Deep learning for solving dynamic economic models

Lilia Maliar, Serguei Maliar and Pablo Winant

Journal of Monetary Economics, 2021, vol. 122, issue C, 76-101

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. Our deep learning method is tractable in large-scale problems, e.g., Krusell and Smith (1998). We provide a TensorFlow code that accommodates a variety of applications.

Keywords: Artificial intelligence; Machine learning; Deep learning; Neural network; Stochastic gradient; Dynamic models; Model reduction; Dynamic programming; Bellman equation; Euler equation; Value functio (search for similar items in EconPapers)
JEL-codes: C61 C63 C65 C68 C88 E32 E37 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:moneco:v:122:y:2021:i:c:p:76-101

DOI: 10.1016/j.jmoneco.2021.07.004

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