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Simulation-based Bayesian Estimation of Dynamic Models

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.

Imputing Data with Transformers

This paper uses a transformer neural network model to perform imputation of missing data. The method returns a distribution which allows for easy marginalization which can allow for statistically efficient analysis when combined with a model for inference.

Simulation Based Maximum Likelihood Estimation

This paper introduces a novel, fast, and robust method to perform maximum likelihood estimation without using a likelihood function and only relying on samples generated from the model.

Variational Inference for Fast Bayesian DSGE Estimation

This paper demonstrates how to use variational inference as well as a flexible variational family that allows fast estimation of DSGE models, which roughly reduces computation time by a factor of 100 compared to Markov chain Monte Carlo.