",[1808,4218,4219],{},"选择进入样本与样本量大小有什么不同？",[1793,4221,4222],{"id":4222},"下一章",[1798,4224,4225],{},"下一章把线性投影写成矩阵形式，推导 OLS，并使用 FWL 定理解释“控制变量”究竟改变了什么比较。",{"title":10,"searchDepth":4227,"depth":4227,"links":4228},2,[4229,4230,4231,4232,4233,4234,4235,4236,4237],{"id":1795,"depth":4227,"text":1796},{"id":1822,"depth":4227,"text":1822},{"id":1843,"depth":4227,"text":1844},{"id":2266,"depth":4227,"text":2267},{"id":2977,"depth":4227,"text":2978},{"id":3587,"depth":4227,"text":3588},{"id":4175,"depth":4227,"text":4176},{"id":4202,"depth":4227,"text":4202},{"id":4222,"depth":4227,"text":4222},"从样本、总体、条件期望和线性投影开始建立计量经济学语言。","md",{},true,{"title":1330,"description":4238},"CxLg3hVtxymcVYogwDELBVKQQrXtiwZF5MLAM701qB4",[4245,4247],{"title":1324,"path":1325,"stem":1326,"description":4246,"children":-1},"面向本科生与研究生的计量经济学基础课程，从数据、回归和推断进入内生性、面板、时间序列与可重复实证。",{"title":1334,"path":1335,"stem":1336,"description":4248,"children":-1},"推导普通最小二乘、解释 FWL 定理并分析函数形式与遗漏变量偏误。",1785754747362]