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Random Scenario Forecasts Versus Stochastic Forecasts
by Shripad Tuljapurkar, Ronald D. Lee and Qi Li
WP 2004-073
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Probabilistic 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
C:\Eudora\attach\demo_3_25_04.pdfexamples 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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