ANN: SimulatedNeuralMoments

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.

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An example showing how to use the methods with real data has been added. It shows how to estimate a simple stochastic volatility model. There is an explanation at https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/examples/SV/README.md

Everything runs fine with Julia 1.5.3 or 1.6 beta1.

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