Prof. Brendan McCabe
University of Liverpool, UK
Title: Approximate Bayesian Forecasting
Abstract:
Approximate Bayesian Computation (ABC) has become increasingly prominent as
a 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
method. Some Stochastic Volatility models are discussed.
Biography: