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Predictive Analytics","\u002Fen\u002Fbusiness-analytics\u002F02-predictive","en\u002Fbusiness-analytics\u002F02-predictive\u002Findex",[343,344,348,352,356,360,364],{"title":339,"path":340,"stem":341,"sidebar":12},{"title":345,"path":346,"stem":347,"sidebar":12},"Validation, Baselines and Leakage","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage","en\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage",{"title":349,"path":350,"stem":351,"sidebar":12},"Regression and Forecasting","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting","en\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting",{"title":353,"path":354,"stem":355,"sidebar":12},"Classification and Calibration","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration","en\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration",{"title":357,"path":358,"stem":359,"sidebar":12},"Trees and Ensembles","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles","en\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles",{"title":361,"path":362,"stem":363,"sidebar":12},"Decision Metrics and Thresholds","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds","en\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds",{"title":365,"path":366,"stem":367,"sidebar":12},"Explainability, Monitoring and Drift","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift","en\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift",{"title":369,"path":370,"stem":371,"children":372,"sidebar":12},"3. Prescriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive","en\u002Fbusiness-analytics\u002F03-prescriptive\u002Findex",[373,374,378,382,386,390,394],{"title":369,"path":370,"stem":371,"sidebar":12},{"title":375,"path":376,"stem":377,"sidebar":12},"Decision-Making under Uncertainty","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty",{"title":379,"path":380,"stem":381,"sidebar":12},"Linear Optimisation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization",{"title":383,"path":384,"stem":385,"sidebar":12},"Inventory and Allocation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation",{"title":387,"path":388,"stem":389,"sidebar":12},"Queueing and Simulation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation",{"title":391,"path":392,"stem":393,"sidebar":12},"Pricing and Revenue Management","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation",{"title":395,"path":396,"stem":397,"sidebar":12},"Causal Targeting and Policy Learning","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting",{"title":399,"path":400,"stem":401,"children":402,"sidebar":12},"4. Deployment and Governance","\u002Fen\u002Fbusiness-analytics\u002F04-deployment","en\u002Fbusiness-analytics\u002F04-deployment\u002Findex",[403,404,408,412],{"title":399,"path":400,"stem":401,"sidebar":12},{"title":405,"path":406,"stem":407,"sidebar":12},"Data Products and Monitoring","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring","en\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring",{"title":409,"path":410,"stem":411,"sidebar":12},"Governance, Fairness and Privacy","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy","en\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy",{"title":413,"path":414,"stem":415,"sidebar":12},"Adoption and Business Value","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value","en\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value",{"title":417,"path":418,"stem":419,"children":420,"sidebar":12},"Appendix and Revision Tools","\u002Fen\u002Fbusiness-analytics\u002Fappendix","en\u002Fbusiness-analytics\u002Fappendix\u002Findex",[421,422,426,430],{"title":417,"path":418,"stem":419,"sidebar":12},{"title":423,"path":424,"stem":425,"sidebar":12},"Formula and Decision Map","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map","en\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map",{"title":427,"path":428,"stem":429,"sidebar":12},"Worked Examples and Pitfalls","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls","en\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls",{"title":88,"path":431,"stem":432,"sidebar":12},"\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map","en\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map",{"title":434,"path":435,"stem":436,"children":437,"sidebar":12},"Capstone — The Evening Delivery Promise","\u002Fen\u002Fbusiness-analytics\u002Fcapstone","en\u002Fbusiness-analytics\u002Fcapstone\u002Findex",[438],{"title":434,"path":435,"stem":436,"sidebar":12},{"title":440,"path":441,"stem":442,"children":443,"page":280},"Financial Economic Time Series","\u002Fen\u002Ffinancial-economic-time-series","en\u002Ffinancial-economic-time-series",[444,452,460,468,476,484,493,502,511,520,529],{"title":445,"path":446,"stem":447,"children":448,"sidebar":451},"Financial and Economic Time Series — Course Guide","\u002Fen\u002Ffinancial-economic-time-series\u002F00-intro","en\u002Ffinancial-economic-time-series\u002F00-intro\u002Findex",[449],{"title":445,"path":446,"stem":447,"sidebar":450},{"order":71},{"order":71},{"title":453,"path":454,"stem":455,"children":456,"sidebar":459},"Three Versions of Time Series","\u002Fen\u002Ffinancial-economic-time-series\u002F01-bridge","en\u002Ffinancial-economic-time-series\u002F01-bridge\u002Findex",[457],{"title":453,"path":454,"stem":455,"sidebar":458},{"order":204},{"order":204},{"title":461,"path":462,"stem":463,"children":464,"sidebar":467},"Data, Clocks, and Transformations","\u002Fen\u002Ffinancial-economic-time-series\u002F02-data-transformations","en\u002Ffinancial-economic-time-series\u002F02-data-transformations\u002Findex",[465],{"title":461,"path":462,"stem":463,"sidebar":466},{"order":210},{"order":210},{"title":469,"path":470,"stem":471,"children":472,"sidebar":475},"Predictive Regressions and Persistent Predictors","\u002Fen\u002Ffinancial-economic-time-series\u002F03-predictive-regressions","en\u002Ffinancial-economic-time-series\u002F03-predictive-regressions\u002Findex",[473],{"title":469,"path":470,"stem":471,"sidebar":474},{"order":216},{"order":216},{"title":477,"path":478,"stem":479,"children":480,"sidebar":483},"Volatility, Tails, and Financial Risk","\u002Fen\u002Ffinancial-economic-time-series\u002F04-volatility-risk","en\u002Ffinancial-economic-time-series\u002F04-volatility-risk\u002Findex",[481],{"title":477,"path":478,"stem":479,"sidebar":482},{"order":222},{"order":222},{"title":485,"path":486,"stem":487,"children":488,"sidebar":492},"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",[489],{"title":485,"path":486,"stem":487,"sidebar":490},{"order":491},5,{"order":491},{"title":494,"path":495,"stem":496,"children":497,"sidebar":501},"VAR, Structural Identification, and Local Projections","\u002Fen\u002Ffinancial-economic-time-series\u002F06-var-identification","en\u002Ffinancial-economic-time-series\u002F06-var-identification\u002Findex",[498],{"title":494,"path":495,"stem":496,"sidebar":499},{"order":500},6,{"order":500},{"title":503,"path":504,"stem":505,"children":506,"sidebar":510},"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",[507],{"title":503,"path":504,"stem":505,"sidebar":508},{"order":509},7,{"order":509},{"title":512,"path":513,"stem":514,"children":515,"sidebar":519},"Forecast Evaluation for Decisions","\u002Fen\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation","en\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation\u002Findex",[516],{"title":512,"path":513,"stem":514,"sidebar":517},{"order":518},8,{"order":518},{"title":521,"path":522,"stem":523,"children":524,"sidebar":528},"Integrated