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Report
The Effect of the Central Bank Liquidity Support during Pandemics: Evidence from the 1918 Influenza Pandemic
The coronavirus outbreak raises the question of how central bank liquidity support affects financial stability and promotes economic recovery. Using newly assembled data on cross-county flu mortality rates and state-charter bank balance sheets in New York State, we investigate the effects of the 1918 influenza pandemic on the banking system and the role of the Federal Reserve during the pandemic. We find that banks located in more severely affected areas experienced deposit withdrawals. Banks that were members of the Federal Reserve System were able to access central bank liquidity, enabling ...
Working Paper
Longer-Run Economic Consequences of Pandemics
How do major pandemics affect economic activity in the medium to longer term? Is it consistent with what economic theory prescribes? Since these are rare events, historical evidence over many centuries is required. We study rates of return on assets using a dataset stretching back to the 14th century, focusing on 12 major pandemics where more than 100,000 people died. In addition, we include major armed conflicts resulting in a similarly large death toll. Significant macroeconomic after-effects of the pandemics persist for about 40 years, with real rates of return substantially depressed. In ...
Journal Article
The Uncertainty Channel of the Coronavirus
The outbreak of the novel coronavirus, or COVID-19, has severely disrupted economic activity through various supply and demand channels. The pandemic can also have pervasive economic impact by raising uncertainty. In the past, sudden and outsized spikes in uncertainty have led to large and protracted increases in unemployment and declines in inflation. These effects are similar to those resulting from declines in aggregate demand. Monetary policy accommodation, such as interest rate cuts, can help cushion the economy from such uncertainty shocks.
Working Paper
Nowcasting Tail Risks to Economic Activity with Many Indicators
This paper focuses on tail risk nowcasts of economic activity, measured by GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, classical and Bayesian quantile regressions, quantile MIDAS regressions) and also different methods for data reduction (either the combination of forecasts from smaller models or forecasts from models that incorporate data reduction). The results show that classical and MIDAS quantile regressions perform very well in-sample but not out-of-sample, ...
Working Paper
Nowcasting Tail Risks to Economic Activity with Many Indicators
This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, classical and Bayesian quantile regressions, quantile MIDAS regressions) and also different methods for data reduction (either forecasts from models that incorporate data reduction or the combination of forecasts from smaller models). Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically ...