In this Chicago Fed Insights article, we provide an update on the Chicago Fed Advance Retail Trade Summary (CARTS). CARTS is a summary measure of multiple high-frequency consumer spending indicators that aims to improve on the timeliness and reliability of traditional measures of U.S. retail spending.

In this Chicago Fed Insights article, we provide an update on the Chicago Fed Advance Retail Trade Summary (CARTS). As readers may remember, CARTS debuted in mid-2021. A summary measure of multiple high-frequency indicators of retail sales (including payment card transactions, foot traffic, gas sales, and consumer sentiment), CARTS grew out of pandemic-era research that aimed to improve the timeliness and reliability of traditional measures of U.S. retail spending.

In this Chicago Fed Insights article, we unveil version 2.0 of our Chicago Fed Advance Retail Trade Summary (CARTS) and provide a preview of what it signals for retail spending during the 2023 holiday season in the United States.

During the early days of the Covid-19 pandemic in the U.S., the speed with which economic activity responded to the spread of the virus and the public policy responses that followed laid bare the need for both accurate and timely measures of consumer spending. In this Chicago Fed Insights blog post, we discuss a new Chicago Fed index called CARTS and its implications for the high-frequency measurement of consumer spending.

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 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, monthly autoregressive models, or other commonly-cited high-frequency data that aims to track retail spending. The gains are substantial, with 50 percent or more reductions in mean absolute forecast errors.