R Laboratory","\u002Fen\u002Ffinancial-economic-time-series\u002F09-r-laboratory","en\u002Ffinancial-economic-time-series\u002F09-r-laboratory\u002Findex",[525],{"title":521,"path":522,"stem":523,"sidebar":526},{"order":527},9,{"order":527},{"title":530,"path":531,"stem":532,"children":533,"sidebar":537},"Capstones, Data, and Reading Ladder","\u002Fen\u002Ffinancial-economic-time-series\u002F10-capstone-readings","en\u002Ffinancial-economic-time-series\u002F10-capstone-readings\u002Findex",[534],{"title":530,"path":531,"stem":532,"sidebar":535},{"order":536},10,{"order":536},{"title":539,"path":540,"stem":541,"children":542,"page":280},"Intro To Economics","\u002Fen\u002Fintro-to-economics","en\u002Fintro-to-economics",[543,547,551,555,559,563,567,571,575,579,583,587,591],{"title":544,"path":545,"stem":546,"sidebar":12},"Introduction to Economics — Decisions, Markets, and the Macroeconomy","\u002Fen\u002Fintro-to-economics\u002F00-intro","en\u002Fintro-to-economics\u002F00-intro",{"title":548,"path":549,"stem":550,"sidebar":12},"Chapter 1 — Choice, Opportunity Cost, and Trade","\u002Fen\u002Fintro-to-economics\u002F01-foundations","en\u002Fintro-to-economics\u002F01-foundations",{"title":552,"path":553,"stem":554,"sidebar":12},"Chapter 2 — Demand, Supply, Equilibrium, and Welfare","\u002Fen\u002Fintro-to-economics\u002F02-demand-and-supply","en\u002Fintro-to-economics\u002F02-demand-and-supply",{"title":556,"path":557,"stem":558,"sidebar":12},"Chapter 3 — Elasticity, Revenue, and Tax Incidence","\u002Fen\u002Fintro-to-economics\u002F03-elasticity","en\u002Fintro-to-economics\u002F03-elasticity",{"title":560,"path":561,"stem":562,"sidebar":12},"Chapter 4 — Firms, Market Power, and Market Failure","\u002Fen\u002Fintro-to-economics\u002F04-market-structures","en\u002Fintro-to-economics\u002F04-market-structures",{"title":564,"path":565,"stem":566,"sidebar":12},"Chapter 5 — GDP, Income, Wealth, and Welfare","\u002Fen\u002Fintro-to-economics\u002F05-gdp-and-wealth","en\u002Fintro-to-economics\u002F05-gdp-and-wealth",{"title":568,"path":569,"stem":570,"sidebar":12},"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":572,"path":573,"stem":574,"sidebar":12},"Chapter 7 — Productivity, Technology, and Economic Growth","\u002Fen\u002Fintro-to-economics\u002F07-economic-growth","en\u002Fintro-to-economics\u002F07-economic-growth",{"title":576,"path":577,"stem":578,"sidebar":12},"Chapter 8 — Money, Credit, and Banking","\u002Fen\u002Fintro-to-economics\u002F08-money-and-banking","en\u002Fintro-to-economics\u002F08-money-and-banking",{"title":580,"path":581,"stem":582,"sidebar":12},"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":584,"path":585,"stem":586,"sidebar":12},"Chapter 10 — Fiscal Policy, Distribution, and Public Debt","\u002Fen\u002Fintro-to-economics\u002F10-fiscal-policy","en\u002Fintro-to-economics\u002F10-fiscal-policy",{"title":588,"path":589,"stem":590,"sidebar":12},"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":592,"path":593,"stem":594,"sidebar":12},"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":596,"path":597,"stem":598,"children":599,"page":280},"Microeconometrics","\u002Fen\u002Fmicroeconometrics","en\u002Fmicroeconometrics",[600,618,626,644,666,692,716,740,760,780],{"title":601,"path":602,"stem":603,"children":604,"sidebar":12},"0. Causal Questions and Designs","\u002Fen\u002Fmicroeconometrics\u002F00-foundations","en\u002Fmicroeconometrics\u002F00-foundations\u002Findex",[605,606,610,614],{"title":601,"path":602,"stem":603,"sidebar":12},{"title":607,"path":608,"stem":609,"sidebar":12},"Causal Questions and Estimands","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands","en\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands",{"title":611,"path":612,"stem":613,"sidebar":12},"Potential Outcomes and Experiments","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments","en\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments",{"title":615,"path":616,"stem":617,"sidebar":12},"Causal Diagrams and Controls","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls","en\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls",{"title":619,"path":620,"stem":621,"children":622,"sidebar":625},"Microeconometrics — Designing Credible Counterfactuals","\u002Fen\u002Fmicroeconometrics\u002F00-index","en\u002Fmicroeconometrics\u002F00-index",[623],{"title":619,"path":620,"stem":621,"sidebar":624},{"order":71},{"order":71},{"title":627,"path":628,"stem":629,"children":630,"sidebar":12},"1. Regression and Inference","\u002Fen\u002Fmicroeconometrics\u002F01-regression","en\u002Fmicroeconometrics\u002F01-regression\u002Findex",[631,632,636,640],{"title":627,"path":628,"stem":629,"sidebar":12},{"title":633,"path":634,"stem":635,"sidebar":12},"OLS as a Projection","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection","en\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection",{"title":637,"path":638,"stem":639,"sidebar":12},"FWL, Selection and Controls","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls","en\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls",{"title":641,"path":642,"stem":643,"sidebar":12},"Inference and Clustering","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering","en\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering",{"title":645,"path":646,"stem":647,"children":648,"sidebar":12},"2. Instruments and Panel Data","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002Findex",[649,650,654,658,662],{"title":645,"path":646,"stem":647,"sidebar":12},{"title":651,"path":652,"stem":653,"sidebar":12},"Instrumental Variables","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification",{"title":655,"path":656,"stem":657,"sidebar":12},"Weak Instruments and LATE","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late",{"title":659,"path":660,"stem":661,"sidebar":12},"Panel Fixed Effects","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects",{"title":663,"path":664,"stem":665,"sidebar":12},"Dynamic Panels and Limits","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel",{"title":667,"path":668,"stem":669,"children":670,"sidebar":12},"3. Policy Evaluation Designs","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs","en\u002Fmicroeconometrics\u002F03-policy-designs\u002Findex",[671,672,676,680,684,688],{"title":667,"path":668,"stem":669,"sidebar":12},{"title":673,"path":674,"stem":675,"sidebar":12},"Difference-in-Differences","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core",{"title":677,"path":678,"stem":679,"sidebar":12},"Staggered DID and Event Studies","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies",{"title":681,"path":682,"stem":683,"sidebar":12},"Regression Discontinuity","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd",{"title":685,"path":686,"stem":687,"sidebar":12},"Matching, Weighting and Overlap","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting",{"title":689,"path":690,"stem":691,"sidebar":12},"Synthetic Control","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control",{"title":693,"path":694,"stem":695,"children":696,"sidebar":715},"Module 4 — Choice and Limited Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002Findex",[697,699,703,707,711],{"title":693,"path":694,"stem":695,"sidebar":698},{"order":222},{"title":700,"path":701,"stem":702,"sidebar":12},"Binary Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice",{"title":704,"path":705,"stem":706,"sidebar":12},"Multinomial