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 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.
Publié le : 2004-08-14
Classification:
Probabilistic forecast,
Population forecast,
Trajectory,
Vital rates,
Scenario,
Random scenario,
Dependency ratio
@article{1091543054,
author = {Tuljapurkar, Shripad and Lee, Ronald D. and Li, Qi},
title = {Random Scenario Forecasts Versus Stochastic Forecasts},
journal = {Internat. Statist. Rev.},
volume = {72},
number = {1},
year = {2004},
pages = { 185-199},
language = {en},
url = {http://dml.mathdoc.fr/item/1091543054}
}
Tuljapurkar, Shripad; Lee, Ronald D.; Li, Qi. Random Scenario Forecasts Versus Stochastic Forecasts. Internat. Statist. Rev., Tome 72 (2004) no. 1, pp. 185-199. http://gdmltest.u-ga.fr/item/1091543054/