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The effectiveness of imprecise probability forecasts
Author(s) -
George Benson P.,
Whitcomb Kathleen M.
Publication year - 1993
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980120207
Subject(s) - empirical probability , probability distribution , imprecise probability , principle of maximum entropy , law of total probability , statistics , mathematics , entropy (arrow of time) , coverage probability , probability estimation , conditional probability , probability and statistics , econometrics , computer science , posterior probability , bayesian probability , confidence interval , artificial intelligence , physics , quantum mechanics
In this paper we investigate the feasibility of algorithmically deriving precise probability forecasts from imprecise forecasts. We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability intervals with second‐order probability distributions. The minimum cross‐entropy (MCE) principle is applied to the former to derive precise (i.e. additive) probabilities; expectation (EX) is used to derive precise probabilities in the latter case. Probability intervals that were constructed without second‐order probabilities tended to be narrower than and contained in those that were amplified by second‐order probabilities. Evidence that this narrowness is due to motivational bias is presented. Analysis of forecasters' mean Probability Scores for the derived precise probabilities indicates that it is possible to derive precise forecasts whose external correspondence is as good as directly assessed precise probability forecasts. The forecasts of the EX method, however, are more like the directly assessed precise forecasts than those of the MCE method.