Predicting macroeconomic indicators from online activity data: A review

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Costa, EA (Author) Other; Maria Eduarda Silva (Author) FEP

Journal

Title: Statistical Journal of the IAOS

Vol. 40

Pages: 403-419

ISSN: 1874-7655

Publisher: IOS PRESS

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-010-DZ3

DOI: 10.3233/sji-230102

Abstract (EN): Predictors of macroeconomic indicators rely primarily on traditional data sourced from National Statistical Offices. However, new data sources made available from recent technological advancements, namely data from online activities, have the potential to bring about fresh perspectives on monitoring economic activities and enhance the accuracy of forecasting. This paper reviews the literature on predicting macroeconomic indicators, such as the gross domestic product, unemployment rate, consumer price index or private consumption, based on online activity data sourced from Google Trends, Twitter (rebranded to X) and mobile devices. Based on a systematic search of publications indexed on the Web of Science and Scopus databases, the analysis of a final set of 56 publications covers the publication history of the data sources, the methods used to model the data and the predictive accuracy of information from such data sources. The paper also discusses the limitations and challenges of using online activity data for macroeconomic predictions. The review concludes that online activity data can be a valuable source of information for predicting macroeconomic indicators. However, one must consider certain limitations and challenges to improve the models' accuracy and reliability. © 2024 - IOS Press. All rights reserved.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16