Tutorial: Approximate Bayesian computation (ABC)

Brendan McCabe, Economics, Finance and Accounting, University of Liverpool, UK

This tutorial looks at how to do Bayesian Inference when it is too difficult to calculate the true likelihood and hence the exact posterior. (This is a Bayesian version of frequentist ‘indirect inference’ really.) We use model based summary statistics to match simulations form the assumed (difficult) model with the actual data at hand. Conventional approaches to ABC emphasize the role of parameter estimation but, in time series problems, forecasting is often the focus of attention and so it is to this dimension we direct our efforts. The role of Bayesian consistency is highlighted.

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