Simulation-based Bayesian Estimation of Dynamic Models

Abstract

This paper introduces a simulation-based deep learning approach to Bayesian inference to the economic literature. This technique allows for the estimation of a posterior even when the likelihood is intractable and works even with data that has a latent time structure which is ubiquitous in macroeconomic structural modeling. This approach allows value function iteration and other methods that don’t return a likelihood function to be estimated in a Bayesian fashion for likely the first time. I demonstrate the approach on two toy models using value function iteration model and also a 10 parameter HANK model solved via Reiter and time iteration.

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