Random Scenario Forecasts Versus Stochastic Forecasts
AbstractProbabilistic population forecasts are useful because they describe uncertainty in a quantitatively useful way. One approach (that we call LT) uses historical data to estimate stochastic models (e.g., a time series model) of vital rates, and then makes forecasts. Another (we call it RS) began as a kind of randomized scenario: we consider its simplest variant, in which expert opinion is used to make probability distributions for terminal vital rates, and smooth trajectories are followed over time. We use analysis and examples to show several key differences between these methods: serial correlations in the forecast are much smaller in LT; the variance in LT models of vital rates (especially fertility)is much higher than in RS models that are based on official expert scenarios; trajectories in LT are much more irregular than in RS; probability intervals in LT tend to widen faster over forecast time. Newer versions of RS have been developed that reduce or eliminate some of these differences.
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Bibliographic InfoPaper provided by University of Michigan, Michigan Retirement Research Center in its series Working Papers with number wp073.
Length: 26 pages
Date of creation: Apr 2004
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- António Brandão Moniz, 2008. "Assessing scenarios on the future of work," Enterprise and Work Innovation Studies, Universidade Nova de Lisboa, IET/CESNOVA-Research on Enterprise and Work Innovation, Faculty of Science and Technology, vol. 4(4), pages 91-106, November.
- Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
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