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Author:Brave, Scott A. 

Journal Article
A New “Big Data” Index of U.S. Economic Activity

The authors present a new ?big data? index of U.S. economic activity that can be used to track business and inflation cycles in real time and estimate monthly real gross domestic product growth.
Economic Perspectives , Issue 1 , Pages 1-30

Working Paper
Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims

We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic “unemployment,” we show that out-of-sample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty ...
Working Paper Series , Paper WP-2020-10

What Can Revisions to the NFCI Tell Us About Stock Market Volatility?

In this blog post, we document that recent revisions to the Chicago Fed’s National Financial Conditions Index (NFCI) have been large and clustered in time—a pattern not seen since the 2007–09 global financial crisis. As financial conditions tightened early on during the Covid-19 outbreak here in the U.S., there were large positive revisions to the NFCI through much of March. We show that revisions of this magnitude and in this direction have often preceded substantial increases in stock market volatility. More recently, in late March and April, the large negative revisions to the NFCI ...
Chicago Fed Insights

Working Paper
Predicting Benchmarked US State Employment Data in Real Time

US payroll employment data come from a survey and are subject to revisions. While revisions are generally small at the national level, they can be large enough at the state level to alter assessments of current economic conditions. Users must therefore exercise caution in interpreting state employment data until they are “benchmarked” against administrative data 5–16 months after the reference period. This paper develops a state-space model that predicts benchmarked state employment data in real time. The model has two distinct features: 1) an explicit model of the data revision process ...
Working Papers , Paper 2019-037

Working Paper
Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims

We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic “unemployment,” we show that out-of-sample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty ...
Working Paper Series , Paper WP-2020-10

Newsletter
Measuring Detroit’s Economic Progress with the DEAI

This article explains what the Detroit Economic Activity Index (DEAI) tells us about Detroit’s economic progress as of late 2019. Although the rate of progress had slowed some since 2016, the city continued to make headway last year in its recovery from bankruptcy.In a previous Chicago Fed Letter,1 we introduced the DEAI to show that Detroit was doing better in late 2016 than in late 2014, when it exited bankruptcy. According to the DEAI, there were signs of increasing private investment, higher employment, lower unemployment, rising incomes, and improving real estate values in December ...
Chicago Fed Letter , Issue 434

Newsletter
The Stay-at-Home Labor Market: Google Searches, Unemployment Insurance, and Public Health Orders

This article looks at the relationships between internet searches for unemployment-related terms, unemployment insurance (UI), and the public health orders issued in the U.S. during the Covid-19 pandemic. We find that Google searches for unemployment-related subjects surged before the record increase in initial UI claims, which in turn peaked before the public health orders were implemented. As of mid-April 2020, these orders covered the vast majority of the U.S. population. Since then, the rates of increase in both search activity and initial UI claims have slowed.
Chicago Fed Letter , Issue 436

Newsletter
Measuring the Effects of the Covid-19 Delta Wave on the U.S. Hourly Labor Market

In this article, we take a closer look at the implications of rising Covid-19 cases and vaccination rates for the U.S. hourly labor market. To do so, we rely on geographic variation in the high-frequency data collected by the firm Homebase with its timekeeping software. This data source allows us to make use of U.S. state-level variation on a daily basis in order to decompose the effects on hourly employees and hours worked from both rising cases and vaccinations.
Chicago Fed Letter , Issue 461 , Pages 6

A Closer Look at the Correlation Between Google Trends and Initial Unemployment Insurance Claims

Since the onset of the pandemic, there has been growing interest in tracking labor market activity with “big data” sources like Google Trends.1 Just as an example, one can track how the number of Google searches with the term unemployment office has changed over the past week for the Chicago metro area or explore how unemployment became one of the top searched issues across the U.S. during the early months of the pandemic here.
Chicago Fed Insights

Working Paper
The perils of working with Big Data and a SMALL framework you can use to avoid them

The use of “Big Data” to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages when compared to these more traditional data sources, but with these advantages also comes the potential for pitfalls. We lay out a framework called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL ...
Working Paper Series , Paper WP-2020-35

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