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

Author

Listed:
  • Mahdi Ebrahimi Kahou
  • Jesús Fernández-Villaverde
  • Sebastián Gómez-Cardona
  • Jesse Perla
  • Jan Rosa

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, forward-looking 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 behavioural agents with self-consistent expectations.

Suggested Citation

  • Mahdi Ebrahimi Kahou & Jesús Fernández-Villaverde & Sebastián Gómez-Cardona & Jesse Perla & Jan Rosa, 2024. "Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro," CESifo Working Paper Series 11292, CESifo.
    • Handle: RePEc:ces:ceswps:_11292
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    References listed on IDEAS

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    1. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, vol. 48(5), pages 1305-1311, July.
    2. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
    3. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
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    Cited by:

    1. Jesús Fernández-Villaverde & Kenneth T. Gillingham & Simon Scheidegger, 2024. "Climate Change through the Lens of Macroeconomic Modeling," PIER Working Paper Archive 24-024, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Jésus Fernández-Villaverde & Galo Nuño & Jesse Perla & Jesús Fernández-Villaverde, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," CESifo Working Paper Series 11448, CESifo.

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    More about this item

    Keywords

    machine learning; inductive bias; rational expectations; transitional dynamics; transversality; behavioural macroeconomics;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

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