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

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  • Ebrahimi Kahou, Mahdi
  • Fernández-Villaverde, Jesús
  • Gomez Cardona, Sebastian
  • Perla, Jesse
  • Rosa, Jan

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 behavioral agents with self-consistent expectations.

Suggested Citation

  • Ebrahimi Kahou, Mahdi & Fernández-Villaverde, Jesús & Gomez Cardona, Sebastian & Perla, Jesse & Rosa, Jan, 2024. "Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro," CEPR Discussion Papers 19386, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:19386
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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 & Galo Nuño & Jesse Perla, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," NBER Working Papers 33117, National Bureau of Economic Research, Inc.
    2. Jésus Fernández-Villaverde & Kenneth T. Gillingham & Simon Scheidegger & Jesús Fernández-Villaverde & Kenneth Gillingham, 2024. "Climate Change through the Lens of Macroeconomic Modeling," CESifo Working Paper Series 11346, CESifo.

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    More about this item

    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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