Template-Type: ReDIF-Paper 1.0
Author-Name: Hanno Kase
Author-X-Name-First: Hanno
Author-X-Name-Last: Kase
Author-Name: Matthias Rottner
Author-X-Name-First: Matthias
Author-X-Name-Last: Rottner
Author-Name: Fabio Stohler
Author-X-Name-First: Fabio
Author-X-Name-Last: Stohler
Title: Generative economic modeling
Abstract: 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 the economic model to approximate the complete model's dynamic behavior. Relying on these less complex submodels circumvents the curse of dimensionality, allowing the use of well-established numerical methods. We demonstrate our approach across settings with analytical characterizations, nonlinear dynamics, and heterogeneous agents, employing asset pricing and business cycle models. Finally, we solve a high-dimensional HANK model with an occasionally binding financial friction to highlight how aggregate risk amplifies the precautionary motive.
Creation-Date: 2025-12
File-URL: https://www.bis.org/publ/work1312.pdf
File-Format: Application/pdf
File-Function: Full PDF document
File-URL: https://www.bis.org/publ/work1312.htm
File-Format: text/html
Number: 1312
Keywords: machine learning, neural networks, nonlinearities, heterogeneous agents
Classification-JEL: C11, C45, D31, E32, E52
Handle: RePEc:bis:biswps:1312