Sequential solution for DSGE models with deep neural networks

Massimo Ferrari Minesso and Carla Frenzel

No 3236, Working Paper Series from European Central Bank

Abstract: This paper develops a sequential deep learning algorithm for solving dynamic stochastic general equilibrium (DSGE) models. The algorithm trains a deep neural network to approximate the model’s policy functions across four progressive phases: steady-state anchoring, exploration around the steady state, simulation on the ergodic set, and Monte Carlo integration of stochastic expectations. Training requires no pre-computed starting approximation: the network initialises from the analytically known steady state and constructs its training data endogenously, resolving the circularity between the training distribution and the solution. A systematic comparison across network architectures shows that shallow, moderately wide networks with an intermediate steady-state penalty consistently deliver the best accuracy at the lowest computational cost. We apply the method to a two-country open-economy model and show that large tariff shocks generate non-linearities that local methods cannot reproduce even at higher orders. JEL Classification: C45, C63, C68, E13, F13

Keywords: deep neural networks; DSGE models; non-linear solution; policy function approximation; solution methods (search for similar items in EconPapers)
Date: 2026-05
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20263236

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