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Activity Number: 666 - Bayesian Penalized Regression Models
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #330956
Title: Approximate Bayesian Forecasting
Author(s): Brendan McCabe*
Companies: University of Liverpool
Keywords: Bayesian Prediction; Likelihood free Methods; Proper Scoring Rules; Particle Filtering; Jump-diffusion Models

Approximate Bayesian Computation has become increasingly prominent asa method for conducting parameter inference in a range of challenging statistical prob- lems, 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 'ap- proximate 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 gen- erating 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 m

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