Deep Learning

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.

Improving External Validity of Machine Learning, Reduced Form, and Structural Macroeconomic Models using Panel Data

We show that adding countries as a panel dimension to macroeconomic data can statistically significantly improve external validity of structural and reduced form models, as well as allow machine learning methods to outperform these and other macroeconomic forecasting models.