and Ordered Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice",{"title":708,"path":709,"stem":710,"sidebar":12},"Count Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes",{"title":712,"path":713,"stem":714,"sidebar":12},"Censoring, Truncation and Selection","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection",{"order":222},{"title":717,"path":718,"stem":719,"children":720,"sidebar":739},"Module 5 — Modern Causal Analysis","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal","en\u002Fmicroeconometrics\u002F05-modern-causal\u002Findex",[721,723,727,731,735],{"title":717,"path":718,"stem":719,"sidebar":722},{"order":491},{"title":724,"path":725,"stem":726,"sidebar":12},"Double Machine Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml",{"title":728,"path":729,"stem":730,"sidebar":12},"Heterogeneity and Policy Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy",{"title":732,"path":733,"stem":734,"sidebar":12},"Sensitivity and Partial Identification","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id",{"title":736,"path":737,"stem":738,"sidebar":12},"External Validity and Transport","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity",{"order":491},{"title":741,"path":742,"stem":743,"children":744,"sidebar":759},"Module 6 — From Data to Auditable Evidence","\u002Fen\u002Fmicroeconometrics\u002F06-workflow","en\u002Fmicroeconometrics\u002F06-workflow\u002Findex",[745,747,751,755],{"title":741,"path":742,"stem":743,"sidebar":746},{"order":500},{"title":748,"path":749,"stem":750,"sidebar":12},"Data Provenance","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance","en\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance",{"title":752,"path":753,"stem":754,"sidebar":12},"Reproducibility and Reporting","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting","en\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting",{"title":756,"path":757,"stem":758,"sidebar":12},"Paper and Evidence Audit","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit","en\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit",{"order":500},{"title":761,"path":762,"stem":763,"children":764,"sidebar":779},"Appendix — Revision and Evidence Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix","en\u002Fmicroeconometrics\u002Fappendix\u002Findex",[765,767,771,775],{"title":761,"path":762,"stem":763,"sidebar":766},{"order":518},{"title":768,"path":769,"stem":770,"sidebar":12},"Formula and Design Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map","en\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map",{"title":772,"path":773,"stem":774,"sidebar":12},"Ten Worked Microeconometric Pitfalls","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls","en\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls",{"title":776,"path":777,"stem":778,"sidebar":12},"Reading and Software 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Duality, and Revealed Preference","\u002Fen\u002Fmicroeconomics\u002F01-fundations","en\u002Fmicroeconomics\u002F01-fundations\u002Findex",[804],{"title":800,"path":801,"stem":802,"sidebar":12},{"title":806,"path":807,"stem":808,"children":809,"sidebar":12},"Module 2 — Comparative Statics and Welfare Measurement","\u002Fen\u002Fmicroeconomics\u002F02-comparative-statics","en\u002Fmicroeconomics\u002F02-comparative-statics\u002Findex",[810],{"title":806,"path":807,"stem":808,"sidebar":12},{"title":812,"path":813,"stem":814,"children":815,"sidebar":12},"Module 3 — Choice under Risk and Insurance","\u002Fen\u002Fmicroeconomics\u002F03-uncertainty","en\u002Fmicroeconomics\u002F03-uncertainty\u002Findex",[816],{"title":812,"path":813,"stem":814,"sidebar":12},{"title":818,"path":819,"stem":820,"children":821,"sidebar":12},"Module 4 — General Equilibrium and Welfare","\u002Fen\u002Fmicroeconomics\u002F04-general-equilibrium","en\u002Fmicroeconomics\u002F04-general-equilibrium\u002Findex",[822],{"title":818,"path":819,"stem":820,"sidebar":12},{"title":824,"path":825,"stem":826,"children":827,"sidebar":12},"Module 5 — Static, Dynamic, and Repeated Games","\u002Fen\u002Fmicroeconomics\u002F05-game-theory","en\u002Fmicroeconomics\u002F05-game-theory\u002Findex",[828],{"title":824,"path":825,"stem":826,"sidebar":12},{"title":830,"path":831,"stem":832,"children":833,"sidebar":12},"Module 6 — Oligopoly, Entry, and Algorithmic Pricing","\u002Fen\u002Fmicroeconomics\u002F06-oligopoly","en\u002Fmicroeconomics\u002F06-oligopoly\u002Findex",[834],{"title":830,"path":831,"stem":832,"sidebar":12},{"title":836,"path":837,"stem":838,"children":839,"sidebar":12},"Module 7 — Information Economics and Contracts","\u002Fen\u002Fmicroeconomics\u002F07-information-economics","en\u002Fmicroeconomics\u002F07-information-economics\u002Findex",[840],{"title":836,"path":837,"stem":838,"sidebar":12},{"title":842,"path":843,"stem":844,"children":845,"sidebar":12},"Module 8 — Mechanism Design and Auctions","\u002Fen\u002Fmicroeconomics\u002F08-mechanism-design","en\u002Fmicroeconomics\u002F08-mechanism-design\u002Findex",[846],{"title":842,"path":843,"stem":844,"sidebar":12},{"title":848,"path":849,"stem":850,"children":851,"sidebar":12},"Module 9 — Behavioural and Experimental Microeconomics","\u002Fen\u002Fmicroeconomics\u002F09-behavioural-economics","en\u002Fmicroeconomics\u002F09-behavioural-economics\u002Findex",[852],{"title":848,"path":849,"stem":850,"sidebar":12},{"title":854,"path":855,"stem":856,"children":857,"sidebar":12},"Module 10 — Externalities, Public Goods, and Collective Action","\u002Fen\u002Fmicroeconomics\u002F10-externalities-public-goods","en\u002Fmicroeconomics\u002F10-externalities-public-goods\u002Findex",[858],{"title":854,"path":855,"stem":856,"sidebar":12},{"title":860,"path":861,"stem":862,"children":863,"sidebar":12},"Module 11 — Matching and Market Design","\u002Fen\u002Fmicroeconomics\u002F11-market-design","en\u002Fmicroeconomics\u002F11-market-design\u002Findex",[864],{"title":860,"path":861,"stem":862,"sidebar":12},{"title":866,"path":867,"stem":868,"children":869,"sidebar":12},"Module 12 — Integrated Microeconomic Design Studio","\u002Fen\u002Fmicroeconomics\u002F12-review","en\u002Fmicroeconomics\u002F12-review\u002Findex",[870],{"title":866,"path":867,"stem":868,"sidebar":12},{"title":872,"path":873,"stem":874,"children":875,"sidebar":12},"Playground","\u002Fen\u002Fplayground","en\u002Fplayground\u002Findex",[876,877,881,885,889,893],{"title":872,"path":873,"stem":874,"sidebar":12},{"title":878,"path":879,"stem":880,"sidebar":12},"Typst Plugin Playground","\u002Fen\u002Fplayground\u002F01-typstex","en\u002Fplayground\u002F01-typstex",{"title":882,"path":883,"stem":884,"sidebar":12},"Citation Plugin Test","\u002Fen\u002Fplayground\u002F02-citation","en\u002Fplayground\u002F02-citation",{"title":886,"path":887,"stem":888,"sidebar":12},"Pyodide Playground","\u002Fen\u002Fplayground\u002F03-pyodide","en\u002Fplayground\u002F03-pyodide",{"title":890,"path":891,"stem":892,"sidebar":12},"WebR Playground","\u002Fen\u002Fplayground\u002F04-webr","en\u002Fplayground\u002F04-webr",{"title":894,"path":895,"stem":896,"sidebar":12},"Chart.js Visualizations","\u002Fen\u002Fplayground\u002F05-chartjs","en\u002Fplayground\u002F05-chartjs",{"title":898,"path":899,"stem":900,"children":901,"page":280},"Statistics For Insurance","\u002Fen\u002Fstatistics-for-insurance","en\u002Fstatistics-for-insurance",[902,910,938,971,999,1027,1040],{"title":903,"path":904,"stem":905,"children":906,"sidebar":909},"Statistics