I’m happy to announce that the package SimulatedNeuralMoments is available in the general registry. This is a package for inference methods that can be thought of as approximate Bayesian computing (ABC), with a particular choice of criterion, or as a method of simulated moments (MSM) estimator, using Bayesian tools.
An important feature of the methods is that the statistics used to identify the parameters are filtered through a neural net. This process is automatic and requires no intervention by the user. Monte Carlo evidence has shown, so far, that the methods lead to confidence/credible intervals that have proper coverage, with sample sizes representative of real data.