Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro / Mahdi E. Kahou, Jesús Fernández-Villaverde, Sebastian Gomez-Cardona, Jesse Perla, Jan Rosa.

Format:

Book

Author/Creator:

Kahou, Mahdi E.

Contributor:

Fernández-Villaverde, Jesús.

Gomez-Cardona, Sebastian.

Perla, Jesse.

Rosa, Jan.

National Bureau of Economic Research.

Series:

Working Paper Series (National Bureau of Economic Research) no. w32850.

NBER working paper series no. w32850

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2024.

Summary:

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.

Notes:

August 2024.

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