In the United States, 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, enjoy more leisure, and work less. We propose a theory that generates the cross-sectional wealth-hours relation as in the data. We quantify this theory in the context of a new general-equilibrium 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 expenditure patterns that are consistent with the data, as well as 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 et al. (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.

We examine the role of government investment in defense capital as a deterrence tool. Using an optimal fiscal policy framework with endogenous disaster risk, we allow for an endogenous determination of geopolitical risk and defense capacity, which we discipline using the Geopolitical Risk Index. We show both analytically and quantitatively that financing defense primarily through debt, rather than taxation, is optimal. Debt issuance mitigates present tax distortions but exacerbates them in the future, especially in wartime. However, since additional defense capital deters future wars, the expected tax distortions decline as well, making debt financing a welfare-improving strategy. Quantitatively, the optimal defense financing in the presence of heightened risk involves a twice higher share of debt and backloading of tax distortions compared to other types of government spending.

Can governments use real bonds such as Treasury Inflation-Protected Securities (TIPS) to tame inflation? We propose a novel framework of optimal debt management

with sticky prices and a government issuing nominal and real state-uncontingent bonds. A government debt portfolio with both nominal and real bonds helps completing markets unless the monetary policy stance renders them perfect substitutes. Under Full Commitment, the government borrows with nominal debt and accumulates real assets, to be able to use inflation to smooth taxes. With No Commitment, the government portfolio favors real bonds to strategically prevent future governments from monetizing debt ex-post. Quantitatively, our model with No Commitment is consistent with the small and persistent TIPS share in U.S. data. A higher TIPS share mitigates the commitment friction, and e
ectively curbs inflation.

How does housing illiquidity affect household risk-aversion and saving behavior? This paper shows that when housing services provide utility, the risk over the relative consumption ratio of nondurables and houses determines household relative risk aversion and drives asset prices. I show in a calibrated heterogeneous-agents model thataccounting for the relative consumption risk (i) helps to explain challenging asset pric-ing facts, such as the countercyclical market price of risk and a stable risk-free rate,(ii) greatly amplifies the business cycle fluctuations, (iii) illuminates the new source ofbusiness cycle costs (iv) helps to understand the endogenous variation in uncertainty.