Neural network learning for nonlinear economies

• 成果类型: Article
• 署名作者: Ashwin, Julian; Beaudry, Paul; Ellison, Martin
• 署名单位: Maastricht University; University of British Columbia; University of Oxford; Centre for Economic Policy Research - UK
• 刊物名称: JOURNAL OF MONETARY ECONOMICS
• ISSN/ISSBN: 0304-3932
• DOI: 10.1016/j.jmoneco.2024.103723
• 发表日期: 2025
• 关键词: Inflation targeting; Machine Learning; Neural Networks; zero lower bound

摘要

Neural networks offer a promising tool for the analysis of nonlinear economies. In this paper, we derive conditions for the stability of nonlinear rational expectations equilibria under neural network learning. We demonstrate the applicability of the conditions in analytical and numerical examples where the nonlinearity is caused by monetary policy targeting a range, rather a specific value, of inflation. If shock persistence is high or there is inertia in the structure of the economy, then the only rational expectations equilibria that are learnable may involve inflation spending long periods outside its target range. Neural network learning is also useful for solving and selecting between multiple equilibria and steady states in other settings, as when there is a zero lower bound on the nominal interest rate.