DEEP EQUILIBRIUM NETS
提出深度均衡网络方法，用无监督训练的神经网络直接逼近经济模型的理性预期均衡函数，能高效求解含大量异质性、不确定性和偶尔约束的模型，对宏观经济学和计算经济学研究者有用。
Abstract We introduce deep equilibrium nets (DEQNs)—a deep learning‐based method to compute approximate functional rational expectations equilibria of economic models featuring a significant amount of heterogeneity, uncertainty, and occasionally binding constraints. DEQNs are neural networks trained in an unsupervised fashion to satisfy all equilibrium conditions along simulated paths of the economy. Since DEQNs approximate the equilibrium functions directly, simulating the economy is computationally cheap, and training data can be generated at virtually zero cost. We demonstrate that DEQNs can accurately solve economically relevant models by applying them to two challenging life‐cycle models and a Bewley‐style model with aggregate risk.