Synthetic Likelihood in Misspecified Models: Consequences and Corrections

Abstract: We analyse the behaviour of the synthetic likelihood (SL) method when the model generating the simulated data differs from the actual data generating process. One of the most common methods to obtain SL-based inferences is via the Bayesian posterior distribution, with this method often referred to as Bayesian synthetic likelihood (BSL). We demonstrate that when the model is misspecified, the BSL posterior can be poorly behaved, placing significant posterior mass on values of the model parameters that do not represent the true features observed in the data. Theoretical results demonstrate that in misspecified models the BSL posterior can display a wide range of behaviours depending on the level of model misspecification, including being asymptotically non-Gaussian. Our results suggest that a recently proposed robust BSL approach can ameliorate this behavior and deliver reliable posterior inference under model misspecification. We document all theoretical results using a simple running example.