Due to the growing popularity of the Internet, many purchases are made in online stores. The Google Trends service collects data based on user requests and breaks them down into categories. In this paper, we review the existing forecasting methods using this service, and make an attempt to predict the dynamics of retail sales using macroeconomic variables and categories in Google Trends corresponding to various commodity groups of food and non-food products. For each type of retail, we build the best predictive models from macroeconomic variables and try to improve them by adding trends. А.В. Зубарев к.э.н., старший научный сотрудник Лаборатории математического моделирования экономических процессов ИПЭИ РАНХиГС Е.А. Голованова младший научный сотрудник Студенческого центра экономических исследований ИПЭИ РАНХиГС Данная работа подготовлена на основе материалов научно-исследовательской работы, выполненной в соответствии с Государственным заданием РАНХиГС при Президенте Российской Федерации на 2021 год 3

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Published on 06/01/23
Submitted on 29/12/22

Licence: CC BY-NC-SA license

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