Estimating Nonlinear Heterogeneous Agents Models with Neural Networks

55 Pages Posted: 17 Jun 2022

See all articles by Hanno Kase

Hanno Kase

University of Minnesota

Leonardo Melosi

Federal Reserve Bank of Chicago

Matthias Rottner

Deutsche Bundesbank - Research Centre

Date Written: June 14, 2022

Abstract

Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While relaxing these assumptions is well-known to give rise to complicated curse-of-dimensionality problems, it is often unclear how seriously these simplifications distort the dynamics and predictions of the model. We leverage the recent advancements in machine learning to develop a solution and estimation method based on neural networks that does not require these strong assumptions. We apply our method to a nonlinear Heterogeneous Agents New Keynesian (HANK) model with a zero lower bound (ZLB) constraint for the nominal interest rate to show that the method is much more efficient than existing global solution methods and that the estimation converges to the true parameter values. Further, this application sheds light on how effectively our method is capable to simultaneously deal with a large number of state variables and parameters, nonlinear dynamics, heterogeneity as well as aggregate uncertainty.

Keywords: Machine learning, neural networks, Bayesian estimation, global solution, heterogeneous agents, nonlinearities, aggregate uncertainty, HANK model, zero lower bound

JEL Classification: C11, C45, D31, E32, E52

Suggested Citation

Kase, Hanno and Melosi, Leonardo and Rottner, Matthias, Estimating Nonlinear Heterogeneous Agents Models with Neural Networks (June 14, 2022). FRB of Chicago Working Paper No. 2022-26, Available at SSRN: https://ssrn.com/abstract=4138711 or http://dx.doi.org/10.2139/ssrn.4138711

Hanno Kase

University of Minnesota ( email )

10 University Avenue
Duluth, MN 55810
United States

Leonardo Melosi (Contact Author)

Federal Reserve Bank of Chicago ( email )

230 South LaSalle Street
Chicago, IL 60604
United States

Matthias Rottner

Deutsche Bundesbank - Research Centre ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

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