Essays in Macroeconomics

Abstract

This dissertation has three independent essays. The first essay "Housing Illiquidity, AssetPrices, and the Amplification of Macroeconomic Shocks" studies asset pricing and labor market dynamics when houses are modeled as a component of wealth and a consumption good. The stochastic discount factor depends on the consumption of nondurables and houses that are subject to trading frictions. Housing liquidity frictions give rise to risk over the relative consumption ratio that drives asset prices. I study this idea quantitatively in a model that features a portfolio choice based on liquidity as well as risk. Following a TFP shock, firms post fewer vacancies. Rising unemployment implies more aggregate and individual risk and, therefore, more risk over the relative consumption ratio, which leads to higher discounts and rising unemployment. This parsimonious mechanism accounts for both the responsiveness of individual consumption to unemployment shocks and the countercyclical market price of risk, and is quantitatively important. After calibrating the model to match the salient features of the wealth distribution, I find that risk over the relative consumption ratio amplifies output fluctuations by 18% and unemployment volatility by 200%, while welfare losses are mostly borne by asset-poor households. The second essay \Wealth and Hours" studies the relation between household wealth and their labor supply decisions. In the United States, market hours worked are approximatelyat across the wealth distribution. Accounting for this phenomenon is a standing challengefor standard heterogeneous-agent macro models. In these models, wealthier households consume more, enjoy more leisure, and work less. The paper proposes a theory that generates the cross-sectional wealth-hours relation as in the data. This theory is quantifieded 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. As external validation, the paper shows that the model produces expenditure patterns that are consistent with the data, as well as realistic \quality Engel curves." The third essay "Machine Learning Projection Method for Macro-Finance Models" develops a global simulation-based solution method to solve large states space macro-finance models using machine learning. The method uses an artificial neural network (ANN) to approximate the expectations in the optimality conditions in the spirit of the parameterized expectations algorithm (PEA). Because this method can process the entire information set at once, it is easily scalable to handle models with large state spaces that are highly collinear. The paper demonstrates these computational gains in two applications. First, the paper extends the optimal government debt problem studied by Faraglia et al. (2019) to ten maturities and finds that, when borrowing and lending constraints are tight, the optimal policy prescribes an active role for the medium-term maturities. Second, the paper reassesses the resolution of the international business cycle puzzles in Kehoe and Perri (2002). This paper shows that extending their two-country framework to three countries, namely US Europe and China, can change the risk-sharing properties of the economy significantly.