Dr Vytautas Valaitis

In the U.S., market hours worked are approximately flat across the wealth distribution. Accounting for this phenomenon is a standing challenge for standard heterogeneous-agent macro models. In these models, wealthier households consume more and work fewer hours. We propose a theory that generates the cross-sectional wealth-hours relation as in the data. We quantify this theory in a heterogeneous-agent incomplete-markets model with three key features: a quality choice in consumption, non-homothetic preferences, and a multi-sector production structure. We show that the model produces consumption expenditure patterns consistent with the data and realistic " quality Engel curves " .

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.