for General Insurance","\u002Fen\u002Fstatistics-for-insurance\u002F01-intro","en\u002Fstatistics-for-insurance\u002F01-intro\u002Findex",[907],{"title":903,"path":904,"stem":905,"sidebar":908},{"order":71},{"order":71},{"title":911,"path":912,"stem":913,"children":914,"sidebar":937},"Claims Development and Reserving","\u002Fen\u002Fstatistics-for-insurance\u002F02-chain-ladder","en\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002Findex",[915,917,922,927,932],{"title":911,"path":912,"stem":913,"sidebar":916},{"order":71},{"title":918,"path":919,"stem":920,"sidebar":921},"Basic Chain Ladder","\u002Fen\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F01-basic-chain","en\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F01-basic-chain",{"order":204},{"title":923,"path":924,"stem":925,"sidebar":926},"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",{"order":210},{"title":928,"path":929,"stem":930,"sidebar":931},"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",{"order":216},{"title":933,"path":934,"stem":935,"sidebar":936},"Uncertainty and Diagnostics","\u002Fen\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F04-uncertainty-diagnostics","en\u002Fstatistics-for-insurance\u002F02-chain-ladder\u002F04-uncertainty-diagnostics",{"order":222},{"order":71},{"title":939,"path":940,"stem":941,"children":942,"sidebar":970},"Frequency and Severity Models","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002Findex",[943,945,950,955,960,965],{"title":939,"path":940,"stem":941,"sidebar":944},{"order":71},{"title":946,"path":947,"stem":948,"sidebar":949},"Core Severity Models","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist\u002F01-loss-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002F01-loss-dist",{"order":204},{"title":951,"path":952,"stem":953,"sidebar":954},"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",{"order":210},{"title":956,"path":957,"stem":958,"sidebar":959},"Claim Count Models","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist\u002F03-case-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002F03-case-dist",{"order":216},{"title":961,"path":962,"stem":963,"sidebar":964},"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",{"order":222},{"title":966,"path":967,"stem":968,"sidebar":969},"Mixtures and Heterogeneity","\u002Fen\u002Fstatistics-for-insurance\u002F03-common-dist\u002F05-mix-dist","en\u002Fstatistics-for-insurance\u002F03-common-dist\u002F05-mix-dist",{"order":491},{"order":71},{"title":972,"path":973,"stem":974,"children":975,"sidebar":998},"Reinsurance as a Loss Transformation","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002Findex",[976,978,983,988,993],{"title":972,"path":973,"stem":974,"sidebar":977},{"order":71},{"title":979,"path":980,"stem":981,"sidebar":982},"Proportional Reinsurance","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F01-proportional","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F01-proportional",{"order":204},{"title":984,"path":985,"stem":986,"sidebar":987},"Excess-of-Loss Reinsurance","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F02-excess","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F02-excess",{"order":210},{"title":989,"path":990,"stem":991,"sidebar":992},"Inflation and Layer Erosion","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F03-inflation","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F03-inflation",{"order":216},{"title":994,"path":995,"stem":996,"sidebar":997},"Reinsurance Decision Lab","\u002Fen\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F04-examples","en\u002Fstatistics-for-insurance\u002F04-reinsurance\u002F04-examples",{"order":222},{"order":71},{"title":1000,"path":1001,"stem":1002,"children":1003,"sidebar":1026},"Aggregate Risk and Capital","\u002Fen\u002Fstatistics-for-insurance\u002F05-risk-theory","en\u002Fstatistics-for-insurance\u002F05-risk-theory\u002Findex",[1004,1006,1011,1016,1021],{"title":1000,"path":1001,"stem":1002,"sidebar":1005},{"order":71},{"title":1007,"path":1008,"stem":1009,"sidebar":1010},"Collective Risk Model","\u002Fen\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F01-collective","en\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F01-collective",{"order":204},{"title":1012,"path":1013,"stem":1014,"sidebar":1015},"Individual Risk Model","\u002Fen\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F02-individual","en\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F02-individual",{"order":210},{"title":1017,"path":1018,"stem":1019,"sidebar":1020},"Aggregate Risk Computation Lab","\u002Fen\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F03-examples","en\u002Fstatistics-for-insurance\u002F05-risk-theory\u002F03-examples",{"order":216},{"title":1022,"path":1023,"stem":1024,"sidebar":1025},"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",{"order":222},{"order":71},{"title":1028,"path":1029,"stem":1030,"children":1031,"sidebar":1039},"Surplus and Ruin Theory","\u002Fen\u002Fstatistics-for-insurance\u002F06-ruin-theory","en\u002Fstatistics-for-insurance\u002F06-ruin-theory\u002Findex",[1032,1034],{"title":1028,"path":1029,"stem":1030,"sidebar":1033},{"order":71},{"title":1035,"path":1036,"stem":1037,"sidebar":1038},"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",{"order":204},{"order":71},{"title":1041,"path":1042,"stem":1043,"children":1044,"sidebar":1047},"Portfolio Risk Capstone","\u002Fen\u002Fstatistics-for-insurance\u002F07-capstone","en\u002Fstatistics-for-insurance\u002F07-capstone\u002Findex",[1045],{"title":1041,"path":1042,"stem":1043,"sidebar":1046},{"order":71},{"order":71},{"title":1049,"path":1050,"stem":1051,"children":1052,"page":280},"Time Series","\u002Fen\u002Ftime-series","en\u002Ftime-series",[1053,1066,1074,1082,1090,1098,1106,1114,1142],{"title":1054,"path":1055,"stem":1056,"children":1057,"sidebar":1065},"Classical Time Series — Course Guide","\u002Fen\u002Ftime-series\u002F00-intro","en\u002Ftime-series\u002F00-intro\u002Findex",[1058,1060],{"title":1054,"path":1055,"stem":1056,"sidebar":1059},{"order":71},{"title":1061,"path":1062,"stem":1063,"sidebar":1064},"Preparation — Stationarity in 30 Minutes","\u002Fen\u002Ftime-series\u002F00-intro\u002F01-stationary","en\u002Ftime-series\u002F00-intro\u002F01-stationary",{"order":204},{"order":71},{"title":1067,"path":1068,"stem":1069,"children":1070,"sidebar":1073},"Module 1 — Processes, Dependence, and Stationarity","\u002Fen\u002Ftime-series\u002F01-stochastic-process","en\u002Ftime-series\u002F01-stochastic-process\u002Findex",[1071],{"title":1067,"path":1068,"stem":1069,"sidebar":1072},{"order":204},{"order":204},{"title":1075,"path":1076,"stem":1077,"children":1078,"sidebar":1081},"Module 2 — ARMA, ARIMA, and Model Identification","\u002Fen\u002Ftime-series\u002F02-arma","en\u002Ftime-series\u002F02-arma\u002Findex",[1079],{"title":1075,"path":1076,"stem":1077,"sidebar":1080},{"order":210},{"order":210},{"title":1083,"path":1084,"stem":1085,"children":1086,"sidebar":1089},"Module 3 — Linear Prediction and State-Space Recursions","\u002Fen\u002Ftime-series\u002F03-prediction","en\u002Ftime-series\u002F03-prediction\u002Findex",[1087],{"title":1083,"path":1084,"stem":1085,"sidebar":1088},{"order":216},{"order":216},{"title":1091,"path":1092,"stem":1093,"children":1094,"sidebar":1097},"Module 4 — Estimation, Likelihood, and