Forecasting Aggregate Retail Sales with Google Trends
Elizaveta Golovanova () and
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Elizaveta Golovanova: RANEPA
Russian Journal of Money and Finance, 2021, vol. 80, issue 4, 50–73
As the internet grows in popularity, many purchases are being made in online stores. Google Trends is an online tool that collects data on user queries and forms categories from them. We forecast the dynamics of both aggregate retail sales and individual categories of food and non-food products using macroeconomic variables and Google Trends categories that correspond to various product groups. For each type of retail, we consider the best forecasting models from macroeconomic variables and try to improve them by adding trends. For these purposes, we use pseudo-out-of-sample nowcasting as well as recursive forecasting several months ahead. We conclude that forecasts for food and non-food products can improve significantly once trends are added to the models.
Keywords: aggregated retail sales; food products; non-food products; Google Trends; big data; forecasting; linear regressions (search for similar items in EconPapers)
JEL-codes: C22 C53 E27 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:80:y:2021:i:4:p:50-73
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