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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
Social Distancing, Vaccination and Evolution of COVID-19 Transmission Rates in Europe
This paper provides estimates of COVID-19 transmission rates and explains their evolution for selected European countries since the start of the pandemic taking account of changes in voluntary and government-mandated social distancing, incentives to comply, vaccination and the emergence of new variants. Evidence based on panel data modeling indicates that the diversity of outcomes that we document may have resulted from the non-linear interaction of mandated and voluntary social distancing and the economic incentives that governments provided to support isolation. The importance of these ...
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
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 ...
Working Paper
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility
The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that ...
Working Paper
Social Distancing, Vaccination and Evolution of COVID-19 Transmission Rates in Europe
This paper provides estimates of COVID-19 effective reproduction numbers worldwide and explains their evolution for selected European countries since the start of the pandemic, taking account of changes in voluntary and government-mandated social distancing, incentives to comply, vaccination and the emergence of mutations. Evidence based on panel data modeling indicates that the diversity of outcomes that we document resulted from the non-linear interaction of mandated and voluntary social distancing and the economic incentives that governments provided to support isolation, with no one ...
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
Measuring Uncertainty and Its Effects in the COVID-19 Era
We measure the effects of the COVID-19 outbreak on macroeconomic and financial uncertainty, and we assess the consequences of the latter for key economic variables. We use a large, heteroskedastic vector autoregression (VAR) in which the error volatilities share two common factors, interpreted as macro and financial uncertainty, in addition to idiosyncratic components. Macro and financial uncertainty are allowed to contemporaneously affect the macroeconomy and financial conditions, with changes in the common component of the volatilities providing contemporaneous identifying information on ...
Working Paper
Average Inflation Targeting: Time Inconsistency And Intentional Ambiguity
We study the implications of the Fed's new policy framework of average inflation targeting (AIT) and its ambiguous communication. The central bank has the incentive to deviate from its announced AIT and implement inflation targeting ex post to maximize social welfare. We show two motives for ambiguous communication about the horizon over which the central bank averages inflation as a result of time inconsistency. First, it is optimal for the central bank to announce different horizons depending on the state of the economy. Second, ambiguous communication helps the central bank gain ...
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, as well as classical and Bayesian quantile 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 improves as time moves ...