Fabio Stohler

Generative Economic Modeling

Abstract  |  Draft upon request

We introduce a novel approach for solving quantitative economic models: generative economic modeling. Our method combines neural networks with conventional solution techniques. Specifically, we train neural networks on simplified versions of an economic model to generate approximations of the full model’s dynamic behavior. By relying on these less complex satellite models, we circumvent the curse of dimensionality and are able to employ well-established numerical methods. We demonstrate our approach on models with nonlinear dynamics and heterogeneous agents. Finally, we apply generative economic modeling to solve a high-dimensional HANK model with financial frictions.

Information Content of Option Prices

Wealthy Seniors and the Equity Premium Puzzle

The Volatility Costs of Public Debt