G2TT
来源类型Discussion paper
规范类型论文
来源IDDP16285
DP16285 Exploiting Symmetry in High-Dimensional Dynamic Programming
Mahdi Ebrahimi Kahou; Jesus Fernandez-Villaverde; Jesse Perla; Arnav Sood
发表日期2021-06-23
出版年2021
语种英语
摘要We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The ``curse of dimensionality'' is avoided due to four complementary techniques: (1) exploiting symmetry in the approximate law of motion and the value function; (2) constructing a concentration of measure to calculate high-dimensional expectations using a single Monte Carlo draw from the distribution of idiosyncratic shocks; (3) sampling methods to ensure the model fits along manifolds of interest; and (4) selecting the most generalizable over-parameterized deep learning approximation without calculating the stationary distribution or applying a transversality condition. As an application, we solve a global solution of a multi-firm version of the classic Lucas and Prescott (1971) model of ``investment under uncertainty.'' First, we compare the solution against a linear-quadratic Gaussian version for validation and benchmarking. Next, we solve nonlinear versions with aggregate shocks. Finally, we describe how our approach applies to a large class of models in economics.
主题Monetary Economics and Fluctuations
关键词Machine learning Dynamic programming
URLhttps://cepr.org/publications/dp16285
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/545250
推荐引用方式
GB/T 7714
Mahdi Ebrahimi Kahou,Jesus Fernandez-Villaverde,Jesse Perla,et al. DP16285 Exploiting Symmetry in High-Dimensional Dynamic Programming. 2021.
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