A Machine Learning Projection Method for Macro-Finance Models


Vytautas Valaitis, Alessandro T. Villa

April 30, 2024

Federal Reserve Research: Chicago

We use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic simulation, it is likely to suffer from multicollinearity. We show that a neural network‐based expectations algorithm can deal efficiently with multicollinearity by extending the optimal debt management problem studied by Faraglia, Marcet, Oikonomou, and Scott (2019) to four maturities. We find that the optimal policy prescribes an active role for the newly added medium‐term maturities, 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 during recessions.

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