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Deep learning for solving dynamic economic models
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DOI：10.1016/j.jmoneco.2021.07.004.png)
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摘要
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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.
Keyword:
Artificial intelligence
Machine learning
Deep learning
Neural network
Stochastic gradient
Dynamic models
Model reduction
Dynamic programming
Bellman equation
Euler equation
Value functio
期刊
IF:
4.1
论文数:
3.2K
被引数:
1.1W
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