Talk 4

One Day Meeting on Advanced Statistics Topics

CIDMA- University of Aveiro

Speaker: Maria Eduarda Silva - CIDMA & FEP, Universidade do Porto

Title of the talk: Nowcasting the Monthly Portuguese Unemployment Rates with Daily Google Trends Data

Abstract: Policymakers usually evaluate the current or recent-past states of economies by relying on incomplete statistical information since official statistics are published with delays. Accordingly, data from Google Trends (GT) have been gaining importance as predictors for economic indicators to overcome delays allowing timely forecasting. Such data have emerged in the literature as alternative predictors of macroeconomic outcomes, such as the unemployment rate, featuring readiness, public availability and no costs. This talk introduces extensive daily GT data to develop a framework to nowcast monthly unemployment rates in a real-time data availability environment, resorting to Mixed Data Sampling (MIDAS) regressions. Portugal is chosen as a use case for the methodology since the extraction of GT data requires the selection of keywords, which is culture-dependent. The nowcasting period comprises from 2019 to 2021, which includes the coronavirus outbreak. The results show that daily GT predictors via MIDAS lead to accurate and timely information on the unemployment rate and are particularly effective in dealing with the external shock from COVID-19, showing accuracy gains even when compared to nowcasts obtained from typical monthly GT data via traditional ARIMAX models.

Maria Eduarda Silva obtained her Ph.D. degree in Statistics from The University of Manchester, in 1994. In 2015, she received the Habilitation (“Agregação”) Degree from University of Porto. She is Associate Professor at Faculty of Economics of University of Porto since 2008 and is member CIDMA Group. Her current research interests include several topics in the areas of time series, nonlinear time series, time series of counts, spatio-temporal processes, forecasting, applications to economics and finance, environment, biomedicine, finance and geophysical processes.

ORCID: 0000-0003-2972-2050

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