A Machine Learning Projection Method for Macro-Finance Models
49 Pages Posted: 27 Jul 2018 Last revised: 22 Nov 2021
There are 2 versions of this paper
A Machine Learning Projection Method for Macro-Finance Models
A Machine Learning Projection Method for Macro-Finance Models
Date Written: November 18, 2021
Abstract
This paper develops a simulation-based solution method to solve large state space macro-finance
models using machine learning. We use a neural network (NN) to approximate the
expectations in the optimality conditions in the spirit of the stochastic parameterized expectations
algorithm (PEA). Because our method can process the entire information set at once, it is
scalable and can handle models with large and multicollinear state spaces. We demonstrate the
computational gains by extending the optimal government debt management problem studied
by Faraglia et al. (2019) from two to three maturities. We find that the optimal policy prescribes
an active role for the newly added medium-term maturity, enabling the planner to raise financial
income without increasing its total borrowing in response to expenditure shocks. Through this
mechanism the government effectively subsidizes the private sector in recessions, resulting in a
welfare gain of 2.38% when the number of available maturities increases from two to three.
Keywords: Machine Learning, Incomplete Markets, Projection Methods, Optimal Fiscal Policy, Maturity Management.
JEL Classification: C63, D52, E32, E37, E62, G12.
Suggested Citation: Suggested Citation