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Conditional Probabilistic Population Forecasting
Author(s) -
Sanderson Warren C.,
Scherbov Sergei,
O'Neill Brian C.,
Lutz Wolfgang
Publication year - 2004
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/j.1751-5823.2004.tb00230.x
Subject(s) - probabilistic logic , probabilistic forecasting , context (archaeology) , econometrics , population , conditional probability , computer science , consensus forecast , jump , economics , statistics , artificial intelligence , mathematics , geography , physics , demography , archaeology , quantum mechanics , sociology
Summary 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.