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  3. 2025
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  5. QE Session: Computational Methods for Challenging Macroeconomic Models
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QE Session: Computational Methods for Challenging Macroeconomic Models

Paper Session

Friday, Jan. 3, 2025 10:15 AM - 12:15 PM (PST)

Hilton San Francisco Union Square, Union Square 14
Hosted By: Econometric Society
  • Chair: James D. Hamilton, University of California-San Diego

Spooky Boundaries at a Distance: Exploring Transversality and Stability with Deep Learning

Mahdi Ebrahimi Kahou
,
University of British Columbia
Jesus Fernandez-Villaverde
,
University of Pennsylvania
Sebastian Gomez-Cardona
,
University of British Columbia
Jesse Perla
,
University of British Columbia
Jan Rosa
,
University of British Columbia

Abstract

In the long run, we are all dead. Nonetheless, even when investigating short-run dynamics, models require boundary conditions on long-run, forward-looking behavior (e.g., transversality and no-bubble conditions). In this paper, we show how deep learning approximations can automatically fulfill these conditions despite not directly calculating the steady-state, balanced growth path, or ergodic distribution. The main implication is that we can solve for transition dynamics with forward-looking agents, confident that long-run boundary conditions will implicitly discipline the short-run decisions, even converging towards the correct equilibria in cases with steady-state multiplicity. While this paper analyzes benchmarks such as the neoclassical growth model, the results suggest deep learning may let us calculate accurate transition dynamics with high-dimensional state spaces, and without directly solving for long-run behavior.

The Power of Open-Mouth Policies

Vadym Lepetyuk
,
Bank of Canada
Lilia Maliar
,
CUNY-Graduate Center
Serguei Maliar
,
Santa Clara University
John B. Taylor
,
Stanford University

Abstract

Central banks announcements about future monetary policy make economic agents to react before
the announced policy takes place. We evaluate the anticipation effects of such announcements in the
context of a realistic dynamic economic model of central banking. In our experiments, we consider tem-
porary and permanent anticipated changes in policy rules including changes in inflation target, natural
rate of interest and Taylor-rule coefficients, as well as anticipated switches from inflation targeting to
price-level targeting and average inflation targeting. We show that the studied nonrecurrent news shocks
about future policies have sizable anticipation e¤ects on the economy. Our methodological contribution
is to develop a novel perturbation-based framework for constructing nonstationary solutions to economic
models with nonrecurrent news shocks.

Thinking Big: Determinacy and Large-Scale Solutions in the Sequence Space

Matthew Rognlie
,
Northwestern University
Ludwig Straub
,
Harvard University

Abstract

We prove a structure theorem for sequence-space Jacobians of stationary models, showing that they have a “quasi-Toeplitz” form. This result has three major computational benefits: it yields a simple test for existence and uniqueness of solutions, drastically reduces the error from truncating the time horizon, and allows the solution of extremely large sequence-space systems. We apply the latter in a 180-country model of fiscal policy propagation on a trade network, with heterogeneous agents in each country, and show that this can be solved almost instantly, despite having a state space with dimension 2 million.

Deep Learning for Search and Matching Models

Jonathan Payne
,
Princeton University
Adam Rebei
,
Stanford University
Yucheng Yang
,
University of Zurich

Abstract

We develop a new method for characterizing global solutions to search and matching models with aggregate shocks and heterogeneous agents. We formulate general equilibrium as a high-dimensional partial differential equation (PDE) with the distribution as a state variable. Solving this problem has previously been intractable because the distribution impacts agent decisions through the matching mechanism rather than through aggregate prices. We overcome these challenges by developing a new deep learning algorithm with efficient sampling in a high-dimensional state space. This allows us to study search markets that are not “block recursive”. In applications to labor search models, we show that distribution feedback plays a more important role when aggregate shocks have an asymmetric impact across agents. Business cycles have a “cleansing” effect by amplifying positive assortative matching in recessions, and the magnitude of the countercyclicality depends on the bargaining process between workers and firms.
JEL Classifications
  • C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling