Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade

Daniel Aaronson, Scott Brave, Michael Fogarty, Ezra Karger () and Spencer Krane

No WP-2021-05, Working Paper Series from Federal Reserve Bank of Chicago

Abstract: We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, foot traffic, gasoline sales, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implement a novel benchmarking method that uses a mixed-frequency dynamic factor model to constrain the weekly index to match the monthly MRTS. We use the index to document several interesting features of U.S. retail sales during the Covid-19 pandemic, many of which are not visible in the MRTS. In addition, we show that our index would have more accurately predicted the MRTS in real time during the pandemic when compared to either consensus forecasts available at the time, monthly autoregressive models, or other commonly-cited high-frequency data that aims to track retail spending. The gains are substantial, with roughly 50 to 75 percent reductions in mean absolute forecast errors.

Keywords: mixed-frequency dynamic factor model; retail sales; consumer spending (search for similar items in EconPapers)
JEL-codes: C32 C38 C43 C53 D12 (search for similar items in EconPapers)
Pages: 44
Date: 2021-03-04, Revised 2021-06-18
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.21033/wp-2021-05

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