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

Daniel Aaronson, Scott Brave, R. Andrew Butters, Daniel Sacks and Boyoung Seo ()

No WP 2020-10, Working Paper Series from Federal Reserve Bank of Chicago

Abstract: 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 surrounding these estimates and demonstrate their use within a broader forecasting framework for US economic activity.

Keywords: Covid-19; Google trends; hurricanes; unemployment; unemployment insurance (search for similar items in EconPapers)
JEL-codes: C53 H12 J65 (search for similar items in EconPapers)
Pages: 19
Date: 2020-04-07
New Economics Papers: this item is included in nep-big, nep-for and nep-ias
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Working Paper: Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedhwp:87770

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DOI: 10.21033/wp-2020-10

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