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Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro

Author

Listed:
  • Mahdi Ebrahimi Kahou

    (Bowdoin College)

  • Jesus Fernandez-Villaverde

    (University of Pennsylvania, NBER, and CEPR)

  • Sebastian Gomez-Cardona

    (Morningstar)

  • Jesse Perla

    (University of British Columbia)

  • Jan Rosa

    (University of British Columbia)

Abstract

In the long run, we are all dead. Nonetheless, when studying the short-run dynamics of economic models, it is crucial to consider boundary conditions that govern long-run, forwardlooking behavior, such as transversality conditions. We demonstrate that machine learning (ML) can automatically satisfy these conditions due to its inherent inductive bias toward finding flat solutions to functional equations. This characteristic enables ML algorithms to solve for transition dynamics, ensuring that long-run boundary conditions are approximately met. ML can even select the correct equilibria in cases of steady-state multiplicity. Additionally, the inductive bias provides a foundation for modeling forward-looking behavioral agents with self-consistent expectations.

Suggested Citation

  • Mahdi Ebrahimi Kahou & Jesus Fernandez-Villaverde & Sebastian Gomez-Cardona & Jesse Perla & Jan Rosa, 2024. "Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro," PIER Working Paper Archive 24-019, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:24-019
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    More about this item

    Keywords

    Machine learning; inductive bias; rational expectations; transitional dynamics; transversality; behavioral macroeconomics;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models

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