Abstract
This thesis analyzes the effect of COVID-19 shutdown policy and economic support shocks on U.S. macroeconomic indicators. Vector autoregressive (VAR) models are used to estimate impulse response functions for eighteen different variables representing various sectors of the economy. Data from FRED, Oxford University, Indeed, and Yahoo Finance are used to interpret the impacts of COVID-19 shutdown policy stringency and the economic support given during the pandemic in the United States. In addition, my research is innovative in that to simulate a daily unemployment rate, job postings from Indeed for various sectors were utilized to model the shocks to the labor market from shutdown and fiscal support. The results show that job postings overall fell due to increases in shutdown stringency, with tourism and service job postings declining the most, followed by construction job postings. Driving jobs, on the other hand, showed minimal response to shutdown stringency. Financial markets demonstrated stability, while the 30-year mortgage rate declined, signaling improved investor sentiment and incentive to invest in real estate. Oil price increased in the short-run due to shutdown, while crude oil price experienced declines, which is significant as domestic oil shocks typically readjust through pricing pressure. Gold prices experienced positive increases when positive shocks to the shutdown stringency index are applied, demonstrating investor flight to safe-haven assets in times of economic uncertainty, a result that is consistent with previous research. Overall, this thesis contributes to the existing literature on pandemics by illustrating that U.S pandemic shutdowns and financial support only mildly affect financial markets but had pronounced negative effects on labor demand in specific sectors.