Frazier, David T, Maneesoonthorn, Worapree, Martin, Gael M and McCabe, Brendan PM ORCID: 0000-0002-9731-1766
(2019)
Approximate Bayesian forecasting.
INTERNATIONAL JOURNAL OF FORECASTING, 35 (2).
pp. 521-539.
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Abstract
Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably those characterized by an intractable likelihood function. In this paper, we focus on the use of ABC not as a tool for parametric inference, but as a means of generating probabilistic forecasts; or for conducting what we refer to as `approximate Bayesian forecasting'. The four key issues explored are: i) the link between the theoretical behavior of the ABC posterior and that of the ABC-based predictive; ii) the use of proper scoring rules to measure the (potential) loss of forecast accuracy when using an approximate rather than an exact predictive; iii) the performance of approximate Bayesian forecasting in state space models; and iv) the use of forecasting criteria to inform the selection of ABC summaries in empirical settings. The primary finding of the paper is that ABC can provide a computationally efficient means of generating probabilistic forecasts that are nearly identical to those produced by the exact predictive, and in a fraction of the time required to produce predictions via an exact method. y identical to those produced by the exact predictive, and in a fraction of the time required to produce predictions via an exact method.
Item Type: | Article |
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Uncontrolled Keywords: | Bayesian prediction, Likelihood-free methods, Predictive merging, Proper scoring rules, Particle filtering, Jump-diffusion models |
Depositing User: | Symplectic Admin |
Date Deposited: | 19 Sep 2018 09:49 |
Last Modified: | 19 Jan 2023 01:17 |
DOI: | 10.1016/j.ijforecast.2018.08.003 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3026441 |