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Neural Network Learning for Nonlinear Economies

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  • Julian Ashwin
  • Paul Beaudry
  • Martin Ellison

Abstract

Neural networks offer a promising tool for the analysis of nonlinear economies. In this paper, we derive conditions for the global 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 than 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, such as when there is a zero lower bound on the nominal interest rate.

Suggested Citation

  • Julian Ashwin & Paul Beaudry & Martin Ellison, 2024. "Neural Network Learning for Nonlinear Economies," NBER Working Papers 32807, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:32807
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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E19 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Other
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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