Deep Learning Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models

Zhouzhou Gu, Mathieu Laurière, Sebastian Merkel and Jonathan Payne
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Zhouzhou Gu: Princeton University
Mathieu Laurière: NYU Shanghai, NYU-ECNU Institute of Mathematical Sciences
Sebastian Merkel: University of Exeter
Jonathan Payne: Princeton University

Working Papers from Princeton University. Economics Department.

Abstract: We propose a new global solution algorithm for continuous time heterogeneous agent economies with aggregate shocks. First, we approximate the state space so that equilibrium in the economy can be characterized by one high, but finite, dimensional partial differential equation. Second, we approximate the value function using neural networks and solve the differential equation using deep learning tools. We refer to the solution as an Economic Model Informed Neural Network (EMINN). The main advantage of this technique is that it allows us to find global solutions to high dimensional, non-linear problems. We demonstrate our algorithm by solving two canonical models in the macroeconomics literature: the Aiyagari (1994) model and the Krusell and Smith (1998) model.

Keywords: Heterogeneous agents; computational methods; deep learning; inequality; mean field games; continuous time methods; aggregate shocks; global solution (search for similar items in EconPapers)
JEL-codes: C70 (search for similar items in EconPapers)
Date: 2023-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dge and nep-hme
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Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:2023-19

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