  Many scientifically well-motivated statistical models in natural, engineering
and environmental sciences are specified through a generative process, but in
some cases it may not be possible to write down a likelihood for these models
analytically. Approximate Bayesian computation (ABC) methods, which allow
Bayesian inference in these situations, are typically computationally
intensive. Recently, computationally attractive empirical likelihood based ABC
methods have been suggested in the literature. These methods heavily rely on
the availability of a set of suitable analytically tractable estimating
equations. We propose an easy-to-use empirical likelihood ABC method, where the
only inputs required are a choice of summary statistic, it's observed value,
and the ability to simulate summary statistics for any parameter value under
the model. It is shown that the posterior obtained using the proposed method is
consistent, and its performance is explored using various examples.
