Real-time nowcasting the monthly unemployment rates with daily Google Trends data

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

Authors

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

Journal

Title: Socio-Economic Planning Sciences

Vol. 95

ISSN: 0038-0121

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-010-G6D

DOI: 10.1016/j.seps.2024.101963

Abstract (EN): Policymakers often have to make decisions based on incomplete economic data because of the usual delay in publishing official statistics. To circumvent this issue, researchers use data from Google Trends (GT) as an early indicator of economic performance. Such data have emerged in the literature as alternative and complementary predictors of macroeconomic outcomes, such as the unemployment rate, featuring readiness, public availability and no costs. This study deals with extensive daily GT data to develop a framework to nowcast monthly unemployment rates tailored to work with real-time data availability, resorting to Mixed Data Sampling (MIDAS) regressions. Portugal is chosen as a use case for the methodology since extracting GT data requires the selection of culturally dependent keywords. The nowcasting period spans 2019 to 2021, encompassing the time frame in which the coronavirus pandemic initiated. The findings indicate that using daily GT data with MIDAS provides timely and accurate insights into the unemployment rate, especially during the COVID-19 pandemic, showing accuracy gains even when compared to nowcasts obtained from typical monthly GT data via traditional ARMAX models.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13