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Developments in Macroeconomics and Finance

Paper Session

Sunday, Jan. 8, 2023 10:15 AM - 12:15 PM (CST)

Hilton Riverside, Grand Salon C Sec 18
Hosted By: Society for Nonlinear Dynamics and Econometrics
  • Chair: Tatevik Sekhposyan, Texas A&M University

Estimating Nonlinear Heterogeneous Agents Models with Neural Networks

Hanno Kase
University of Minnesota
Leonardo Melosi
Federal Reserve Bank of Chicago
Matthias Rottner
Deutsche Bundesbank


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.

The Unattractiveness of Indeterminate Dynamic Equilibria

Julian Ashwin
London Business School
Paul Beaudry
University of British Columbia and Bank of Canada
Martin Ellison
University of Oxford


Macroeconomic forces that generate multiple equilibria often support locally-indeterminate dynamic equilibria in which a continuum of perfect foresight paths converge towards the same steady state. The set of rational expectations equilibria (REE) in such environments can be very large, although the relevance of many of them has been questioned on the basis that they may not be learnable. In this paper we document the existence of a learnable REE in such situations. However, we show that the dynamics of this learnable REE do not resemble perturbations around any of the convergent perfect foresight paths. Instead, the learnable REE treats the locally-indeterminate steady state as unstable, in contrast to it resembling a stable attractor under perfect foresight.

Talking Over Time: Dynamic Central Bank Communication

Laura Gati
European Central Bank


This paper studies the optimal dynamic communication strategy of central banks using a Bayesian persuasion game framework. In a dynamic environment, financial market participants and the general public have misaligned interests because the present and future have different relevance in their optimization problems, leading to a novel tradeoff for the monetary authority. Compared to the static benchmark, I show that the central bank’s optimal dynamic communication policy should put a higher weight on talking about the present state than the future. In addition, the central bank should strategically send more noisy signals than in the static benchmark.

A Bewley Model of Asset Pricing

Dong-Hyun Ahn
Seoul National University
Hwagyun Kim
Texas A&M University
Eunseong Ma
Louisiana State University


Capital adjustment costs and stochastic capital depreciation exist as additional frictions. Our model quantitatively accounts for economic data, including consumption and income distributions, and excels at producing the key properties of asset prices. Limited risk-sharing with costly capital adjustment lies at the center of explaining the cross-section of stock returns. Our model supports empirical asset pricing models employing firm-specific investment and profitability as factors. Relaxing the assumption of an incomplete market weakens or reverses quantitative results.

Ed Herbst
Federal Reserve Board
Thomas Lubik
Federal Reserve Bank of Richmond
Tatjana Dahlhaus
Bank of Canada
Jack Favilukis
University of British Columbia
JEL Classifications
  • E1 - General Aggregative Models
  • G1 - Asset Markets and Pricing