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Working Paper

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

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 implications of policies implemented during the Covid-19 pandemic. To construct the index, we extract the co-movement in several weekly data series capturing credit & debit card transactions and revenues, mobility, and consumer sentiment as well as monthly retail and food services sales excluding automotive spending (ex. autos) from the MRTS. To ensure that the index remains representative of the sample of firms covered in the MRTS, we use a mixed-frequency dynamic factor model constrained to match the MRTS growth rate at a monthly frequency. We document several interesting features of U.S. retail sales during the 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 the consensus forecasts available at the time or monthly autoregressive models. The gains are substantial, with approximately 70 to 85 percent reductions in mean absolute forecast errors.

Keywords: mixed-frequency dynamic factor model; retail sales; consumer spending;

JEL Classification: C32; C38; C43; C53; D12;


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Bibliographic Information

Provider: Federal Reserve Bank of Chicago

Part of Series: Working Paper Series

Publication Date: 2021-03-04

Number: WP-2021-05

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