Random Scenario Forecasts Versus Stochastic Forecasts

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. Authors’ Acknowledgements This work was supported by grants from the National Institute of Aging to Tuljapurkar and to Lee, and by the Center for the Economics and Demography of Aging at the University of California at Berkeley, and from the Michigan Retirement Research Center. Li was also supported in part by the Morrison Institute of Population and Resource Studies at Stanford University.