Inference","\u002Fen\u002Ftime-series\u002F04-estimation","en\u002Ftime-series\u002F04-estimation\u002Findex",[1095],{"title":1091,"path":1092,"stem":1093,"sidebar":1096},{"order":222},{"order":222},{"title":1099,"path":1100,"stem":1101,"children":1102,"sidebar":1105},"Module 5 — Systems, Seasonality, and Cointegration","\u002Fen\u002Ftime-series\u002F05-multi-ar","en\u002Ftime-series\u002F05-multi-ar\u002Findex",[1103],{"title":1099,"path":1100,"stem":1101,"sidebar":1104},{"order":491},{"order":491},{"title":1107,"path":1108,"stem":1109,"children":1110,"sidebar":1113},"Module 6 — Spectral Analysis, Cycles, and Filters","\u002Fen\u002Ftime-series\u002F06-spectral-analysis","en\u002Ftime-series\u002F06-spectral-analysis\u002Findex",[1111],{"title":1107,"path":1108,"stem":1109,"sidebar":1112},{"order":500},{"order":500},{"title":1115,"path":1116,"stem":1117,"children":1118,"sidebar":1141},"R Matrix Laboratory","\u002Fen\u002Ftime-series\u002F07-r-implementation","en\u002Ftime-series\u002F07-r-implementation\u002Findex",[1119,1121,1126,1131,1136],{"title":1115,"path":1116,"stem":1117,"sidebar":1120},{"order":509},{"title":1122,"path":1123,"stem":1124,"sidebar":1125},"R Matrix Lab 1 — Covariance Geometry","\u002Fen\u002Ftime-series\u002F07-r-implementation\u002F01-covariance-matrices","en\u002Ftime-series\u002F07-r-implementation\u002F01-covariance-matrices",{"order":204},{"title":1127,"path":1128,"stem":1129,"sidebar":1130},"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",{"order":210},{"title":1132,"path":1133,"stem":1134,"sidebar":1135},"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",{"order":216},{"title":1137,"path":1138,"stem":1139,"sidebar":1140},"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",{"order":222},{"order":509},{"title":1143,"path":1144,"stem":1145,"children":1146,"sidebar":1169},"Optional Python Appendix — Empirical Forecasting","\u002Fen\u002Ftime-series\u002F08-python-implementation","en\u002Ftime-series\u002F08-python-implementation\u002Findex",[1147,1149,1154,1159,1164],{"title":1143,"path":1144,"stem":1145,"sidebar":1148},{"order":518},{"title":1150,"path":1151,"stem":1152,"sidebar":1153},"Optional Python Lab 1 — Explore Before Modeling","\u002Fen\u002Ftime-series\u002F08-python-implementation\u002F01-exploration","en\u002Ftime-series\u002F08-python-implementation\u002F01-exploration",{"order":204},{"title":1155,"path":1156,"stem":1157,"sidebar":1158},"Optional Python Lab 2 — Diagnose Stationarity","\u002Fen\u002Ftime-series\u002F08-python-implementation\u002F02-stationarity","en\u002Ftime-series\u002F08-python-implementation\u002F02-stationarity",{"order":210},{"title":1160,"path":1161,"stem":1162,"sidebar":1163},"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",{"order":216},{"title":1165,"path":1166,"stem":1167,"sidebar":1168},"Optional Python Lab 4 — Forecast, Backtest, and 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Statistics","\u002Fzh\u002Fprob-and-stats\u002F02-statistics","zh\u002Fprob-and-stats\u002F02-statistics",[1979,1987,1995,2003,2011,2019,2027],{"title":1980,"path":1981,"stem":1982,"children":1983,"sidebar":1986},"第六章：抽样分布","\u002Fzh\u002Fprob-and-stats\u002F02-statistics\u002F01-sampling","zh\u002Fprob-and-stats\u002F02-statistics\u002F01-sampling\u002Findex",[1984],{"title":1980,"path":1981,"stem":1982,"sidebar":1985},{"order":509},{"order":509},{"title":1988,"path":1989,"stem":1990,"children":1991,"sidebar":1994},"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",[1992],{"title":1988,"path":1989,"stem":1990,"sidebar":1993},{"order":518},{"order":518},{"title":1996,"path":1997,"stem":1998,"children":1999,"sidebar":2002},"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",[2000],{"title":1996,"path":1997,"stem":1998,"sidebar":2001},{"order":527},{"order":527},{"title":2004,"path":2005,"stem":2006,"children":2007,"sidebar":2010},"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",[2008],{"title":2004,"path":2005,"stem":2006,"sidebar":2009},{"order":536},{"order":536},{"title":2012,"path":2013,"stem":2014,"children":2015,"sidebar":2018},"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",[2016],{"title":2012,"path":2013,"stem":2014,"sidebar":2017},{"order":1307},{"order":1307},{"title":2020,"path":2021,"stem":2022,"children":2023,"sidebar":2026},"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",[2024],{"title":2020,"path":2021,"stem":2022,"sidebar":2025},{"order":1313},{"order":1313},{"title":2028,"path":2029,"stem":2030,"children":2031,"sidebar":2034},"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",[2032],{"title":2028,"path":2029,"stem":2030,"sidebar":2033},{"order":1319},{"order":1319},{"title":2036,"path":2037,"stem":2038,"sidebar":2039},"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",{"order":1338},{"order":71},{"title":2042,"path":2043,"stem":2044,"children":2045,"page":280},"Quant","\u002Fzh\u002Fquant","zh\u002Fquant",[2046,2054,2059,2064,2069,2074,2079,2084,2089,2094,2099],{"title":2047,"path":2048,"stem":2049,"children":2050,"sidebar":2053},"量化投资——从可检验信号到可执行组合","\u002Fzh\u002Fquant\u002F00-index","zh\u002Fquant\u002F00-index",[2051],{"title":2047,"path":2048,"stem":2049,"sidebar":2052},{"order":71},{"order":71},{"title":2055,"path":2056,"stem":2057,"sidebar":2058},"数据获取与预处理","\u002Fzh\u002Fquant\u002F01-research-data","zh\u002Fquant\u002F01-research-data",{"order":204},{"title":2060,"path":2061,"stem":2062,"sidebar":2063},"因子与交易信号","\u002Fzh\u002Fquant\u002F02-factor-and-signals","zh\u002Fquant\u002F02-factor-and-signals",{"order":210},{"title":2065,"path":2066,"stem":2067,"sidebar":2068},"策略建模与回测","\u002Fzh\u002Fquant\u002F03-modeling-and-backtest","zh\u002Fquant\u002F03-modeling-and-backtest",{"order":216},{"title":2070,"path":2071,"stem":2072,"sidebar":2073},"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",{"order":222},{"title":2075,"path":2076,"stem":2077,"sidebar":2078},"执行策略与市场微结构概览","\u002Fzh\u002Fquant\u002F05-execution-and-microstructure","zh\u002Fquant\u002F05-execution-and-microstructure",{"order":491},{"title":2080,"path":2081,"stem":2082,"sidebar":2083},"策略上线、监控与迭代","\u002Fzh\u002Fquant\u002F06-production-and-monitoring","zh\u002Fquant\u002F06-production-and-monitoring",{"order":500},{"title":2085,"path":2086,"stem":2087,"sidebar":2088},"量化工程与工具链概览","\u002Fzh\u002Fquant\u002F07-engineering-stack","zh\u002Fquant\u002F07-engineering-stack",{"order":509},{"title":2090,"path":2091,"stem":2092,"sidebar":2093},"计量方法与实证检验","\u002Fzh\u002Fquant\u002F08-econometric-methods","zh\u002Fquant\u002F08-econometric-methods",{"order":518},{"title":2095,"path":2096,"stem":2097,"sidebar":2098},"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",{"order":527},{"title":2100,"path":2101,"stem":2102,"sidebar":2103},"量化投资文献与软件图谱","\u002Fzh\u002Fquant\u002F10-reading-software-map","zh\u002Fquant\u002F10-reading-software-map",{"order":536},{"path":219},{"id":2106,"title":1385,"body":2107,"description":3482,"extension":3483,"features":12,"hero":12,"layout":12,"locale":12,"meta":3484,"navigation":12,"path":1386,"published":3485,"seo":3486,"sidebar":3487,"stem":1387,"__hash__":3488},"docs\u002Fzh\u002Faccounting\u002Ffoundation\u002F03-decision-workshop.md",{"type":2108,"value":2109,"toc":3472},"minimark",[2110,2113,2117,2125,2129,2163,2166,2170,2173,2176,2208,2212,2300,2303,2373,2377,2380,2383,2423,2427,2430,2437,2528,2532,2539,2597,2624,3434,3437,3440,3444,3447,3450,3461],[2111,2112],"span",{"id":1385},[2114,2115,2116],"p",{},"选择指标，要从希望回答的问题开始。现金余额、利润率和 NPV 描述的是企业的不同方面，一个指标表现良好，不能代替对其他方面的判断。