Forecasting
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Forecasting

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Forecasting can enable better data-driven decisions. This presentation explores the spectrum of forecasting techniques, including scenario construction and powerful-yet-approachable quantitative......

Forecasting can enable better data-driven decisions. This presentation explores the spectrum of forecasting techniques, including scenario construction and powerful-yet-approachable quantitative methods. See how to match appropriate techniques to decision-support needs and then implement them in ubiquitous productivity software. Learn effective strategies for visualizing and communicating forecast outcomes, uncertainty, and sensitivity. Jeff details his forecasting experience at the Medical School. Examples include financial forecasts informed by operational data and scenario analysis.

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  • 1. Forecasting Jeff Horon 25 January 2011
  • 2. About me [& forecasting] BA Econ / Honors thesis in petroleum price forecasting MBA [Winter 2011] / Emphases in Finance & Strategy; Formal training in decision support Sr. Analyst / Medical School Design responsibility for econometric and financial modeling describing the Medical School’s $0.5B research enterprise [Ad hoc and standardized reporting, surveys, dashboards]
  • 3. Fore ▪ cast Casten Fore-“Contrive” “Before [the fact]”
  • 4. We forecast all the time Cool Kids Personal Space Achievers Studious
  • 5. You’re doing it right now Not so bad
  • 6. Exit Strategy
  • 7. Why forecast? Decision Support! Decision Support! D-e-c-i-s-i-o-n S-u-p-p-o-r-t! Backward-looking: “How did we do?” (it pays to correct your mistakes) In the present: “How are we doing?” (it pays to not make the mistakes in the first place) Forward-looking: “Are we headed in the right direction?” (it pays to be proactive, consistent with reasonable expectations)
  • 8. Continuum of methods Qualitative --> Quantitative Subjective Objective‘Gut feeling’ Extrapolation Prediction Intervals Monte Carlo Casual observation Decision trees / Scenario construction
  • 9. Cost [Method] Objectivity Cost ~ Complexity ~ Time investment (Skills, effort devoted to creation, maintenance, delivery)
  • 10. Cost [Scale] Scale Cost ~ Resources ~ Time and/or capital investment (Skills, effort, capital devoted to implementation and maintenance)
  • 11. Expected Value of Information Objectivity; Scale Expected Value of Information ~ Quality ~ Scale of Decision
  • 12. Decision Framework Cost EV (Info) Objectivity; Scale Marginal analysis: Target Expected Value of Information = Cost of Information
  • 13. Practical Decision Framework High Impact -High per-unit stakes High EV (Info) -High volume Repeated Low Impact -Low per-unit stakes Low EV (Info) -Low volume Not Repeated
  • 14. Practical Implementation Cost EV (Info) EV (Info) Cost Objectivity; Scale ‘Back of Envelope’ ‘Sketching’ ‘Low-Fi Prototyping’
  • 15. Workflow START Identify unmet decision support need Match method to need Is it feasible? Is it practical? Create Sketch Prototype ‘Gut check’ results ‘Sell idea’ Improve Share Standardize Build into existing reporting
  • 16. Method – Gut feeling Subjective “Well, we usually fall within… ” “I’ve got a good feeling about… ” Based upon experience Useful for a check if you are ‘in the ballpark’
  • 17. Method – Casual observation Subjective “If we stay on the same growth trajectory as the past few years… ” Based upon experience and data
  • 18. However, as our minds rush to think ahead… Often this is fine…
  • 19. …. but sometimes it isn’t… Analogous to extrapolation outside the ‘relevant range’
  • 20. Method – Extrapolation Less subjective; ‘Baked-in’ assumptions Based upon historical data
  • 21. Method – Extrapolation Extremely easy to implement in Excel =FORECAST(); =TREND(); =GROWTH() or Right-click graphed data series, ‘Add Trendline’ Every investment prospectus: “Past performance does not guarantee future results”
  • 22. Method – Decision tree Potential for ‘guesstimation’ in the absence of historical data Typically based upon historical data Method for calculating an expected value across multiple possible outcomes; Branches can be decisions or random events
  • 23. Example – Decision treeKey: Decision Random Event Result 50% High Savings EV = -$12k Yes Meets EV = $20k specs? No Yes 50% Low Savings Invest? No EV = $10k No savings EV = $0
  • 24. Example – Decision treeKey: Decision Random Event Result 50% High Savings EV = -$12k Yes Meets EV = $20k specs? No Yes 50% Low Savings Invest? No EV = $10kEV = +$3k Invest = Yes No savings EV = $0
  • 25. Method – Scenario construction Some room for subjectivity in assumptions; Helpful to jog memory regarding important variables, events, etc. Based upon historical observations or future expectations Flexible approach depending on decision support need, because you create the scenario
  • 26. Use case – Effects of legislation Similar to a marketing ‘conversion rate’ calculation NIH budgeted extra $10B under ARRA ARRA sets aside $1B for medical school facilities ARRA dictates internal fund distribution similar to ‘regular appropriation’ funds$1B for medical school facilities $9B proportionally budgeted U-M Med School tends to attain ‘market share’ of 2.7% U-M Med School tends to attain ‘market share’ of ‘regular appropriation’ funds to medical schools of 1% of ‘regular appropriation’ funds x 2.7% = $27M x 1% = $90M
  • 27. Use case – Effects of legislation Similar to a marketing ‘conversion rate’ calculation NIH budgeted extra $10B under ARRA ARRA sets aside $1B for medical school facilities ARRA dictates internal fund distribution similar to ‘regular appropriation’ funds$1B for medical school facilities $9B proportionally budgeted U-M Med School tends to attain ‘market share’ of 2.7% U-M Med School tends to attain ‘market share’ of ‘regular appropriation’ funds to medical schools of 1% of ‘regular appropriation’ funds x 2.7% = $27M x 1% = $90MProposals submitted, not funded Noted sensitivity to market share % $82M
  • 28. Use case – Revenue projection Fiscal YearAwards
  • 29. Use case – Revenue projection Fiscal YearAwards
  • 30. Use case – Revenue projectionAwards ($) Current FY Fiscal Year
  • 31. Use case – Revenue projection Fiscal YearAwardsProposals
  • 32. Use case – Revenue projection Fiscal YearAwardsProposals
  • 33. Use case – Revenue projectionAwards ($) Current FY Fiscal Year
  • 34. Use case – Revenue projectionAwards ($) Current FY Fiscal Year
  • 35. Method – Prediction intervals For unknown population mean and variance, the endpoints of a 100p% prediction interval for Xn + 1 are: Sample mean Sample standard Observations deviation 100((1 + p)/2)th percentile of Students t-distribution with n − 1 degrees of freedom
  • 36. Method – Prediction intervals Upper Endpoint Sample mean Lower Endpoint
  • 37. Method – Monte Carlo simulation Use random sampling to work around difficult or impossible deterministic problems Variable 1 Variable 2 Variable 3 Result
  • 38. Best Practices ‘Gut check’ (Expectations ~ Results?) Litmus test Sensitivity analysis Adjust for inflation
  • 39. Communication Always communicate uncertainty, particularly sensitive outcomes Source: CBO http://www.cbo.gov/ftpdocs/100xx/doc10014/03-20-PresidentBudget.pdf p34
  • 40. Q&A Jeff Horon jhoron@umich.eduhttp://www.umich.edu/~jhoron/