  We present a procedure to diagnose model misspecification in situations where
inference is performed using approximate Bayesian computation. We demonstrate
theoretically, and empirically that this procedure can consistently detect the
presence of model misspecification. Our examples demonstrates that this
approach delivers good finite-sample performance and is computational less
onerous than existing approaches, all of which require re-running the inference
algorithm. An empirical application to modelling exchange rate log returns
using a g-and-k distribution completes the paper.