下面通过例题说明每项计算的用途，以及得出结论前还需要什么证据。",[2114,2118,2119,2120,2124],{},"各例使用",[2121,2122,2123],"strong",{},"相互独立的虚构数据","，金额均为英镑。余额分别计算，不要跨例结转，也不要与 Northstar 混用。可以对照数据阅读解答，也可以先关闭反馈，把问题当作自查。",[2126,2127,2128],"h2",{"id":2128},"几种工具分别能告诉我们什么",[2130,2131,2132,2139,2145,2151,2157],"ul",{},[2133,2134,2135,2138],"li",{},[2121,2136,2137],{},"现金流分类","解释现金变化来自哪里。借款能增加现金，却不会因此形成收入。",[2133,2140,2141,2144],{},[2121,2142,2143],{},"比率","把选定的数字联系起来。比率不变，内部构成仍然可能发生变化。",[2133,2146,2147,2150],{},[2121,2148,2149],{},"边际贡献分析","说明在给定假设下，销售怎样覆盖变动成本和固定成本；它本身并不预测降价会带来多少新增需求。",[2133,2152,2153,2156],{},[2121,2154,2155],{},"弹性预算","把实际成本与实际业务量下的预算成本进行比较。剩余差异指出需要调查的地方，还没有完整解释原因。",[2133,2158,2159,2162],{},[2121,2160,2161],{},"投资评价","比较未来增量现金流。NPV 按必要报酬率衡量价值，回收期关注收回投入的时间，两者都不保证预测能够实现。",[2114,2164,2165],{},"带着这些区别阅读例题，重点理解计算为什么适合当前问题，以及它还留下了哪些问题。",[2126,2167,2169],{"id":2168},"例-1现金流分类","例 1：现金流分类",[2114,2171,2172],{},"期初现金 £4,000。客户支付 £48,000，向供应商付款 £25,000、向员工付款 £12,000。企业以现金 £8,000 买设备，取得借款 £10,000，偿还借款本金 £3,000。这些是全部现金变动，本题没有利息或税款。",[2114,2174,2175],{},"区分经营、投资和融资活动，核对期末现金。解释为什么新借款不是收入，设备付款也不直接等于同额的当期费用。",[2177,2178,2180,2199],"legacy-spoiler",{"title":2179},"参考反馈",[2114,2181,2182,2183,2186,2187,2190,2191,2194,2195,2198],{},"经营现金流 = £48,000 − £25,000 − £12,000 = ",[2121,2184,2185],{},"£11,000","；投资现金流 = ",[2121,2188,2189],{},"−£8,000","；融资现金流 = £10,000 − £3,000 = ",[2121,2192,2193],{},"£7,000","。现金净增加 £10,000，期末现金 ",[2121,2196,2197],{},"£14,000","。",[2114,2200,2201,2202,2207],{},"借款形成负债，购入的设备初始确认为资产。后续计提折旧是在分配设备成本，不会再支付一次设备款。由于缺少收入、费用和应计项目的信息，仅凭这些现金收付无法计算利润。可对照",[2203,2204,2206],"a",{"href":2205},"..\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements","财务报表章节","理解这一点。",[2126,2209,2211],{"id":2210},"例-2比率与有依据的解释","例 2：比率与有依据的解释",[2213,2214,2215,2232],"table",{},[2216,2217,2218],"thead",{},[2219,2220,2221,2225,2229],"tr",{},[2222,2223,2224],"th",{},"项目",[2222,2226,2228],{"align":2227},"right","第 1 年 £",[2222,2230,2231],{"align":2227},"第 2 年 £",[2233,2234,2235,2247,2258,2269,2279,2289],"tbody",{},[2219,2236,2237,2241,2244],{},[2238,2239,2240],"td",{},"营业收入",[2238,2242,2243],{"align":2227},"100,000",[2238,2245,2246],{"align":2227},"120,000",[2219,2248,2249,2252,2255],{},[2238,2250,2251],{},"毛利",[2238,2253,2254],{"align":2227},"40,000",[2238,2256,2257],{"align":2227},"42,000",[2219,2259,2260,2263,2266],{},[2238,2261,2262],{},"净利润",[2238,2264,2265],{"align":2227},"8,000",[2238,2267,2268],{"align":2227},"6,000",[2219,2270,2271,2274,2277],{},[2238,2272,2273],{},"流动资产",[2238,2275,2276],{"align":2227},"30,000",[2238,2278,2254],{"align":2227},[2219,2280,2281,2283,2286],{},[2238,2282,1288],{},[2238,2284,2285],{"align":2227},"12,000",[2238,2287,2288],{"align":2227},"25,000",[2219,2290,2291,2294,2297],{},[2238,2292,2293],{},"流动负债",[2238,2295,2296],{"align":2227},"15,000",[2238,2298,2299],{"align":2227},"20,000",[2114,2301,2302],{},"假设流动资产仅包括现金、应收和存货。计算两年的毛利率、净利率、流动比率和速动比率。思考这组比较能为正在考虑延长信用期的供应商提供什么信息，再指出还需要哪两项证据。不要套用普遍适用的“安全比率”阈值。",[2177,2304,2305,2365],{"title":2179},[2213,2306,2307,2320],{},[2216,2308,2309],{},[2219,2310,2311,2314,2317],{},[2222,2312,2313],{},"指标",[2222,2315,2316],{"align":2227},"第 1 年",[2222,2318,2319],{"align":2227},"第 2 年",[2233,2321,2322,2333,2344,2354],{},[2219,2323,2324,2327,2330],{},[2238,2325,2326],{},"毛利率：毛利／收入",[2238,2328,2329],{"align":2227},"40%",[2238,2331,2332],{"align":2227},"35%",[2219,2334,2335,2338,2341],{},[2238,2336,2337],{},"净利率：净利润／收入",[2238,2339,2340],{"align":2227},"8%",[2238,2342,2343],{"align":2227},"5%",[2219,2345,2346,2349,2352],{},[2238,2347,2348],{},"流动比率：流动资产／流动负债",[2238,2350,2351],{"align":2227},"2.00",[2238,2353,2351],{"align":2227},[2219,2355,2356,2359,2362],{},[2238,2357,2358],{},"速动比率：（流动资产 − 存货）／流动负债",[2238,2360,2361],{"align":2227},"1.20",[2238,2363,2364],{"align":2227},"0.75",[2114,2366,2367,2368,2372],{},"收入增长 20%，利润率却下降。流动比率虽然没变，其中对存货的依赖却明显增加，需要进一步检查流动性。但这还不足以认定企业会违约，或存货已经过时。应查看应收账龄、存货销售与减值情况，再对照现金预测检查能否按约付款。仅凭比率无法确定收款日期。",[2203,2369,2371],{"href":2370},"..\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis","比率分析","还讨论了分母口径和可比性。",[2126,2374,2376],{"id":2375},"例-3边际贡献与降价","例 3：边际贡献与降价",[2114,2378,2379],{},"维修套装单价 £25，单位变动成本 £15，每月固定成本 £12,000，预计销售 1,800 套。假设产销相等，在 2,200 套以内保持同样的成本结构。",[2114,2381,2382],{},"计算单位贡献、保本销量、预计利润、安全边际数量及其占预计销量的百分比，以及达到 £9,000 目标利润所需销量。再将单价降低 10%，其他假设不变，重算保本点、利润和安全边际。降价会自动增加利润吗？",[2177,2384,2385,2408],{"title":2179},[2114,2386,2387,2388,2391,2392,2395,2396,2399,2400,2403,2404,2407],{},"原单位贡献 ",[2121,2389,2390],{},"£10","。保本销量 = £12,000 \u002F £10 = ",[2121,2393,2394],{},"1,200 套","；预计利润 = £18,000 − £12,000 = ",[2121,2397,2398],{},"£6,000","；安全边际 ",[2121,2401,2402],{},"600 套／33.3%","。目标利润 £9,000 需要 ",[2121,2405,2406],{},"2,100 套","，在给定范围内。",[2114,2409,2410,2411,2414,2415,2418,2419,2422],{},"价格降至 £22.50 后，单位贡献为 £7.50，保本点 ",[2121,2412,2413],{},"1,600 套","，1,800 套的利润 ",[2121,2416,2417],{},"£1,500","，安全边际 ",[2121,2420,2421],{},"200 套／11.1%","。要保持原 £6,000 利润需卖 2,400 套，超过 2,200 套的假定范围；必须先取得新的产能与成本预测，不能直接认定可行。正的安全边际不等于需求风险一定低。",[2126,2424,2426],{"id":2425},"例-4在相同业务量下比较成本","例 4：在相同业务量下比较成本",[2114,2428,2429],{},"原预算产量为 1,000 件，单位变动材料成本 £8、单位变动人工成本 £6。实际生产 1,200 件，材料成本 £10,200，人工成本 £6,900。这两项成本中没有固定部分。",[2114,2431,2432,2433,2436],{},"计算原预算、按实际产量编制的弹性预算，以及实际成本相对弹性预算的差异。采用",[2121,2434,2435],{},"实际成本减弹性预算成本","：正值为不利 U，负值为有利 F。解释为何不能只凭总差异确定价格或效率原因。",[2177,2438,2439,2521],{"title":2179},[2213,2440,2441,2460],{},[2216,2442,2443],{},[2219,2444,2445,2448,2451,2454,2457],{},[2222,2446,2447],{},"成本",[2222,2449,2450],{"align":2227},"原预算 £",[2222,2452,2453],{"align":2227},"弹性预算 £",[2222,2455,2456],{"align":2227},"实际 £",[2222,2458,2459],{"align":2227},"实际减弹性 £",[2233,2461,2462,2478,2494],{},[2219,2463,2464,2467,2469,2472,2475],{},[2238,2465,2466],{},"材料",[2238,2468,2265],{"align":2227},[2238,2470,2471],{"align":2227},"9,600",[2238,2473,2474],{"align":2227},"10,200",[2238,2476,2477],{"align":2227},"600 U",[2219,2479,2480,2483,2485,2488,2491],{},[2238,2481,2482],{},"人工",[2238,2484,2268],{"align":2227},[2238,2486,2487],{"align":2227},"7,200",[2238,2489,2490],{"align":2227},"6,900",[2238,2492,2493],{"align":2227},"300 F",[2219,2495,2496,2501,2506,2511,2516],{},[2238,2497,2498],{},[2121,2499,2500],{},"合计",[2238,2502,2503],{"align":2227},[2121,2504,2505],{},"14,000",[2238,2507,2508],{"align":2227},[2121,2509,2510],{},"16,800",[2238,2512,2513],{"align":2227},[2121,2514,2515],{},"17,100",[2238,2517,2518],{"align":2227},[2121,2519,2520],{},"300 U",[2114,2522,2523,2524,2198],{},"实际成本比原预算高出 £3,100，其中 £2,800 来自产量增加，另有 £300 超出弹性预算。材料涨价、耗损和规格变化都可能解释差异，但还需要数量和价格资料来判断；分析人工差异则需要工时和工资率。即使人工成本总体低于预算，也不能排除质量问题。可继续学习",[2203,2525,2527],{"href":2526},"..\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances","差异拆解",[2126,2529,2531],{"id":2530},"例-5互斥投资方案","例 5：互斥投资方案",[2114,2533,2534,2535,2538],{},"两个互斥项目寿命均为三年，假定风险相同，最多选择一个。必要报酬率 ",[2121,2536,2537],{},"10%"," 是本题假设。下表已经包含全部增量项目现金流，不要另加税款、营运资金、残值或融资流量。NPV 计算假定未来现金流均发生在年末。",[2213,2540,2541,2554],{},[2216,2542,2543],{},[2219,2544,2545,2548,2551],{},[2222,2546,2547],{},"时点",[2222,2549,2550],{"align":2227},"项目 A £",[2222,2552,2553],{"align":2227},"项目 B £",[2233,2555,2556,2566,2577,2587],{},[2219,2557,2558,2561,2564],{},[2238,2559,2560],{},"0",[2238,2562,2563],{"align":2227},"(24,000)",[2238,2565,2563],{"align":2227},[2219,2567,2568,2571,2574],{},[2238,2569,2570],{},"1",[2238,2572,2573],{"align":2227},"10,000",[2238,2575,2576],{"align":2227},"2,000",[2219,2578,2579,2582,2584],{},[2238,2580,2581],{},"2",[2238,2583,2573],{"align":2227},[2238,2585,2586],{"align":2227},"11,000",[2219,2588,2589,2592,2594],{},[2238,2590,2591],{},"3",[2238,2593,2573],{"align":2227},[2238,2595,2596],{"align":2227},"19,000",[2598,2599,2600,2603,2611,2618,2621],"ol",{},[2133,2601,2602],{},"计算各年现值与 NPV，按给定的价值标准选择方案。",[2133,2604,2605,2606,2610],{},"用电子表格，或参考英文",[2203,2607,2609],{"href":2608},"..