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Jel Classification:E17 

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Vulnerable growth

We study the conditional distribution of GDP growth as a function of economic and financial conditions. Deteriorating financial conditions are associated with an increase in the conditional volatility and a decline in the conditional mean of GDP growth, leading the lower quantiles of GDP growth to vary with financial conditions and the upper quantiles to be stable over time: Upside risks to GDP growth are low in most periods while downside risks increase as financial conditions become tighter. We argue that amplification mechanisms in the financial sector generate the observed growth ...
Staff Reports , Paper 794

Working Paper
The Accuracy of Forecasts Prepared for the Federal Open Market Committee

We analyze forecasts of consumption, nonresidential investment, residential investment, government spending, exports, imports, inventories, gross domestic product, inflation, and unemployment prepared by the staff of the Board of Governors of the Federal Reserve System for meetings of the Federal Open Market Committee from 1997 to 2008, called the Greenbooks. We compare the root mean squared error, mean absolute error, and the proportion of directional errors of Greenbook forecasts of these macroeconomic indicators to the errors from three forecasting benchmarks: a random walk, a first-order ...
Finance and Economics Discussion Series , Paper 2015-62

Working Paper
Significance Bands for Local Projections

An impulse response function describes the dynamic evolution of an outcome variable following a stimulus or treatment. A common hypothesis of interest is whether the treatment affects the outcome. We show that this hypothesis is best assessed using significance bands rather than relying on commonly displayed confidence bands. Under the null hypothesis, we show that significance bands are trivial to construct with standard statistical software using the LM principle, and should be reported as a matter of routine when displaying impulse responses graphically.
Working Paper Series , Paper 2023-15

Working Paper
Information and Inequality in the Time of a Pandemic

We introduce two types of agent heterogeneity in a calibrated epidemiological search model. First, some agents cannot afford to stay home to minimize virus exposure. Our results show that poor agents bear most of the epidemic’s health costs. Furthermore, we show that when a larger share of agents fail to change their behavior during the epidemic, a deeper recession is possible. Second, agents develop symptoms heterogeneously. We show that for diseases with a higher share of asymptomatic cases, even when less lethal, health and economic outcomes are worse. Public policies such as testing, ...
Working Papers , Paper 20-25

Report
Behavior and the Transmission of COVID-19

We show that a simple model of COVID-19 that incorporates feedback from disease prevalence to disease transmission through an endogenous response of human behavior does a remarkable job fitting the main features of the data on the growth rates of daily deaths observed across a large number countries and states of the United States from March to November of 2020. This finding, however, suggests a new empirical puzzle. Using an accounting procedure akin to that used for Business Cycle Accounting as in Chari et al. (2007), we show that when the parameters of the behavioral response of ...
Staff Report , Paper 618

Working Paper
Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility

We develop a sequential Monte Carlo (SMC) algorithm for Bayesian inference in vector autoregressions with stochastic volatility (VAR-SV). The algorithm builds particle approximations to the sequence of the model’s posteriors, adapting the particles from one approximation to the next as the window of available data expands. The parallelizability of the algorithm’s computations allows the adaptations to occur rapidly. Our particular algorithm exploits the ability to marginalize many parameters from the posterior analytically and embeds a known Markov chain Monte Carlo (MCMC) algorithm for ...
Working Papers , Paper 19-29

Working Paper
What's the Story? A New Perspective on the Value of Economic Forecasts

We apply textual analysis tools to measure the degree of optimism versus pessimism of the text that describes Federal Reserve Board forecasts published in the Greenbook. The resulting measure of Greenbook text sentiment, ?Tonality,? is found to be strongly correlated, in the intuitive direction, with the Greenbook point forecast for key economic variables such as unemployment and inflation. We then examine whether Tonality has incremental power for predicting unemployment, GDP growth, and inflation up to four quarters ahead. We find it to have significant and substantive predictive power for ...
Finance and Economics Discussion Series , Paper 2017-107

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 Papers , Paper 20-13

Working Paper
Evaluating the Conditionality of Judgmental Forecasts

We propose a framework to evaluate the conditionality of forecasts. The crux of our framework is the observation that a forecast is conditional if revisions to the conditioning factor are faithfully incorporated into the remainder of the forecast. We consider whether the Greenbook, Blue Chip, and the Survey of Professional Forecasters exhibit systematic biases in the manner in which they incorporate interest rate projections into the forecasts of other macroeconomic variables. We do not find strong evidence of systematic biases in the three economic forecasts that we consider, as the interest ...
Finance and Economics Discussion Series , Paper 2019-002

Report
Forecasting CPI Shelter under Falling Market-Rent Growth

Shelter (housing) costs constitute a large component of price indexes, including 42 percent of the widely followed core Consumer Price Index (CPI). The shelter prices measured in the CPI capture new and existing renters and tend to lag market rents. This lag explains how in recent months the shelter-price index (CPI shelter) has accelerated while market rents have pulled back. We construct an error correction model using data at the metropolitan statistical area level to forecast how CPI shelter will evolve. We forecast that CPI shelter will grow 5.88 percent from September 2022 to September ...
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Clark, Todd E. 12 items

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Boyarchenko, Nina 3 items

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