Solving economic models with neural networks without backpropagation

Julien Pascal ()

No 196, BCL working papers from Central Bank of Luxembourg

Abstract: This paper presents a novel method to solve high-dimensional economic models using neural networks when the exact calculation of the gradient by backpropagation is impractical or inapplicable. This method relies on the gradient-free bias-corrected Monte Carlo (bc-MC) operator, which constitutes, under certain conditions, an asymptotically unbiased estimator of the gradient of the loss function. This method is well-suited for high-dimensional models, as it requires only two evaluations of a residual function to approximate the gradient of the loss function, regardless of the model dimension. I demonstrate that the gradient-free bias-corrected Monte Carlo operator has appealing properties as long as the economic model satisfies Lipschitz continuity. This makes the method particularly attractive in situations involving non-differentiable loss functions. I demonstrate the broad applicability of the gradient-free bc-MC operator by solving large-scale overlapping generations (OLG) models with aggregate uncertainty, including scenarios involving borrowing constraints that introduce non-differentiability in household optimization problems.

Keywords: Dynamic programming; neural networks; machine learning; Monte Carlo; overlapping generations; occasionally binding constraints. (search for similar items in EconPapers)
JEL-codes: C45 C61 C63 C68 E32 E37 (search for similar items in EconPapers)
Pages: 69 pages
Date: 2025-04
New Economics Papers: this item is included in nep-dge
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Persistent link: https://EconPapers.repec.org/RePEc:bcl:bclwop:bclwp196

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