\u002F..\u002F..\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting","资本预算计算示例","，求 IRR 并检查该利率下 NPV 接近零。",[2133,2612,2613,2614,2617],{},"严格按",[2121,2615,2616],{},"年末才收款","，指出普通回收期和折现回收期。",[2133,2619,2620],{},"另作年内插值近似：普通回收期假设回收年度内现金均匀流入；折现回收期插值把该年折现后的金额均匀分配。明确标注近似，不要悄悄更换时间假设。",[2133,2622,2623],{},"给出建议、一项预测风险，以及即使 NPV 较低、较早回款仍可能有价值的原因。",[2177,2625,2627,3298,3415,3418,3425],{"title":2626},"计算与建议",[2111,2628,2631],{"className":2629},[2630],"katex-display",[2111,2632,2635,2780],{"className":2633},[2634],"katex",[2111,2636,2639],{"className":2637},[2638],"katex-mathml",[2640,2641,2644],"math",{"xmlns":2642,"display":2643},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML","block",[2645,2646,2647,2775],"semantics",{},[2648,2649,2653,2718],"mtable",{"rowspacing":2650,"columnalign":2651,"columnspacing":2652},"0.25em","right left","0em",[2654,2655,2656,2674],"mtr",{},[2657,2658,2659],"mtd",{},[2660,2661,2663],"mstyle",{"scriptlevel":2560,"displaystyle":2662},"true",[2664,2665,2666,2670],"msub",{},[2667,2668,2669],"mtext",{},"NPV",[2671,2672,2673],"mi",{},"A",[2657,2675,2676],{},[2660,2677,2678],{"scriptlevel":2560,"displaystyle":2662},[2679,2680,2681,2683,2687,2690,2694,2697,2700,2703],"mrow",{},[2679,2682],{},[2684,2685,2686],"mo",{},"=",[2684,2688,2689],{},"−",[2691,2692,2693],"mn",{},"24",[2684,2695,2696],{"separator":2662},",",[2691,2698,2699],{},"000",[2684,2701,2702],{},"+",[2704,2705,2706,2715],"mfrac",{},[2679,2707,2708,2711,2713],{},[2691,2709,2710],{},"10",[2684,2712,2696],{"separator":2662},[2691,2714,2699],{},[2691,2716,2717],{},"1.1",[2654,2719,2720,2726],{},[2657,2721,2722],{},[2660,2723,2724],{"scriptlevel":2560,"displaystyle":2662},[2679,2725],{},[2657,2727,2728],{},[2660,2729,2730],{"scriptlevel":2560,"displaystyle":2662},[2679,2731,2732,2734,2738,2740,2757,2759],{},[2679,2733],{},[2735,2736],"mspace",{"width":2737},"1em",[2684,2739,2702],{},[2704,2741,2742,2750],{},[2679,2743,2744,2746,2748],{},[2691,2745,2710],{},[2684,2747,2696],{"separator":2662},[2691,2749,2699],{},[2751,2752,2753,2755],"msup",{},[2691,2754,2717],{},[2691,2756,2581],{},[2684,2758,2702],{},[2704,2760,2761,2769],{},[2679,2762,2763,2765,2767],{},[2691,2764,2710],{},[2684,2766,2696],{"separator":2662},[2691,2768,2699],{},[2751,2770,2771,2773],{},[2691,2772,2717],{},[2691,2774,2591],{},[2776,2777,2779],"annotation",{"encoding":2778},"application\u002Fx-tex","\\begin{aligned}\n\\text{NPV}_A&=-24,000+\\frac{10,000}{1.1}\\\\\n&\\quad+\\frac{10,000}{1.1^2}+\\frac{10,000}{1.1^3}\n\\end{aligned}",[2111,2781,2784],{"className":2782,"ariaHidden":2662},[2783],"katex-html",[2111,2785,2788,2793],{"className":2786},[2787],"base",[2111,2789],{"className":2790,"style":2792},[2791],"strut","height:4.3149em;vertical-align:-1.9074em;",[2111,2794,2797],{"className":2795},[2796],"mord",[2111,2798,2800,2906],{"className":2799},[2648],[2111,2801,2804],{"className":2802},[2803],"col-align-r",[2111,2805,2809,2897],{"className":2806},[2807,2808],"vlist-t","vlist-t2",[2111,2810,2813,2894],{"className":2811},[2812],"vlist-r",[2111,2814,2818,2885],{"className":2815,"style":2817},[2816],"vlist","height:2.4074em;",[2111,2819,2821,2826],{"style":2820},"top:-4.4074em;",[2111,2822],{"className":2823,"style":2825},[2824],"pstrut","height:3.3214em;",[2111,2827,2829],{"className":2828},[2796],[2111,2830,2832,2839],{"className":2831},[2796],[2111,2833,2836],{"className":2834},[2796,2835],"text",[2111,2837,2669],{"className":2838},[2796],[2111,2840,2843],{"className":2841},[2842],"msupsub",[2111,2844,2846,2876],{"className":2845},[2807,2808],[2111,2847,2849,2871],{"className":2848},[2812],[2111,2850,2853],{"className":2851,"style":2852},[2816],"height:0.3283em;",[2111,2854,2856,2860],{"style":2855},"top:-2.55em;margin-right:0.05em;",[2111,2857],{"className":2858,"style":2859},[2824],"height:2.7em;",[2111,2861,2867],{"className":2862},[2863,2864,2865,2866],"sizing","reset-size6","size3","mtight",[2111,2868,2673],{"className":2869},[2796,2870,2866],"mathnormal",[2111,2872,2875],{"className":2873},[2874],"vlist-s","​",[2111,2877,2879],{"className":2878},[2812],[2111,2880,2883],{"className":2881,"style":2882},[2816],"height:0.15em;",[2111,2884],{},[2111,2886,2888,2891],{"style":2887},"top:-2.1em;",[2111,2889],{"className":2890,"style":2825},[2824],[2111,2892],{"className":2893},[2796],[2111,2895,2875],{"className":2896},[2874],[2111,2898,2900],{"className":2899},[2812],[2111,2901,2904],{"className":2902,"style":2903},[2816],"height:1.9074em;",[2111,2905],{},[2111,2907,2910],{"className":2908},[2909],"col-align-l",[2111,2911,2913,3290],{"className":2912},[2807,2808],[2111,2914,2916,3287],{"className":2915},[2812],[2111,2917,2919,3051],{"className":2918,"style":2817},[2816],[2111,2920,2921,2924],{"style":2820},[2111,2922],{"className":2923,"style":2825},[2824],[2111,2925,2927,2930,2934,2938,2941,2944,2947,2951,2955,2958,2962,2966,2969],{"className":2926},[2796],[2111,2928],{"className":2929},[2796],[2111,2931],{"className":2932,"style":2933},[2735],"margin-right:0.2778em;",[2111,2935,2686],{"className":2936},[2937],"mrel",[2111,2939],{"className":2940,"style":2933},[2735],[2111,2942,2689],{"className":2943},[2796],[2111,2945,2693],{"className":2946},[2796],[2111,2948,2696],{"className":2949},[2950],"mpunct",[2111,2952],{"className":2953,"style":2954},[2735],"margin-right:0.1667em;",[2111,2956,2699],{"className":2957},[2796],[2111,2959],{"className":2960,"style":2961},[2735],"margin-right:0.2222em;",[2111,2963,2702],{"className":2964},[2965],"mbin",[2111,2967],{"className":2968,"style":2961},[2735],[2111,2970,2972,2977,3047],{"className":2971},[2796],[2111,2973],{"className":2974},[2975,2976],"mopen","nulldelimiter",[2111,2978,2980],{"className":2979},[2704],[2111,2981,2983,3038],{"className":2982},[2807,2808],[2111,2984,2986,3035],{"className":2985},[2812],[2111,2987,2990,3003,3014],{"className":2988,"style":2989},[2816],"height:1.3214em;",[2111,2991,2993,2997],{"style":2992},"top:-2.314em;",[2111,2994],{"className":2995,"style":2996},[2824],"height:3em;",[2111,2998,3000],{"className":2999},[2796],[2111,3001,2717],{"className":3002},[2796],[2111,3004,3006,3009],{"style":3005},"top:-3.23em;",[2111,3007],{"className":3008,"style":2996},[2824],[2111,3010],{"className":3011,"style":3013},[3012],"frac-line","border-bottom-width:0.04em;",[2111,3015,3017,3020],{"style":3016},"top:-3.677em;",[2111,3018],{"className":3019,"style":2996},[2824],[2111,3021,3023,3026,3029,3032],{"className":3022},[2796],[2111,3024,2710],{"className":3025},[2796],[2111,3027,2696],{"className":3028},[2950],[2111,3030],{"className":3031,"style":2954},[2735],[2111,3033,2699],{"className":3034},[2796],[2111,3036,2875],{"className":3037},[2874],[2111,3039,3041],{"className":3040},[2812],[2111,3042,3045],{"className":3043,"style":3044},[2816],"height:0.686em;",[2111,3046],{},[2111,3048],{"className":3049},[3050,2976],"mclose",[2111,3052,3053,3056],{"style":2887},[2111,3054],{"className":3055,"style":2825},[2824],[2111,3057,3059,3062,3066,3069,3072,3075,3178,3181,3184,3187],{"className":3058},[2796],[2111,3060],{"className":3061},[2796],[2111,3063],{"className":3064,"style":3065},[2735],"margin-right:1em;",[2111,3067],{"className":3068,"style":2961},[2735],[2111,3070,2702],{"className":3071},[2965],[2111,3073],{"className":3074,"style":2961},