Amortized Neural Networks for Agent-Based Model Forecasting

Denis Koshelev, Alexey Ponomarenko and Sergei Seleznev
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Denis Koshelev: Bank of Russia, Russian Federation

No wps115, Bank of Russia Working Paper Series from Bank of Russia

Abstract: In this paper, we propose a new procedure for unconditional and conditional forecasting in agent-based models. The proposed algorithm is based on the application of amortized neural networks and consists of two steps. The first step simulates artificial datasets from the model. In the second step, a neural network is trained to predict the future values of the variables using the history of observations. The main advantage of the proposed algorithm is its speed. This is due to the fact that, after the training procedure, it can be used to yield predictions for almost any data without additional simulations or the re-estimation of the neural network.

Keywords: agent-based models; amortized simulation-based inference; Bayesian models; forecasting; neural networks. (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 C45 C53 C63 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2023-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hme and nep-net
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Working Paper: Amortized neural networks for agent-based model forecasting (2023)
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