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Jel Classification:C55 

Working Paper
Business Exit During the COVID-19 Pandemic: Non-Traditional Measures in Historical Context

Given lags in official data releases, economists have studied "alternative data" measures of business exit resulting from the COVID-19 pandemic. Such measures are difficult to understand without historical context, so we review official data on business exit in recent decades. Business exit is common in the U.S., with about 7.5 percent of firms exiting annually in recent years, and is countercyclical (particularly recently). Both the high level and the cyclicality of exit are driven by very small firms. We explore a range of alternative measures and indicators of business exit, including ...
Finance and Economics Discussion Series , Paper 2020-089

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 Papers , Paper 202032

Report
Economic predictions with big data: the illusion of sparsity

We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse or dense model, but on a wide set of models. A clearer pattern of sparsity can only emerge when models of very low dimension are strongly favored a priori.
Staff Reports , Paper 847

Report
Dynamic effects of credit shocks in a data-rich environment

We examine the dynamic effects of credit shocks using a large data set of U.S. economic and financial indicators in a structural factor model. An identified credit shock resulting in an unanticipated increase in credit spreads causes a large and persistent downturn in indicators of real economic activity, labor market conditions, expectations of future economic conditions, a gradual decline in aggregate price indices, and a decrease in short- and longer-term riskless interest rates. Our identification procedure, which imposes restrictions on the response of a small number of economic ...
Staff Reports , Paper 615

Report
Macroeconomic nowcasting and forecasting with big data

Data, data, data . . . Economists know it well, especially when it comes to monitoring macroeconomic conditions?the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before ?big data? became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate the best practices of forecasters on trading desks, at central banks, and in other ...
Staff Reports , Paper 830

Working Paper
The U.S. Syndicated Loan Market: Matching Data

We introduce a new software package for determining linkages between datasets without common identifiers. We apply these methods to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, the Shared National Credit Database, and S&P Global Market Intelligence Compustat. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that the company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. For loan level matching, a ...
Research Working Paper , Paper RWP 18-9

Working Paper
Assessing Macroeconomic Tail Risks in a Data-Rich Environment

We use a large set of economic and financial indicators to assess tail risks of the three macroeconomic variables: real GDP, unemployment, and inflation. When applied to U.S. data, we find evidence that a dense model using principal components (PC) as predictors might be misspecified by imposing the “common slope” assumption on the set of predictors across multiple quantiles. The common slope assumption ignores the heterogeneous informativeness of individual predictors on different quantiles. However, the parsimony of the PC-based approach improves the accuracy of out-of-sample forecasts ...
Research Working Paper , Paper RWP 19-12

Working Paper
Term Structure Analysis with Big Data

Analysis of the term structure of interest rates almost always takes a two-step approach. First, actual bond prices are summarized by interpolated synthetic zero-coupon yields, and second, a small set of these yields are used as the source data for further empirical examination. In contrast, we consider the advantages of a one-step approach that directly analyzes the universe of bond prices. To illustrate the feasibility and desirability of the onestep approach, we compare arbitrage-free dynamic term structure models estimated using both approaches. We also provide a simulation study showing ...
Working Paper Series , Paper 2017-21

Working Paper
Common and Idiosyncratic Inflation

We use a dynamic factor model to disentangle changes in prices due to economy-wide (common) shocks, from changes in prices due to idiosyncratic shocks. Using 146 disaggregated individual price series from the U.S. PCE price index, we find that most of the fluctuations in core PCE prices observed since 2010 have been idiosyncratic in nature. Moreover, we find that common core inflation responds to economic slack, while the idiosyncratic component does not. That said, even after filtering out idiosyncratic factors, the estimated Phillips curve is extremely flat post-1995. Therefore, our ...
Finance and Economics Discussion Series , Paper 2020-024

Working Paper
Tracking Labor Market Developments during the COVID-19 Pandemic: A Preliminary Assessment

Many traditional official statistics are not suitable for measuring high-frequency developments that evolve over the course of weeks, not months. In this paper, we track the labor market effects of the COVID-19 pandemic with weekly payroll employment series based on microdata from ADP. These data are available essentially in real-time, and allow us to track both aggregate and industry effects. Cumulative losses in paid employment through April 4 are currently estimated at 18 million; just during the two weeks between March 14 and March 28 the U.S. economy lost about 13 million paid jobs. ...
Finance and Economics Discussion Series , Paper 2020-030

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