Machine Learning Projection Method for Macro-Finance Models


This paper develops a global simulation-based solution method to solve large states space macro-finance models using machine learning. We use an artificial neural net- work (ANN) to approximate the expectations in the optimality conditions in the spirit of the parameterized expectations algorithm (PEA). Because our method can process the entire information set at once, it is easily scalable to handle models with large state spaces that are highly collinear. We demonstrate these computational gains in two ap- plications. First, we extend the optimal government debt problem studied by Faraglia et al. (Forthcoming) to ten maturities and we find that, when borrowing and lending constraints are tight, the optimal policy prescribes an active role for the medium-term maturities. Second, we reassess the solution of Kehoe and Perri (2002) for the inter- national business cycle puzzles documented in Backus et al. (1992). We show that extending their two-country framework to three countries (US, Europe, China ) can change the risk-sharing properties of the economy significantly.

Joint with: Vytautas Valaitis, R&R, Quantitative Economics

Date Written: August 6, 2019
Keywords: Machine Learning, Incomplete Markets, Projection Methods, Optimal Fiscal Policy, International Business Cycle.
JEL classification: C63, D52, E32, E37, E62, G12.
Pages: 62