Approximate Bayesian forecasting
David Frazier (),
Worapree Maneesoonthorn (),
Gael Martin () and
No 2/18, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
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 forecast accuracy to inform the selection of ABC summaries in empirical settings. The primary finding of the paper is that ABC can provide a computationally ecient 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.
Keywords: Bayesian prediction; likelihood-free methods; predictive merging; proper scoring rules; particle filtering; jump-diffusion models. (search for similar items in EconPapers)
JEL-codes: C11 C53 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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Journal Article: Approximate Bayesian forecasting (2019)
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