Conditional Probabilistic Population Forecasting
Book, Other literature type
Sanderson, Warren C.
O'Neill, Brian C.
- Publisher: International Statistical Institute
Scenario analysis | Forecasting | Probabilistic forecasting | Population forecasting | Scenarios
Since policy-makers often prefer to think in terms of alternative scenarios, the question
has arisen as to whether it is possible to make conditional population forecasts in a
probabilistic context. This paper shows that it is both possible and useful to make these
forecasts. We do this with two different kinds of examples. The first is the probabilistic
analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is
essential for policy-makers because it allows them to answer 'what if' type questions
properly when outcomes are uncertain. The second is a new category that we call 'future
jump-off date forecasts'. Future jump-off date forecasts are valuable because they show
policy-makers the likelihood that crucial features of today's forecasts will also be present
in forecasts made in the future.