Generative economic modeling

Hanno Kase, Matthias Rottner and Fabio Stohler

No 1312, BIS Working Papers from Bank for International Settlements

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.

Keywords: machine learning; neural networks; nonlinearities; heterogeneous agents (search for similar items in EconPapers)
JEL-codes: C11 C45 D31 E32 E52 (search for similar items in EconPapers)
Date: 2025-12
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Persistent link: https://EconPapers.repec.org/RePEc:bis:biswps:1312

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