Deep learning for solving dynamic economic models

Lilia Maliar

doi:10.1016/j.jmoneco.2021.07.004

Deep learning for solving dynamic economic models

2021-09-01

23

PRE

AI

DOI：10.1016/j.jmoneco.2021.07.004

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Abstract

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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 fun-damental objects of economic dynamics - lifetime reward functions, Bellman equations and Euler equations. We estimate the decision functions on simulated data using a stochas-tic gradient descent method. We introduce an all-in-one integration operator that facil-itates 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. (c) 2021 Elsevier B.V. All rights reserved.

Keywords:

Artificial intelligence

Machine learning

Deep learning

Neural network

Stochastic gradient

Dynamic models

Model reduction

Dynamic programming

Bellman equation

Euler equation

Value functio

Deep learning for solving dynamic economic models

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