Uncertainty in Forecasting

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Alternative Scenarios • Easy to see effects of different assumptions/policies • Does not require new models or expertise • Gives users choices • Least costly way to examine uncertainty • No measure of likelihood of occurrence • May understate true level of uncertainty • Fail to capture real world fluctuations COG/MPO Modeling Conference, July 17, 2009, San Diego, CA 20
Model-Based Probability Intervals COG/MPO Modeling Conference, July 17, 2009, San Diego, CA 21
Page 1 and 2: Uncertainty in Forecasting Jeff Tay
Page 3 and 4: Key Points • Knowledge of the beh
Page 5 and 6: Value of Understanding Forecast Unc
Page 7 and 8: Forecast Error Decreases with Size
Page 9 and 10: Forecast Bias is Lowest for Stable/
Page 11 and 12: Forecast Error Increases Linearly w
Page 13 and 14: Current Approaches to Forecast Unce
Page 15 and 16: Land Use Distribution Scenarios Cur
Page 17 and 18: Housing Unit Growth, 2000-20302030
Page 19: Range of Projections U.S. Populatio
Page 23 and 24: ARIMA Models: States 68 % Predictio
Page 25 and 26: Empirically-Based Probability Inter
Page 27 and 28: Empirical Intervals: Subcounty 95%
Page 29 and 30: Expert Panels COG/MPO Modeling Conf
Page 31 and 32: Economic Impacts of Border Wait Tim
Page 33 and 34: Survey of State and Regional Agenci
Page 35 and 36: Migration is the Highest Ranked Sou
Page 37 and 38: Only 1 Agency Not Currently or Plan
Page 39 and 40: Alternate Scenarios/Range of Values
Page 41: Website URL of Uncertainty Report h

Uncertainty in Forecasting

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