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.


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

Everything runs fine with Julia 1.5.3 or 1.6 beta1.

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A new example that estimates a small DSGE model has been added: SimulatedNeuralMoments.jl/examples/DSGE at main · mcreel/SimulatedNeuralMoments.jl · GitHub

This makes use of the SolveDSGE.jl package, which I find to be very nice and straightforward to use (and fast!) for solving and simulating DSGE models.

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