#2241. Deep learning for solving dynamic economic models.

September 2026publication date
Proposal available till 01-06-2025
4 total number of authors per manuscript6510 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Finance;
Economics and Econometrics;
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Abstract:
We introduce a unified deep learning method that solves dynamic economic models by casting them into nonlinear regression equations. We derive such equations for three fundamental objects of economic dynamics – lifetime reward functions, Bellman equations and Euler equations. We estimate the decision functions on simulated data using a stochastic gradient descent method. We introduce an all-in-one integration operator that facilitates approximation of high-dimensional integrals. We use neural networks to perform model reduction and to handle multicollinearity.
Keywords:
Artificial intelligence; Bellman equation; Deep learning; Dynamic models; Dynamic programming; Euler equation; Machine learning; Model reduction; Neural network; Stochastic gradient; Value functio

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