[2735],[2111,3076,3078,3081,3175],{"className":3077},[2796],[2111,3079],{"className":3080},[2975,2976],[2111,3082,3084],{"className":3083},[2704],[2111,3085,3087,3167],{"className":3086},[2807,2808],[2111,3088,3090,3164],{"className":3089},[2812],[2111,3091,3093,3136,3144],{"className":3092,"style":2989},[2816],[2111,3094,3095,3098],{"style":2992},[2111,3096],{"className":3097,"style":2996},[2824],[2111,3099,3101,3105],{"className":3100},[2796],[2111,3102,3104],{"className":3103},[2796],"1.",[2111,3106,3108,3111],{"className":3107},[2796],[2111,3109,2570],{"className":3110},[2796],[2111,3112,3114],{"className":3113},[2842],[2111,3115,3117],{"className":3116},[2807],[2111,3118,3120],{"className":3119},[2812],[2111,3121,3124],{"className":3122,"style":3123},[2816],"height:0.7401em;",[2111,3125,3127,3130],{"style":3126},"top:-2.989em;margin-right:0.05em;",[2111,3128],{"className":3129,"style":2859},[2824],[2111,3131,3133],{"className":3132},[2863,2864,2865,2866],[2111,3134,2581],{"className":3135},[2796,2866],[2111,3137,3138,3141],{"style":3005},[2111,3139],{"className":3140,"style":2996},[2824],[2111,3142],{"className":3143,"style":3013},[3012],[2111,3145,3146,3149],{"style":3016},[2111,3147],{"className":3148,"style":2996},[2824],[2111,3150,3152,3155,3158,3161],{"className":3151},[2796],[2111,3153,2710],{"className":3154},[2796],[2111,3156,2696],{"className":3157},[2950],[2111,3159],{"className":3160,"style":2954},[2735],[2111,3162,2699],{"className":3163},[2796],[2111,3165,2875],{"className":3166},[2874],[2111,3168,3170],{"className":3169},[2812],[2111,3171,3173],{"className":3172,"style":3044},[2816],[2111,3174],{},[2111,3176],{"className":3177},[3050,2976],[2111,3179],{"className":3180,"style":2961},[2735],[2111,3182,2702],{"className":3183},[2965],[2111,3185],{"className":3186,"style":2961},[2735],[2111,3188,3190,3193,3284],{"className":3189},[2796],[2111,3191],{"className":3192},[2975,2976],[2111,3194,3196],{"className":3195},[2704],[2111,3197,3199,3276],{"className":3198},[2807,2808],[2111,3200,3202,3273],{"className":3201},[2812],[2111,3203,3205,3245,3253],{"className":3204,"style":2989},[2816],[2111,3206,3207,3210],{"style":2992},[2111,3208],{"className":3209,"style":2996},[2824],[2111,3211,3213,3216],{"className":3212},[2796],[2111,3214,3104],{"className":3215},[2796],[2111,3217,3219,3222],{"className":3218},[2796],[2111,3220,2570],{"className":3221},[2796],[2111,3223,3225],{"className":3224},[2842],[2111,3226,3228],{"className":3227},[2807],[2111,3229,3231],{"className":3230},[2812],[2111,3232,3234],{"className":3233,"style":3123},[2816],[2111,3235,3236,3239],{"style":3126},[2111,3237],{"className":3238,"style":2859},[2824],[2111,3240,3242],{"className":3241},[2863,2864,2865,2866],[2111,3243,2591],{"className":3244},[2796,2866],[2111,3246,3247,3250],{"style":3005},[2111,3248],{"className":3249,"style":2996},[2824],[2111,3251],{"className":3252,"style":3013},[3012],[2111,3254,3255,3258],{"style":3016},[2111,3256],{"className":3257,"style":2996},[2824],[2111,3259,3261,3264,3267,3270],{"className":3260},[2796],[2111,3262,2710],{"className":3263},[2796],[2111,3265,2696],{"className":3266},[2950],[2111,3268],{"className":3269,"style":2954},[2735],[2111,3271,2699],{"className":3272},[2796],[2111,3274,2875],{"className":3275},[2874],[2111,3277,3279],{"className":3278},[2812],[2111,3280,3282],{"className":3281,"style":3044},[2816],[2111,3283],{},[2111,3285],{"className":3286},[3050,2976],[2111,3288,2875],{"className":3289},[2874],[2111,3291,3293],{"className":3292},[2812],[2111,3294,3296],{"className":3295,"style":2903},[2816],[2111,3297],{},[2213,3299,3300,3312],{},[2216,3301,3302],{},[2219,3303,3304,3306,3309],{},[2222,3305,2313],{},[2222,3307,3308],{"align":2227},"项目 A",[2222,3310,3311],{"align":2227},"项目 B",[2233,3313,3314,3325,3335,3346,3363,3374,3384,3393,3404],{},[2219,3315,3316,3319,3322],{},[2238,3317,3318],{},"第 1 年现值 £",[2238,3320,3321],{"align":2227},"9,090.91",[2238,3323,3324],{"align":2227},"1,818.18",[2219,3326,3327,3330,3333],{},[2238,3328,3329],{},"第 2 年现值 £",[2238,3331,3332],{"align":2227},"8,264.46",[2238,3334,3321],{"align":2227},[2219,3336,3337,3340,3343],{},[2238,3338,3339],{},"第 3 年现值 £",[2238,3341,3342],{"align":2227},"7,513.15",[2238,3344,3345],{"align":2227},"14,274.98",[2219,3347,3348,3353,3358],{},[2238,3349,3350],{},[2121,3351,3352],{},"NPV £",[2238,3354,3355],{"align":2227},[2121,3356,3357],{},"868.52",[2238,3359,3360],{"align":2227},[2121,3361,3362],{},"1,184.07",[2219,3364,3365,3368,3371],{},[2238,3366,3367],{},"IRR，约",[2238,3369,3370],{"align":2227},"12.044%",[2238,3372,3373],{"align":2227},"12.147%",[2219,3375,3376,3379,3382],{},[2238,3377,3378],{},"年末收款假设下的普通回收期",[2238,3380,3381],{"align":2227},"第 3 年末",[2238,3383,3381],{"align":2227},[2219,3385,3386,3389,3391],{},[2238,3387,3388],{},"年末收款假设下的折现回收期",[2238,3390,3381],{"align":2227},[2238,3392,3381],{"align":2227},[2219,3394,3395,3398,3401],{},[2238,3396,3397],{},"插值普通回收期，年",[2238,3399,3400],{"align":2227},"2.400",[2238,3402,3403],{"align":2227},"2.579",[2219,3405,3406,3409,3412],{},[2238,3407,3408],{},"插值折现回收期，年",[2238,3410,3411],{"align":2227},"2.884",[2238,3413,3414],{"align":2227},"2.917",[2114,3416,3417],{},"A 的普通插值为 2 + £4,000 \u002F £10,000 = 2.4 年；折现插值为 2 + (£24,000 − £9,090.91 − £8,264.46) \u002F £7,513.15 ≈ 2.884 年。中间运算保留未舍入数值，最后再取小数。",[2114,3419,3420,3421,3424],{},"在给定假设下，",[2121,3422,3423],{},"B 的 NPV 高 £315.55","。这一结果对最后一笔 £19,000 的回款较为敏感：如果少收 £1,000，B 的 NPV 会减少 £751.31，降至 £432.76；若 A 不变，两者的排名就会反转。A 能更早收回较多现金，在流动性受限时可能更有吸引力。NPV 为正或 IRR 超过 10%，都不保证预测一定实现。",[2114,3426,3427,3433],{},[2203,3428,3432],{"href":3429,"rel":3430},"https:\u002F\u002Fwww.accaglobal.com\u002Fuk\u002Fen\u002Fstudent\u002Fexam-support-resources\u002Fprodipsust-study-resources\u002Ftechnical-articles\u002Finvestment-appraisal.html",[3431],"nofollow","ACCA 的投资评价指南","进一步讨论相关现金流与时间安排。本页的题目和数字均为原创教学示例。",[2126,3435,3436],{"id":3436},"检查你的推理",[2114,3438,3439],{},"阅读任何一个例题，都要区分计算结果与自己据此得出的结论。如果答案不同，先找出对业务事实、比较基准或时间假设的理解是否不同。把这层关系重新梳理清楚，才容易将方法用于数字不同的另一家企业。",[2126,3441,3443],{"id":3442},"延伸比较本身是否合理","延伸：比较本身是否合理",[2114,3445,3446],{},"比率例题比较两个年度，弹性预算比较相同业务量下的成本，投资例题则比较互斥的未来项目。三种比较需要不同的基准。解释差异以前，先问哪些条件保持一致，哪些条件发生了变化。",[2114,3448,3449],{},"弹性预算留下的差异不会自动说明原因：不同的价格、工时或用量变化，可能形成相同的总差额。同样，较高的 NPV 也是在给定现金流和风险假设下得出的结论。例 5 的预测变式说明，假设改变后，排名可能反转。因此，一份有用的总结应同时记下结果、成立条件，以及什么证据会使自己重新判断。",[2114,3451,3452,3453,3456,3457,3460],{},"继续阅读时，可先用",[2203,3454,1405],{"href":3455},"..\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts","区分成本行为与决策相关性，再通过",[2203,3458,1457],{"href":3459},"..\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions","理解产能和机会成本怎样影响选择。",[2114,3462,3463,3467,3468],{},[2203,3464,3466],{"href":3465},".\u002F","返回基础路径"," · ",[2203,3469,3471],{"href":3470},"..\u002F00-index","继续完整会计课程",{"title":10,"searchDepth":210,"depth":210,"links":3473},[3474,3475,3476,3477,3478,3479,3480,3481],{"id":2128,"depth":210,"text":2128},{"id":2168,"depth":210,"text":2169},{"id":2210,"depth":210,"text":2211},{"id":2375,"depth":210,"text":2376},{"id":2425,"depth":210,"text":2426},{"id":2530,"depth":210,"text":2531},{"id":3436,"depth":210,"text":3436},{"id":3442,"depth":210,"text":3443},"总结不同会计指标的用途，并通过例题理解比较、判断与结论的边界","md",{},true,{"title":1385,"description":3482},{"order":222},"yuxBFKCI2VU8oetl6xksiz2AH2BevkDcKBUQroOv2OI",[3490,3492],{"title":1380,"path":1381,"stem":1382,"description":3491,"children":-1},"从跨月收付款计划推导现金余额、融资需求和延迟回款影响",{"title":1391,"path":1392,"stem":1393,"description":3493,"children":-1},"用会计信息支持内部计划、控制和决策",1791046371136]