Inference Using Simulated Neural Moments

Abstract

This paper deals with Laplace type methods used with moment-based, simulation-based, econometric estimators. It shows that confidence intervals based upon quantiles of a tuned MCMC chain may have coverage which is far from the nominal level. It discusses how neural networks may be used to easily and automatically reduce the dimension of an initial set of moments to the minimum number of moments needed to maintain identification. When estimation and inference is based on the neural moments, which are the result of filtering moments through a trained neural net, confidence intervals have correct coverage in almost all cases, and departures from correct coverage are small.